Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.
A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHMIAEME Publication
Particle swarm optimization (PSO) is a population-based stochastic optimization technique that is inspired by the intelligent collective behaviour of certain animals, such as flocks of birds or schools of fish. It has undergone numerous improvements since its debut in 1995. As academics became more familiar with the technique, they produced additional versions aimed at different demands, created new applications in a variety of fields, published theoretical analyses of the impacts of various factors, and offered other variants of the algorithm. This paper discusses the PSO's origins and background, as well as its theory analysis. Then, we examine the current state of research and application in algorithm structure, parameter selection, topological structure, discrete and parallel PSO algorithms, multi-objective optimization PSO, and engineering applications. Finally, existing difficulties are discussed, and new study directions are proposed.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial...IJEECSIAES
The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial...nooriasukmaningtyas
The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
IRJET- Path Finder with Obstacle Avoidance RobotIRJET Journal
This document presents a robot that can find a safe path and avoid obstacles. It uses an infrared sensor to detect obstacles in its path. When an obstacle is detected, the robot changes direction to avoid the obstacle and moves towards its destination. The system architecture includes infrared sensors, a microcontroller, and motors. When an obstacle is detected by the infrared sensor, the microcontroller processes the input and redirects the robot using motors controlled by motor drivers, allowing the robot to avoid collisions and safely reach its target location.
This document discusses using particle swarm optimization (PSO) to design optimal close-range photogrammetry networks. PSO is introduced as a heuristic optimization algorithm inspired by bird flocking behavior that can be used to solve complex optimization problems. The document then provides an overview of close-range photogrammetry network design and the four design stages. It explains that PSO will be used to optimize the first stage of determining optimal camera station positions. Mathematical models of PSO for close-range photogrammetry network design are developed. Experimental tests are carried out to develop a PSO algorithm that can determine optimum camera positions and evaluate the accuracy of the developed network.
Research on the mobile robots intelligent path planning based on ant colony a...csandit
The document discusses research on path planning for mobile robots using ant colony algorithms. It begins with an abstract and keywords on manufacturing logistics, mobile robots, path planning, and ant colony algorithms. It then provides background on mobile robot research and development. The main challenges of path planning are discussed, including finding optimal collision-free paths. Traditional path planning methods like grid, topology and artificial potential field methods are reviewed. The ant colony algorithm is introduced as a promising new approach for complex path planning problems as it simulates how ants find optimal paths through pheromone signaling.
RESEARCH ON THE MOBILE ROBOTS INTELLIGENT PATH PLANNING BASED ON ANT COLONY A...cscpconf
With the development of robotics and artificial intelligence field unceasingly thorough, path
planning as an important field of robot calculation has been widespread concern. This paper
analyzes the current development of robot and path planning algorithm and focuses on the
advantages and disadvantages of the traditional intelligent path planning as well as the path
planning. The problem of mobile robot path planning is studied by using ant colony algorithm, and
it also provides some solving methods.
IRJET- Swarm Robotics and their Potential to be Applied in Real Life ProblemsIRJET Journal
This document discusses swarm robotics and its potential applications to real-life problems. It provides an overview of existing research on swarm robotics, which has successfully demonstrated complex collective behaviors like aggregation, pattern formation, and transportation in controlled laboratory environments. However, the document notes that more research is still needed to apply swarm robotics to solve real-world problems. It analyzes the tasks that have been studied in the context of swarm robotics, like aggregation, mapping and localization, and discusses how combining these tasks could help achieve practical applications of swarm robotics.
A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHMIAEME Publication
Particle swarm optimization (PSO) is a population-based stochastic optimization technique that is inspired by the intelligent collective behaviour of certain animals, such as flocks of birds or schools of fish. It has undergone numerous improvements since its debut in 1995. As academics became more familiar with the technique, they produced additional versions aimed at different demands, created new applications in a variety of fields, published theoretical analyses of the impacts of various factors, and offered other variants of the algorithm. This paper discusses the PSO's origins and background, as well as its theory analysis. Then, we examine the current state of research and application in algorithm structure, parameter selection, topological structure, discrete and parallel PSO algorithms, multi-objective optimization PSO, and engineering applications. Finally, existing difficulties are discussed, and new study directions are proposed.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial...IJEECSIAES
The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial...nooriasukmaningtyas
The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
IRJET- Path Finder with Obstacle Avoidance RobotIRJET Journal
This document presents a robot that can find a safe path and avoid obstacles. It uses an infrared sensor to detect obstacles in its path. When an obstacle is detected, the robot changes direction to avoid the obstacle and moves towards its destination. The system architecture includes infrared sensors, a microcontroller, and motors. When an obstacle is detected by the infrared sensor, the microcontroller processes the input and redirects the robot using motors controlled by motor drivers, allowing the robot to avoid collisions and safely reach its target location.
This document discusses using particle swarm optimization (PSO) to design optimal close-range photogrammetry networks. PSO is introduced as a heuristic optimization algorithm inspired by bird flocking behavior that can be used to solve complex optimization problems. The document then provides an overview of close-range photogrammetry network design and the four design stages. It explains that PSO will be used to optimize the first stage of determining optimal camera station positions. Mathematical models of PSO for close-range photogrammetry network design are developed. Experimental tests are carried out to develop a PSO algorithm that can determine optimum camera positions and evaluate the accuracy of the developed network.
Research on the mobile robots intelligent path planning based on ant colony a...csandit
The document discusses research on path planning for mobile robots using ant colony algorithms. It begins with an abstract and keywords on manufacturing logistics, mobile robots, path planning, and ant colony algorithms. It then provides background on mobile robot research and development. The main challenges of path planning are discussed, including finding optimal collision-free paths. Traditional path planning methods like grid, topology and artificial potential field methods are reviewed. The ant colony algorithm is introduced as a promising new approach for complex path planning problems as it simulates how ants find optimal paths through pheromone signaling.
RESEARCH ON THE MOBILE ROBOTS INTELLIGENT PATH PLANNING BASED ON ANT COLONY A...cscpconf
With the development of robotics and artificial intelligence field unceasingly thorough, path
planning as an important field of robot calculation has been widespread concern. This paper
analyzes the current development of robot and path planning algorithm and focuses on the
advantages and disadvantages of the traditional intelligent path planning as well as the path
planning. The problem of mobile robot path planning is studied by using ant colony algorithm, and
it also provides some solving methods.
