This document describes using fuzzy logic for robot navigation. Ultrasonic sensors are mounted on a robot to detect obstacles to the right, front, and left. Fuzzy logic is used to coordinate multiple reactive behaviors like obstacle avoidance, following edges, and moving toward a target. Simulation results show the strategy allows efficient navigation in complex environments. The robot can avoid obstacles, decelerate at turns, escape U-shapes, and reach targets using integrated ultrasonic sensors and fuzzy behavior control.
Fuzzy Logic Application in Robotics( Humanoid Push Recovery)IIIT Allahabad
This document describes a less computationally intensive fuzzy logic controller for humanoid push recovery. The controller uses a hierarchical fuzzy inference system with two levels (FIS1 and FIS2) to reduce the number of rules needed. FIS1 uses force and direction of motion as inputs to determine reaction as small, average, or large rolls or pitches. FIS2 then takes those outputs and determines the recovery strategy (ankle, hip, knee) and outcome (fall or non-fall). The controller was tested using experimental data on push forces applied to different joints of a humanoid robot to evaluate its ability to simplify the complex behavior of push recovery.
Artificial Neural Network based Mobile Robot NavigationMithun Chowdhury
This document presents a neural network based navigation system for mobile robots. It uses an artificial neural network (ANN) trained with Backpropagation Through Time (BPTT) to plan paths and navigate around obstacles. The input to the ANN is the state of the robot described using polar coordinates relative to the target position and orientation. Obstacles are also included as inputs by dividing the area in front of the robot into regions. The cost function for training is extended with a potential field to repel the robot from obstacles. Simulation results showed the robot could successfully navigate a maze and reach the target while avoiding multiple obstacles.
Fuzzy logic is a method of reasoning that resembles human decision making by allowing for intermediate possibilities between yes and no or true and false. It is used in control systems like temperature controllers, anti-lock braking systems, washing machines, and air conditioners. Fuzzy logic applications can be found in areas like aerospace, automotive, defense, electronics, mining, robotics, securities, and industrial processes. The field of fuzzy logic continues to grow and provide opportunities to develop effective controllers for complex systems across many domains.
Design and fabrication of delta robot.pptx igniteAbhishekKash2
A Delta robot is a parallel robot that consists of three arms connected to several joints at the base. The proposed project introduces the two arms planar delta robot which is used for producing high torque for the handling of small objects. Applications - these type of robots is used for picking and placing products in groups and placing them in a container or in an assembly pattern.
The document discusses Vehicular Ad-Hoc Networks (VANETs) which allow vehicles to communicate with each other to share safety and traffic information. It outlines the architecture of VANETs including vehicle-to-vehicle and vehicle-to-roadside communications. The document then covers security challenges in VANETs such as threats to availability, authentication, and confidentiality. It also discusses challenges like mobility, volatility, and balancing privacy with authentication and liability. Finally, it lists security requirements for VANETs including message authentication, non-repudiation, availability, and privacy protection.
This document discusses mobile robot vehicles and provides several examples of different types of mobile robot platforms. It covers key concepts related to mobility including configuration space, task space, degrees of freedom, and actuation. Examples discussed include trains, hovercrafts, helicopters, fixed-wing aircraft, underwater robots, and cars. Each example describes the robot's configuration space, degrees of freedom, actuation, and task space. The document aims to explain the basic issues involved in programming robots to perform tasks by analyzing different types of mobile robot platforms and their mobility characteristics.
Fuzzy Logic Application in Robotics( Humanoid Push Recovery)IIIT Allahabad
This document describes a less computationally intensive fuzzy logic controller for humanoid push recovery. The controller uses a hierarchical fuzzy inference system with two levels (FIS1 and FIS2) to reduce the number of rules needed. FIS1 uses force and direction of motion as inputs to determine reaction as small, average, or large rolls or pitches. FIS2 then takes those outputs and determines the recovery strategy (ankle, hip, knee) and outcome (fall or non-fall). The controller was tested using experimental data on push forces applied to different joints of a humanoid robot to evaluate its ability to simplify the complex behavior of push recovery.
Artificial Neural Network based Mobile Robot NavigationMithun Chowdhury
This document presents a neural network based navigation system for mobile robots. It uses an artificial neural network (ANN) trained with Backpropagation Through Time (BPTT) to plan paths and navigate around obstacles. The input to the ANN is the state of the robot described using polar coordinates relative to the target position and orientation. Obstacles are also included as inputs by dividing the area in front of the robot into regions. The cost function for training is extended with a potential field to repel the robot from obstacles. Simulation results showed the robot could successfully navigate a maze and reach the target while avoiding multiple obstacles.
Fuzzy logic is a method of reasoning that resembles human decision making by allowing for intermediate possibilities between yes and no or true and false. It is used in control systems like temperature controllers, anti-lock braking systems, washing machines, and air conditioners. Fuzzy logic applications can be found in areas like aerospace, automotive, defense, electronics, mining, robotics, securities, and industrial processes. The field of fuzzy logic continues to grow and provide opportunities to develop effective controllers for complex systems across many domains.
Design and fabrication of delta robot.pptx igniteAbhishekKash2
A Delta robot is a parallel robot that consists of three arms connected to several joints at the base. The proposed project introduces the two arms planar delta robot which is used for producing high torque for the handling of small objects. Applications - these type of robots is used for picking and placing products in groups and placing them in a container or in an assembly pattern.
The document discusses Vehicular Ad-Hoc Networks (VANETs) which allow vehicles to communicate with each other to share safety and traffic information. It outlines the architecture of VANETs including vehicle-to-vehicle and vehicle-to-roadside communications. The document then covers security challenges in VANETs such as threats to availability, authentication, and confidentiality. It also discusses challenges like mobility, volatility, and balancing privacy with authentication and liability. Finally, it lists security requirements for VANETs including message authentication, non-repudiation, availability, and privacy protection.