IRJET- Swarm Robotics and their Potential to be Applied in Real Life ProblemsIRJET Journal
This document discusses swarm robotics and its potential applications to real-life problems. It provides an overview of existing research on swarm robotics, which has successfully demonstrated complex collective behaviors like aggregation, pattern formation, and transportation in controlled laboratory environments. However, the document notes that more research is still needed to apply swarm robotics to solve real-world problems. It analyzes the tasks that have been studied in the context of swarm robotics, like aggregation, mapping and localization, and discusses how combining these tasks could help achieve practical applications of swarm robotics.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcscpconf
This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.
This document summarizes an article that proposes improvements to an existing algorithm for resource scheduling in cloud computing environments. The existing algorithm uses a hybrid of ant colony optimization and particle swarm optimization. The proposed improvements add an initial phase that uses an enhanced fish swarm search algorithm to help find more global optimal solutions. This global optimal solution found by fish swarm search is then used to guide the existing ant colony optimization and particle swarm optimization hybrid to find more locally optimal solutions. The document provides background on resource scheduling, metaheuristic algorithms, and describes the specific implementations of the improved fish swarm search algorithm and the overall proposed methodology.
The document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO is inspired by how bacteria like E. coli search for food by swimming and tumbling. ACO is based on how ants deposit and follow pheromone trails to find food sources. PSO mimics the movement of bird flocking and fish schooling. The document provides details on the mechanisms and equations used in each algorithm's approach to finding optimal paths for multiple robots.
Path Optimization for Mobile Robot Using Genetic AlgorithmIRJET Journal
This document summarizes research on using a genetic algorithm to optimize the path planning of a mobile robot in a static environment with predictable terrain and obstacles. A genetic algorithm was used to help the robot find the shortest and collision-free path between a starting and ending point in a grid environment. The genetic algorithm represents possible paths as chromosomes and evaluates their fitness based on path length. Testing showed the genetic algorithm was effective at finding optimal paths for robots as the number of iterations increased, even when obstacle positions changed. The approach ensured the goal position was the global minimum path while maintaining collision-free movement.
With the development of robotics and artificial intelligence field unceasingly thorough, path planning for avoid
obstacles as an important field of robot calculation has been widespread concern. This paper analyzes the
current development of robot and path planning algorithm for path planning to avoid obstacles in practice. We
tried to find a good way in mobile robot path planning by using ant colony algorithm, and it also provides some
solving methods.
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcsandit
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
This document summarizes a research paper about motion planning for multiple robots in a non-rectangular workspace. The paper aims to utilize advantages of both centralized and decentralized planning approaches to minimize limitations. Collision detection is performed by checking if new robot positions overlap with other robot areas. Path planning ensures robots avoid boundaries while reaching destinations. Simulation results show robots reaching targets over time. Adding robots or changing boundaries has minimal effect on planning time. The research is limited to geometric aspects rather than physical robot interaction dynamics.
Semi-Autonomous Control of a Multi-Agent Robotic System for Multi-Target Oper...Waqas Tariq
This document proposes a control method for a single-master multi-slave teleoperation system to control multiple cooperative mobile robots for multi-target missions. The control method includes a modified potential field-based leader-follower formation approach and a robot-target pairing method. The pairing method uses an auction algorithm to optimally pair robots to targets. The robots are split into subteams based on the pairings and each subteam autonomously approaches its paired target while avoiding obstacles. Simulation studies demonstrate the effectiveness of this control method for multi-target operations.
Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with...IJECEIAES
A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Neighborhood search methods with moth optimization algorithm as a wrapper met...IJECEIAES
Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flame Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm IJECEIAES
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments.
Text documents clustering using modified multi-verse optimizerIJECEIAES
In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
Best-worst northern goshawk optimizer: a new stochastic optimization methodIJECEIAES
This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.
MHead - Self-Organized Flocking in Mobile Robot SwarmsSamet Baykul
DATE: 2019.05
- Engineering design
- CAD by creating complex geometry via SolidWorks
- Arduino programming
- Control systems design
- Physics simulation in robotics by using Webots
- Prototyping by using a 3d printer
- Test setups
- Selection of mechatronics components
- Building an effective robot algorithms by using C++
- Literature survey for recent academic studies
PROJECT:
Goal: In order to have a more natural flocking behavior implementation, the data acquisition of each individual robot has to be kept as low as possible. On the other hand, in order to achieve a successful flocking behavior and to solve a more complex task, the number of individuals within a swarm robots must be increased. In other words, flocking size should be as much as possible. Consequently, there is need to develop a new swarm of robot platform that can demonstrate the solution of complex problems with large amounts of limited information. In order to achieve this goal, each individual robot should be designed in a minimalistic way and produced as cheaply as possible.
The document presents a lifelong federated reinforcement learning (LFRL) architecture for navigation in cloud robotic systems. LFRL allows robots to fuse their experience and transfer knowledge so they can effectively use prior knowledge and quickly adapt to new environments. It proposes a knowledge fusion algorithm to upgrade a shared model on the cloud by fusing private models from robots. It also introduces effective transfer learning methods to help robots rapidly adapt to new environments. Experiments show LFRL improves the efficiency of reinforcement learning for robot navigation. A cloud robotic navigation website is also presented to demonstrate LFRL.
Introduction to the Special issue on ‘‘Future trends in robotics and autonomo...Anand Bhojan
Robotics is an extremely dynamic field with thriving advancement in its technology. As research progresses in robotic systems, more and more aspects of vision based processing, GPS enabled services, Autonomous techniques, very far distance communication in robots, dynamic environment handling, mobility techniques, multi-agent control and coordination techniques, multi-robot communication and coordination are explored to make robotics intelligent and to do specific tasks. Vision has helped in many areas for better services and fastens the process for localized results. Advancements in communication, positioning and localization techniques brought the robotics beyond the controlled industrial environments to more dynamic outdoor environments. Research in autonomous and other intelligent techniques has made robots capable of taking decisions in complex environments. The book covers future trends in robotics research topics including motion path planning, routing in dynamic environments, multi-agent control techniques, nature inspired algorithms and synchronization techniques with interesting applications.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcscpconf
This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.
This document summarizes an article that proposes improvements to an existing algorithm for resource scheduling in cloud computing environments. The existing algorithm uses a hybrid of ant colony optimization and particle swarm optimization. The proposed improvements add an initial phase that uses an enhanced fish swarm search algorithm to help find more global optimal solutions. This global optimal solution found by fish swarm search is then used to guide the existing ant colony optimization and particle swarm optimization hybrid to find more locally optimal solutions. The document provides background on resource scheduling, metaheuristic algorithms, and describes the specific implementations of the improved fish swarm search algorithm and the overall proposed methodology.
The document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO is inspired by how bacteria like E. coli search for food by swimming and tumbling. ACO is based on how ants deposit and follow pheromone trails to find food sources. PSO mimics the movement of bird flocking and fish schooling. The document provides details on the mechanisms and equations used in each algorithm's approach to finding optimal paths for multiple robots.
Path Optimization for Mobile Robot Using Genetic AlgorithmIRJET Journal
This document summarizes research on using a genetic algorithm to optimize the path planning of a mobile robot in a static environment with predictable terrain and obstacles. A genetic algorithm was used to help the robot find the shortest and collision-free path between a starting and ending point in a grid environment. The genetic algorithm represents possible paths as chromosomes and evaluates their fitness based on path length. Testing showed the genetic algorithm was effective at finding optimal paths for robots as the number of iterations increased, even when obstacle positions changed. The approach ensured the goal position was the global minimum path while maintaining collision-free movement.
With the development of robotics and artificial intelligence field unceasingly thorough, path planning for avoid
obstacles as an important field of robot calculation has been widespread concern. This paper analyzes the
current development of robot and path planning algorithm for path planning to avoid obstacles in practice. We
tried to find a good way in mobile robot path planning by using ant colony algorithm, and it also provides some
solving methods.
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcsandit
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
This document summarizes a research paper about motion planning for multiple robots in a non-rectangular workspace. The paper aims to utilize advantages of both centralized and decentralized planning approaches to minimize limitations. Collision detection is performed by checking if new robot positions overlap with other robot areas. Path planning ensures robots avoid boundaries while reaching destinations. Simulation results show robots reaching targets over time. Adding robots or changing boundaries has minimal effect on planning time. The research is limited to geometric aspects rather than physical robot interaction dynamics.
Semi-Autonomous Control of a Multi-Agent Robotic System for Multi-Target Oper...Waqas Tariq
This document proposes a control method for a single-master multi-slave teleoperation system to control multiple cooperative mobile robots for multi-target missions. The control method includes a modified potential field-based leader-follower formation approach and a robot-target pairing method. The pairing method uses an auction algorithm to optimally pair robots to targets. The robots are split into subteams based on the pairings and each subteam autonomously approaches its paired target while avoiding obstacles. Simulation studies demonstrate the effectiveness of this control method for multi-target operations.
Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with...IJECEIAES
A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Neighborhood search methods with moth optimization algorithm as a wrapper met...IJECEIAES
Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flame Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm IJECEIAES
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments.
Text documents clustering using modified multi-verse optimizerIJECEIAES
In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
Best-worst northern goshawk optimizer: a new stochastic optimization methodIJECEIAES
This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.
MHead - Self-Organized Flocking in Mobile Robot SwarmsSamet Baykul
DATE: 2019.05
- Engineering design
- CAD by creating complex geometry via SolidWorks
- Arduino programming
- Control systems design
- Physics simulation in robotics by using Webots
- Prototyping by using a 3d printer
- Test setups
- Selection of mechatronics components
- Building an effective robot algorithms by using C++
- Literature survey for recent academic studies
PROJECT:
Goal: In order to have a more natural flocking behavior implementation, the data acquisition of each individual robot has to be kept as low as possible. On the other hand, in order to achieve a successful flocking behavior and to solve a more complex task, the number of individuals within a swarm robots must be increased. In other words, flocking size should be as much as possible. Consequently, there is need to develop a new swarm of robot platform that can demonstrate the solution of complex problems with large amounts of limited information. In order to achieve this goal, each individual robot should be designed in a minimalistic way and produced as cheaply as possible.
The document presents a lifelong federated reinforcement learning (LFRL) architecture for navigation in cloud robotic systems. LFRL allows robots to fuse their experience and transfer knowledge so they can effectively use prior knowledge and quickly adapt to new environments. It proposes a knowledge fusion algorithm to upgrade a shared model on the cloud by fusing private models from robots. It also introduces effective transfer learning methods to help robots rapidly adapt to new environments. Experiments show LFRL improves the efficiency of reinforcement learning for robot navigation. A cloud robotic navigation website is also presented to demonstrate LFRL.
Introduction to the Special issue on ‘‘Future trends in robotics and autonomo...Anand Bhojan
Robotics is an extremely dynamic field with thriving advancement in its technology. As research progresses in robotic systems, more and more aspects of vision based processing, GPS enabled services, Autonomous techniques, very far distance communication in robots, dynamic environment handling, mobility techniques, multi-agent control and coordination techniques, multi-robot communication and coordination are explored to make robotics intelligent and to do specific tasks. Vision has helped in many areas for better services and fastens the process for localized results. Advancements in communication, positioning and localization techniques brought the robotics beyond the controlled industrial environments to more dynamic outdoor environments. Research in autonomous and other intelligent techniques has made robots capable of taking decisions in complex environments. The book covers future trends in robotics research topics including motion path planning, routing in dynamic environments, multi-agent control techniques, nature inspired algorithms and synchronization techniques with interesting applications.
Similar to Impact of initialization of a modified particle swarm optimization on cooperative source searching (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
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Impact of initialization of a modified particle swarm optimization on cooperative source searching
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 218~229
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp218-229 218
Journal homepage: http://ijece.iaescore.com
Impact of initialization of a modified particle swarm
optimization on cooperative source searching
Mad Helmi Ab. Majid1
, Mohd Rizal Arshad2
, Mohd Faid Yahya3
, Abu Bakar Ibrahim1
1
Department of Software Engineering and Smart Technology, Faculty of Computer and Meta-Technology, Universiti Pendidikan
Sultan Idris, Perak, Malaysia
2
School of Electrical and Electronic Engineering, Universiti Sains Malaysia (Engineering Campus), Penang, Malaysia
3
Department of Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
Article Info ABSTRACT
Article history:
Received May 11, 2023
Revised Jul 29, 2023
Accepted Aug 1, 2023
Swarm robotic is well known for its flexibility, scalability and robustness
that make it suitable for solving many real-world problems. Source
searching which is characterized by complex operation due to the spatial
characteristic of the source intensity distribution, uncertain searching
environments and rigid searching constraints is an example of application
where swarm robotics can be applied. Particle swarm optimization (PSO) is
one of the famous algorithms have been used for source searching where its
effectiveness depends on several factors. Improper parameter selection may
lead to a premature convergence and thus robots will fail (i.e., low success
rate) to locate the source within the given searching constraints.
Additionally, target overshooting and improper initialization strategies may
lead to a nonoptimal (i.e., take longer time to converge) target searching. In
this study, a modified PSO and three different initializations strategies (i.e.,
random, equidistant and centralized) were proposed. The findings shown
that the proposed PSO model successfully reduce the target overshooting by
choosing optimal PSO parameters and has better convergence rate and
success rate compared to the benchmark algorithms. Additionally, the
findings also indicate that the random initialization give better searching
success compared to equidistant and centralize initialization.
Keywords:
Cooperative searching
Initialization
Parameter optimization
Particle swarm optimization
Source searching
Swarm robotics
This is an open access article under the CC BY-SA license.
Corresponding Author:
Mad Helmi Ab. Majid
Department of Software Engineering and Smart Technology, Faculty of Computer and Meta-Technology,
Universiti Pendidikan Sultan Idris
35900 Tanjong Malim, Perak, Malaysia
Email: madhelmi@meta.upsi.edu.my
1. INTRODUCTION
Target searching is an important task but very challenging to solve in real world scenarios. Target
searching is usually associated with complex and time-consuming processes despite strict constraints to be
met. For example, the searching mission for black box of Malaysian Airlines MH370 in the Indian Ocean
took huge efforts and involved a complex and challenging search environment. In this case, the boundary of
the searching environment based on global positioning system (GPS) coordinates was set before the
searching process took place. In addition to complex operations and huge challenges, the rescuers have a very
limited time to search for the black box before the battery of the black box dies out. This real scenario
demonstrates the importance of target searching and the need for a better target searching strategy and
solution in dealing with real world searching problems [1]. Other examples of target searching include search
2. Int J Elec & Comp Eng ISSN: 2088-8708
Impact of initialization of a modified particle swarm optimization on cooperative … (Mad Helmi Ab. Majid)
219
and rescue of earthquake victims, demining operation, radioactive leakage source detection, environmental
monitoring and surveillance [2]–[4].
The primary objective of target searching is to locate the source to its proximity location within a
specified boundary (e.g., search space dimension) and operational constraints (e.g., time, accuracy) [5].
Autonomous robots have been researched for the purpose of target searching tasks for a very long time.
However, searching using a single autonomous robotic platform is difficult to achieve optimal searching
result. Alternatively, cooperative searching using a group of robots has been proven to be more efficient and
able to give optimal searching results compared to a single robot searching [6]. From this perspective, the
concept of swarm robotics using relatively simple robots offered greater advantages for solving complex
target searching as a result of its robustness, scalability and flexibility characteristics [7]. Additionally,
incorporating intelligence algorithms allow swarm robots to make automatic decisions intelligently with
minimal interruption from humans while ensuring optimal accuracy of the search output [8].
There are different types of algorithms have been developed for swarm robotic target search such as
based on swarm intelligent (SI) algorithm (e.g., particle swarm optimization (PSO) [9]–[11], Ant colony
optimization (ACO) [12], bean optimization [13] and bacteria foraging optimization (BFO) [14]), behavior-
based approaches (e.g., group explosion strategy [15], firework explosion inspired [16] and sweep cleaning
[17] and stigmergy [18]), random walk (e.g., levy flight [19], Brownian motion [20]) dan hybrid strategy
(e.g., PSO-BFO [21], PSO-fruit fly optimization algorithm (FOA) [22], triangle formation [23] and random
walk or stochastic [24]). Among these types of swarm robotic target searching algorithms, SI based
algorithms have gain the greatest attention from the researchers because of: i) cooperative nature of the
algorithm itself ii) intelligence decision making capability, iii) high convergence rate, and iv) meet swarm
robot characteristics (i.e., scalable, flexible and robust).
PSO is the most common SI based algorithm for swarm robotic target searching. Initially PSO was
implemented for target searching in its original form but many modifications have been proposed to improve
the original PSO performance in target searching tasks. The implementation in its original form includes for
example by Hereford et al. [25] and Ab Aziz et al. [26]. On the other hand, the modifications of PSO were
performed for the following purposes: inclusion of obstacles avoidance capability [27], avoid trapping into
local optima or premature convergence [28], [29], incorporate multi target searching [30], [31], integrate
mobile target searching capability [32]. However, to the best of the author’s knowledge, the analysis of target
overshooting and the impact of different initialization strategies have not been deeply studied. In this paper,
we proposed a modified PSO algorithm to minimize target overshooting in addition to fast convergence and
avoiding premature convergence. Additionally, we also evaluate and compare the performance of the
modified PSO with three different initialization strategies. The rest of this paper is organized as follows:
Section 2 for research methodology, Section 3 for results and discussion and Section 4 for conclusion and
recommendations of future work.
2. RESEARCH METHOD
In this research, a modified PSO is proposed to improve PSO convergence speed, avoid robots stuck
in a local optimum or premature convergence and minimize target overshooting. Additionally, three
initialization strategies were designed for the proposed modified PSO. To validate the performance of the
modified PSO and evaluate the effect of different initialization strategies, a series of simulations using
MATLAB was performed. For the testing purpose, we implemented the algorithm on a swarm of
autonomous surface vehicles (ASV) which was represented by a complete kinematic and dynamic
mathematical model and a complete control system.
2.1. Modified PSO
Firstly, one of the criteria for good searching is a fast convergence. The faster the convergence, the
faster the location of the source can be determined and thus, meet the timing constraint of the searching task.
From the PSO perspective, fast convergence can be achieved through fast information sharing and update
among the robots in the swarm. Typically, this problem is associated with waiting periods using a
synchronous update. In this study, asynchronous updates will be used to improve the convergence speed.
Secondly, due to the nature of the PSO, robots may get stuck at local optima due to premature convergence.
Premature convergence occurs because of rapid decreasing of velocity as the robots rapidly approach or
converge to a local optima position where the updated velocity approaches zero and cause robots to lose their
exploration capability (i.e., decrease of swarm diversity) before finally stop moving. Once robots are trapped
into local optima, they have no capability to escape and are permanently stuck and fail to reach global
convergence. As a result, an inaccurate solution is obtained which affects the accuracy of the source’s
position estimation. Thirdly, target overshooting will result in inaccurate approximation of the target’s
location. Target overshooting primarily happened due to large velocity assigned to the robots as they
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Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 218-229
220
approached the optimal position. Consequently, more control effort is necessary to accommodate the back-
and-forth movements especially for a robot with limited turning capability before robots can reach a stable
convergence. Typically, this problem is caused by excessive velocity magnitude due to non-optimal or fixed
value of inertia weight and inappropriate selection of velocity limit, 𝑉
𝑚𝑎𝑥. To resolve these problems,
asynchronous dynamically adjustable particle swarm optimization (ADAPSO) is proposed in this study. The
velocity, v and position, p update equations of the proposed ADAPSO are given by (1) and (2):
𝑣𝑖(𝑘𝑖 + 1) = 𝜔𝑖(𝑘𝑖)𝑣𝑖(𝑘𝑖) + 𝑐1𝑖(𝑘𝑖)𝑟1,𝑖(𝑝𝐵𝑒𝑠𝑡𝑖(𝑘𝑖) − 𝑝𝑖(𝑘𝑖))
+𝑐2𝑖(𝑘𝑖)𝑟2,𝑖(𝑔𝐵𝑒𝑠𝑡(𝑘𝑖) − 𝑝𝑖(𝑘𝑖)) (1)
𝑝𝑖(𝑘𝑖 + 1) = 𝑝𝑖(𝑘𝑖) + 𝑣𝑖(𝑘𝑖 + 1) + 𝑞𝑖(𝑘 + 1) (2)
where ω is the inertia weight, c is the acceleration coefficient, r is the random number between 0 and 1, pBest
is the personal best position, gBest is the global best position, k is the iteration number and i is the robot’s
index. The vector q is used to adjust position of the robot such that communication between robots is
maintained. In this formulation, the velocity update is set as ||v|| ≤ Vmax to avoid explosion. The inertia weight
is defined as (3).
𝜔𝑖(𝑘𝑖) = 𝜔𝑖𝑛𝑖 − 𝛼(ℎ𝑖(𝑘𝑖) + 𝑔𝑖(𝑘𝑖)) + 𝛽𝑠(𝑘𝑖) (3)
where the evolutionary speed factor, h, and aggregation degree, s (defined according to [33]) and the
convergence speed factor, g are defined as:
ℎ𝑖(𝑘𝑖) = 1 − [
𝑚𝑖𝑛(𝑓(𝑝𝐵𝑒𝑠𝑡(𝑘𝑖−1)),𝑓(𝑝𝐵𝑒𝑠𝑡(𝑘𝑖)))
𝑚𝑎𝑥(𝑓(𝑝𝐵𝑒𝑠𝑡(𝑘𝑖−1)),𝑓(𝑝𝐵𝑒𝑠𝑡(𝑘𝑖)))
] (4)
𝑠𝑖(𝑘𝑖) =
𝑚𝑖𝑛(𝑓𝑘𝐵𝑒𝑠𝑡(𝑘𝑖),𝑓
̄(𝑘𝑖))
𝑚𝑎𝑥(𝑓𝑘𝐵𝑒𝑠𝑡(𝑘𝑖),𝑓
̄(𝑘𝑖))
(5)
𝑔𝑖(𝑘𝑖) = 1 − [
𝑚𝑖𝑛(𝑓(𝑔𝐵𝑒𝑠𝑡(𝑘𝑖−1)),𝑓(𝑔𝐵𝑒𝑠𝑡(𝑘𝑖)))
𝑚𝑎𝑥(𝑓(𝑔𝐵𝑒𝑠𝑡(𝑘𝑖−1)),𝑓(𝑔𝐵𝑒𝑠𝑡(𝑘𝑖)))
] (6)
where f is the fitness function of the robot expressed as measured source signal intensity. If the corresponding
robot is the best robot (i.e., robot with highest fitness value or highest source intensity measurement), the
second and the third terms of (1) become zero or close to zero due to the fact that robot current position is
close to pBest and gBest positions. As a result, the exploration capability of that robot temporarily (i.e., as
long as it is the best robot) dies out. For this reason, the inertia weight adjustment equation is further
modified as (7).
𝜔𝑖(𝑘𝑖) = {
𝜔𝑖(𝑘𝑖) +
𝜔𝑖𝑛𝑖
𝑘𝑖
if 𝑖isthebestrobot
𝜔𝑖(𝑘𝑖) otherwise
(7)
Small cognitive component (i.e., the term with pBest) and large social component (i.e., the term with
gBest) promotes search exploration space at the beginning and in the later stage, when cognitive component
becomes larger and social component becomes smaller, the possibility of convergence to global optima is
improved. The acceleration coefficients are defined as (8) and (9):
𝑐1,𝑖(𝑘) = 𝑐𝑖𝑛𝑖 +
1
2
𝑐𝑜𝑠 (
𝑡
𝑡𝑇𝑚𝑎𝑥
()) (8)
𝑐2,𝑖(𝑘) = 𝑐𝑖𝑛𝑖 −
1
2
𝑐𝑜𝑠 (
𝑡
𝑡𝑇𝑚𝑎𝑥
()) (9)
where cini is the initial acceleration coefficient, t is current time, tT is the total time taken to complete the
previous operation (i.e., sum of time taken for searching) process and tmax is the maximum searching time
allowed (i.e., limited by robot power resource).
4. Int J Elec & Comp Eng ISSN: 2088-8708
Impact of initialization of a modified particle swarm optimization on cooperative … (Mad Helmi Ab. Majid)
221
2.2. Initialization strategies
In this study, three different initialization strategies were proposed relevant to the source searching
task which are known as random initialization, equidistant initialization and centralized initializations as
illustrated in Figure 1. Firstly, for a random initialization, robots are randomly distributed in a search space
such that their initial position can be expressed as (10):
𝑃𝑖(0) = [
(𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛)𝑟𝑎𝑛𝑑 + 𝑥𝑚𝑖𝑛
(𝑦𝑚𝑎𝑥 − 𝑦𝑚𝑖𝑛)𝑟𝑎𝑛𝑑 + 𝑦𝑚𝑖𝑛
], ∀𝑖 ∈ 𝑁 (10)
where xmin and ymin are the minimum initialization positions and xmax and ymax are the maximum initialization
positions. The rand is the random number between 0 and 1 and i is the index of the individual robot. Random
initialization is a realistic initialization method in source searching because the proposed algorithm involves
stochastic elements and robots can perform source searching when there is no initial guest of the source
location. Secondly, for an equidistance initialization strategy, robots are dispersed at an equal angle and
radius surrounding the possible source location, rrs such that
𝑝𝑖(0) = 𝑟𝑟𝑠 [
𝑐𝑜𝑠(2𝜋𝑖/𝑁)
𝑠𝑖𝑛(2𝜋𝑖/𝑁)
], ∀𝑖 ∈ 𝑁 (11)
where N is the total number of robots used in the searching process. In a real implementation, this method is
not practically feasible because of difficulty in setting the exact robot arrangement at desired angle but it can
be useful for evaluating and benchmarking performance of the tested algorithm.
Thirdly, in a centralized deployment, robots are deployed from the same site (i.e., typically a known
reference coordinate) of the search space. This initialization method is typically useful for evaluating
performance of the algorithm when high similarity in terms of measured source signal intensity among the
robots exists. Mathematically, a centralize initialization is defined by (12).
𝑝𝑖(0) =
{
𝑝0, if 𝑖 = 1
𝑝0 + 2𝑟𝑖𝑛𝑖 [
𝑐𝑜𝑠 (
2𝜋
𝑁−1
𝑖)
𝑠𝑖𝑛 (
2𝜋
𝑁−1
𝑖)
] , if 𝑖 > 1
(12)
where p0,i is the initial position of the robot i, p0 is the initial position of deployment of the first robot and rini
is the initialization radius. In case none of the robots detect the source once they are deployed, each robot will
move randomly in the search space.
Figure 1. Different strategies of initializations
2.3. Termination criteria
In swarm robotics source searching tasks, robot physical limit and accuracy of the estimated source
position must be considered in order to set proper termination conditions. To obtain accurate estimation, only
converged robots are considered for the purpose of estimating the source position based on average value.
Notice that since rcon is not physically measurable because the source’s position is unknown and needs to be
estimated, the number of converged robots should be decided based on the intensity difference between the
converged robots. Let the final intensity measured by robot i is Ii,f(t), the position of the converge robots can
be defined as:
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 218-229
222
𝑝𝑖 = {𝑝1, … , 𝑝𝑁𝑠
: (
|𝐼𝑖,𝑓(𝑝𝑖)−𝐼𝑓,𝑚𝑎𝑥|||
𝐼𝑓,𝑚𝑎𝑥×100
< 𝛬) , ∀𝑖 ≠ 𝑗, 𝑖, 𝑗 = 1, . . . . , 𝑁𝑠 } (13)
where Λ ∈ [0,100] is the threshold of percentage intensity difference and If,max is the maximum intensity or
fitness of the gBest robot. Any robot in the swarm is classified as a converged robot if its percentage intensity
difference is less than certain percentage. A small value of Λ provides a better approximation of the source’s
position compared to a large value of Λ. Once terminated, the source position can be estimated from the
average position of the converge robots or taken to be equal to the final gBest position. In this study, the
following termination conditions are used to terminate the searching process:
− Maximum operating time is reached, t > tmax, or
− At least 3 robots successfully converge where fitness difference among the converged robots should be
less than Λ ≤ 5%, and,
− No improvement of gBest value is observed for the last 10 iterations
2.4. Simulation setup
For the purpose of evaluating the performance of the proposed PSO and initialization strategy, a
series of simulations were conducted. In this simulation, robots are a swarm of Autonomous Surface Vehicles
(ASVs) described by a complete kinematics and dynamics model with a proper speed and heading
controllers. To simulate performance of the proposed source searching strategy using ASV swarming
platform, a swarm robotic simulator was developed by using MATLABTM
as shown in Figure 2. This
simulator integrates the localization algorithm, control laws and ASV model as illustrated in Figure 3. The
outputs from the proposed source searching algorithm were used to generate the desired waypoint and thus,
the desired robot path. From the desired path, the reference control signals were computed which were then
fed into speed and heading controllers. The output of the controller was fed into an ASV model where the
states of the ASV can be determined. The controller keeps tracking the path until the robot reaches the
desired waypoint within an acceptable radius. Once the robot reaches the desired waypoint, a new waypoint
is generated and the process is repeated. This is a continuous loop process where it runs until the desired task
is completed (i.e., robot successfully reaches convergence) or the desired termination conditions are satisfied
or terminated by the user. The source used in this simulation is an underwater acoustic source represented by
a mathematical model representing a signal intensity decaying model.
Figure 2. Swarm robot simulator
For the simulation purposes, the acoustic source is represented by an intensity decaying model based
on a cylindrical spreading model given by (14).
𝑃
𝑎 = 2𝜋𝑟ℎ𝑠𝐼 (14)
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where Pa is the power of the source, r is the radius from the source (i.e., distance from ASV to the source), hs
is depth of the source (assumed to be constant) and I is the intensity of the source. By the energy
conservation law, the intensity of the acoustic source can be derived as (15):
𝑃
𝑎 = 2𝜋𝑟0ℎ𝑠𝐼0 = 2𝜋𝑟ℎ𝑠𝐼 (15)
and solving for I gives:
𝐼 = 𝐼0 (
𝑟0
𝑟
) + 𝑤 (16)
where r=drs is the distance between source position and current robot position, w is the added parameter
representing white Gaussian noise generated using MATLAB function wgn(), r0 is the reference radius and I0
is the corresponding acoustic source level. Note that this approximation model is a simplified model where it
does not consider refraction, reflection and propagation effect of the sound wave. White Gaussian noise is
considered since it closely represents the noise model in actual environment measured by the sensor. The
illustration of the source intensity with and without noise effect is shown in Figure 4(a) and 4(b),
respectively.
Figure 3. Swarm robotic simulator architecture
(a) (b)
Figure 4. Acoustic source (a) without noise (b) with noise
3. RESULTS AND DISCUSSION
Based on the proposed algorithm and methodology, a series of simulations were performed in order
to evaluate the fulfilment of the research objectives. The first objective of this research as previously stated is
to evaluate performance of the proposed PSO for source searching in terms of target overshooting and impact
of parameters on searching capability. The second objective is to evaluate the impact of different
initialization strategies on source searching performance.
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3.1. Modified PSO performance
The ADAPSO velocity limit, Vmax, should be examined as one of the ADAPSO parameters since it
has a direct impact on the efficiency of the source searching. In this study, the value of Vmax is independent of
the maximum robot velocity since the position update of the ADAPSO represents the desired waypoint
instead of the robot position update step. To ensure quick convergence and reduce target overshooting (i.e.,
oscillation around the source), which necessitates additional control efforts, it is essential to find the optimal
value Vmax. A choice of a small Vmax value may result in a slow convergence (indicated by actual searching
time), as shown in Figure 5, and may cause the robot to become trapped in local optima because robots lose
its capability to explore the searched area. On the other hand, a choice of a large Vmax may result in large
target overshooting or a continuous oscillation around the source, which is ineffective in terms of the desired
control, as shown in Figure 6. Figure 7 depicts the potential ideal convergence, which is quick with nearly no
oscillation convergence. Notice that equidistant initialization was used in this analysis to obtain consistent
initialization position for different repetition of the simulations.
A simulation was executed with various Vmax values to ascertain the best value, and the time it takes
to attain convergence for various initialization types (such as centralize, equidistance, and random) was
noted. Figure 7 displayed the amount of time required for various values of Vmax to converge for various types
of initializations in a search space with a size of 25x50 m2
. The best value for Vmax, which may be used with
various initialization procedures is 1.0 as shown in Figure 8. As a result, for the remaining simulation
experiments, this value is set as the velocity limit for ADAPSO velocity update.
Figure 5. Slow convergence of the proposed PSO for
small Vmax = 0.1
Figure 6. Oscillatory convergence of the proposed
PSO for large Vmax = 5
Figure 7. Optimal convergence of the proposed PSO
when Vmax = 1
Figure 8. Optimal value of Vmax for the proposed PSO
3.2. Impact of ADAPSO parameters
In this section, the effect of various ADAPSO parameters on source searching performance will be
examined. Depending on where the source signal is possibly detected, source searching can start from a
variety of initialization configurations. For instance, if the source signal is discovered only after a few
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iterations of random movement, the robots must execute source searching from the random position
initialization. Similarly, if the source signal is discovered only after the robots have been deployed into the
search space. Therefore, by taking into account various initialization strategies, a general overview of the
ADAPSO performance in terms of fitness differences between the robots (i.e., robot similarity) was provided.
Thus, the robustness of the proposed algorithm can be assessed taking into account a variety of initial source
signal strengths by evaluating various initialization strategies of the ADAPSO.
For various starting procedures, the effect of various ADAPSO parameters (see (1) through (9)) on
the searching performance is depicted by histograms as in Figure 9. When its effect on the source searching
performance was assessed, the appropriate parameter was relaxed (i.e., set to zero) for this reason. Despite
the fact that robots were deployed using various initialization techniques, it is clear from the figure that each
parameter has a favorable effect on the overall ADAPSO performance. Even when the distance between the
source position and first deployment site is similar, it takes more time to obtain convergence since the initial
degree of similarity among the robots in a centralized initialization strategy is high. This is due to the fact that
velocity update is minimal and the cognitive and social components of the ADAPSO become small when
gBest and pBest are near to each other. Because robots were initialized at equal radial distances from the
source, the equidistance initialization strategy also has the fastest convergence speed. The equally distributed
beginning pattern quickens the robots' convergence rate even though their initial fitness is equivalent. On the
other hand, random initialization has a medium rate of convergence since the robots' similarity depends on
their initial random positions. The pace of convergence increases with the proximity of the robots to the
source and with their random distribution. For the source searching task, however, only random and
centralized initializations are feasible and can easily be employed.
Figure 10 illustrates how ADAPSO parameters affect the search results in terms of swarm best
fitness, or gBest. The outcome demonstrated the beneficial effects of several ADAPSO parameters in terms
of convergence speed and accuracy. As shown in Figure 10, deactivating one of the parameters decreases the
ADAPSO's effectiveness. The combination of those parameters in the suggested ADAPSO algorithm, it is
evidence from this finding, aids the robots in achieving a faster convergence speed and greater accuracy.
Figure 9. Impact of parameters on the overall ADAPSO performance
Figure 10. The impact of parameters on the overall ADAPSO performance in term of average final fitness for
tmax = 200 s
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3.3. Impact of initialization
Similar to the preceding part, three initialization strategies: random, equidistant, and centralized or
initialization from the same region were taken into consideration to study the impact of initialization strategy.
The goal of this section is to evaluate the proposed algorithm's performance and robustness to other benchmark
methods under various initial robot configurations and robot counts. Benchmarking of ADAPSO's performance
is done using the basic PSO algorithms listed in Table 1. This simulation takes into account a search space of
25×50 m2
. Figure 11 displays the simulations' outcomes. Figure 12 displays the success rate for various
initialization techniques and for various robot counts. The dashed line indicates no convergence is observed.
According to the findings, the ADAPSO algorithm performs better than other algorithms in each of
the three different initialization setups. Due to their poor ability to escape local optima, particularly when a
small number of robots were taken into account, especially in a random and centralized initialization,
conventional PSO algorithms such as IWPSO and CFPSO for small numbers of robots fail to achieve
convergence compared to other algorithms. Because of its weak capacity to escape local optima caused by
constant inertia weight, the IWPSO completely fails in the centralized initialization. Although the CFPSO
can typically reach convergence, its rate of convergence is slow, especially when there are many robots that
share characteristics, as was seen in the centralized initialization. However, as shown in Figure 12, the
CFPSO has a greater propensity to trap into local optima when there are fewer robots present. It should be
noted that IWPSO and CFPSO are non-adaptive PSOs, which is the fundamental cause of the two algorithms'
inability to escape local optima when there is a significant degree of robot similarity. An example of a
complete illustration of a source searching using ADAPSO is shown in Figure 13. In this figure, the robot
traces during a complete source searching process when a source is positioned at ps = (20, 0) in an obstacle’s
free environment. In this example, robots were deployed close to each other using a centralized initialization.
Robots cooperatively search the target based on detected source signal and exchange the information within
the swarm to reach convergence.
Table 1. Selected benchmark PSO algorithms
Reference Algorithm Remarks
This study ADAPSO Adaptive
Zou et al. [34] IWPSO Non-adaptive
Yang et al. [33] DAPSO Adaptive
Zou et al. [34] CFPSO Non-adaptive
Hereford et al. [35] PEPSO Non-adaptive
Figure 11. Performance of ADAPSO for different initialization methods in a search space of 25×50 m2
using
N=5
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Figure 12. Success rate for different initialization configurations
Figure 13. Robot traces during a complete source searching process when source located at ps = (20, 0) in
obstacles free environment.
4. CONCLUSION
In this study, source searching based on a modified PSO has been proposed and tested using a
swarm of ASVs. The findings of the study showed that the proposed ADAPSO algorithm has successfully
achieved better searching capability after the velocity limit is optimized by avoiding excessive oscillation. In
addition, the impact of each parameter of ADAPSO has been evaluated and the result showed that each
parameter has significant impact on the searching effectiveness. Moreover, it is also shown that each
initialization strategy has a different impact on the searching operation. For future work, further improvement
will be considered. In some searching scenarios multiple sources may exist within the same search space. In
this situation, robots must be able to partition themselves into multiple sub swarms if all targets are the
targets of interest or otherwise, they must be able to differentiate a correct target from the false targets. This
process involves task distribution among the robots in the swarm may improve capability of the algorithm
which may bring a step further towards real world implementation.
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BIOGRAPHIES OF AUTHORS
Mad Helmi Ab. Majid received the B.Eng. degree in mechatronics engineering
and the M.S. mechatronics engineering from International Islamic University Malaysia
(IIUM), Malaysia, in 2009 and 2012 respectively. He holds a Ph.D. degree in robotics from
Universiti Science Malaysia, Penang, in 2019. Currently, he is a senior lecturer at the
Department of Software Engineering and Smart Technology, Faculty of Computer and Meta-
Technology, Universiti Pendidikan Sultan Idris, Malaysia. His research interests include
robotics, artificial intelligence, internet of things, smart systems, mechatronics system design,
swarm intelligence, biomimetics, optimization, cooperative robotics, educational robotics, and
STEM education. He can be contacted at email: madhelmi@meta.upsi.edu.my.
Mohd Rizal Arshad graduated from the University of Liverpool (UK), in 1994
with a B.Eng. in the field of medical electronics and instrumentation. He then pursued his
MSc. in electronic control engineering at the University of Salford (UK), graduating in Dec.
1995. Following from this, in early 1996, he continued his study with a PhD degree in
electronic engineering back at the University of Liverpool (UK) with specialization in robotic
vision system. After completing his PhD training, i.e., January 1999, he started working at the
Universiti Sains Malaysia (USM), Malaysia as a full-time academic. He has supervised many
postgraduate students at the MSc. and PhD. levels. He has also published actively in local and
international publications. He is currently a full professor in robotics at the School of
Electrical and Electronic Engineering, USM. He is currently the president of the Malaysian
Society for Automatic Control Engineers (MACE) and past-chair of the Oceanic Engineering
Society (OES) Malaysia Chapter. In early 2017, he was awarded with the professional
engineer (P.Eng) status by the Board of Engineer, Malaysia (BEM). Prof Rizal is well known
as the pioneer of underwater system technology research efforts in Malaysia. His research
projects are mainly in the area of underwater robotic platform development, new sensing
device and mechanisms, and intelligent control algorithms. He has strong international
research networks with researchers from countries such as United Kingdom, Singapore, India,
USA and France. He can be contacted at email: eerizal@usm.my.
Mohd Faid Yahya graduated from the International Islamic University Malaysia
in 2009 with a Bachelor degree in mechatronics engineering. In 2013, he completed a MSc.
degree in mechatronics engineering at the same university. He then completed his PhD. degree
at the University of Science, Malaysia with a specialization in robotics in 2019. His interest
includes vision-based robot control, visual serving, control system, machine vision, and
artificial intelligence. He is currently a senior lecturer under Mechatronics Engineering
Department at Faculty of Electrical Engineering in Universiti Teknikal Malaysia Melaka. He
can be contacted at email: faid@utem.edu.my.
Abu Bakar Ibrahim is an associate professor in the Computing Department,
Department of Software Engineering and Smart Technolgy, Faculty of Computer and Meta-
Technology at Sultan Idris Education University, Malaysia. He is received a B.Sc. in electrical
engineering and a Master's Degree from Universiti Teknologi Malaysia (UTM) in 1998 and
2000, respectively. He received a Ph.D. in electronic engineering (communication) from
Universiti Teknikal Malaysia Melaka (UTeM) in the years 2013. He had professional teaching
experience in the development of low noise amplifier (LNA), radio frequency communication
system, instructional technology, engineering mathematics, wireless communication and
engineering education. He has developed confidence and interest in researching and teaching
areas to enhance creative innovation in engineering, science and technology. He can be
contacted at email: abubakar.ibrahim@meta.upsi.edu.my.