This document discusses mobile robot vehicles and provides several examples of different types of mobile robot platforms. It covers key concepts related to mobility including configuration space, task space, degrees of freedom, and actuation. Examples discussed include trains, hovercrafts, helicopters, fixed-wing aircraft, underwater robots, and cars. Each example describes the robot's configuration space, degrees of freedom, actuation, and task space. The document aims to explain the basic issues involved in programming robots to perform tasks by analyzing different types of mobile robot platforms and their mobility characteristics.
Fuzzy logic is a form of multivalued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or high/low. It provides a mathematical framework for representing uncertainty and imprecision in measurement and human cognitive processes. Fuzzy logic systems use fuzzy membership functions and fuzzy "IF-THEN" rules to map inputs to outputs. They include fuzzification of inputs, an inference system to evaluate rules, aggregation of outputs, and defuzzification to produce a crisp output. Common applications include controllers that can handle complex or imprecise inputs better than conventional digital controllers.
Fuzzy logic is a form of logic that deals with reasoning that is approximate rather than precise. It allows intermediate values to be defined between conventional evaluations like true/false, and uses a continuum of truth values between 0 and 1. Fuzzy logic is useful for problems with imprecise or uncertain data, and can represent human reasoning that uses approximate terms like "warm" or "fast". It has been applied in various systems to control variables like temperature, speed, and focus based on fuzzy linguistic rules.
Artificial Intelligence lecture notes. AI summarized notes on uncertainty and handling it through fuzzy logic, tipping problem scenarios are seen in it, for reading and may be for self-learning, I think.
The advent of Mobile Robotics changed the definition of robotics and brought in some very interesting technologies paving the way for cutting edge sciences like AI, Behaviour Based Systems, etc
This document is a term paper report submitted by Priya Hada, a 5th semester B.Tech student in Electronics and Communication Engineering at Amity University Rajasthan. The report is about a line follower robot and includes an introduction, hardware description, working procedure, software skills used, and conclusions. The introduction provides background on line follower robots and describes their use in industrial applications to transport materials along predetermined paths. The hardware section details the basic components used including an AT89C51 microcontroller, IR sensors, motor driver circuitry, and a power supply.
This document discusses fuzzy systems and their applications. It introduces fuzzy logic as an extension of Boolean logic that allows for partial set memberships and uncertainties. It provides examples of fuzzy systems in washing machines, vacuum cleaners, rice cookers, and cars. Fuzzy logic is used in washing machines to adjust operations based on sensor readings. Vacuum cleaners use fuzzy logic to control motor speed based on distance sensors. Rice cookers employ neuro-fuzzy systems for precise heat adjustment. Cars can use fuzzy logic for automatic transmissions to shift gears like an experienced human driver.
This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
This document provides an overview of localization and mapping techniques for robotics, including:
- Markov localization and particle filters for estimating robot location as a probability distribution.
- The Kalman filter for optimally fusing uncertain sensor measurements and updating location estimates.
- Simultaneous localization and mapping (SLAM) and the "hen-egg" problem of needing a map to localize and a location to build a map.
- Feature-based SLAM approaches that build maps from distinct environmental features.
- FastSLAM which uses a particle filter to estimate robot location and build maps from sensor measurements.
- Key challenges in SLAM like recognizing previously visited places and handling dynamic environments.
1) The document discusses robot dynamics and defines equations for velocity and kinetic energy.
2) It presents equations to calculate the velocity of points on robot links using transformation matrices and derivatives with respect to joint variables.
3) Equations are provided to calculate the kinetic energy of elements of mass on robot links as a function of linear and angular velocities, allowing the total kinetic energy to be determined by summing over all links.
The document discusses fuzzy sets and fuzzy logic. It defines fuzzy as meaning not clear or precise, with blurred outlines. Fuzzy sets allow partial membership in a set, whereas classical sets have binary membership. Fuzzy sets are represented by membership functions that can take on values between 0 and 1. Common fuzzy set operations like union, intersection, and complement are defined. Fuzzy logic is then introduced as a way to represent imprecise concepts and approximate reasoning, extending conventional binary logic to allow intermediate truth values.
Fuzzy logic was introduced by Lotfi Zadeh in 1965 to address problems with classical logic being too precise. Fuzzy logic allows for truth values between 0 and 1 rather than binary true/false. It involves fuzzy sets, membership functions, linguistic variables, and fuzzy rules. Fuzzy logic can be applied to knowledge representation and inference using concepts like fuzzy predicates, relations, modifiers and quantifiers. It has various applications including household appliances, animation, industrial automation, and more.
The document provides an overview of Multiple Hypothesis Tracking (MHT), which differs from Probabilistic Data Association (PDA) tracking algorithms by maintaining all association hypotheses over time rather than combining them. MHT addresses tracking challenges like delayed data association, track initiation/termination, and high clutter. It works by generating a tree of feasible data association hypotheses at each time step and pruning unlikely hypotheses to limit exponential growth. The probability of hypotheses is calculated recursively to rank them.
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
This document discusses artificial intelligence for game playing. It introduces different types of games and optimal strategies for games like minimax and alpha-beta pruning. It also discusses challenges for games of imperfect information that include elements of chance, as well as techniques for heuristic evaluation and expected value calculations when chance is involved.
The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms
This document discusses using vehicular networks to disseminate information for applications like traffic and parking management. It proposes that vehicles equipped with sensors and wireless connectivity could form ad hoc networks to share real-time data. This would allow more efficient routing, reduced wait times, and savings on fuel. Several data dissemination approaches are described, including vehicle-to-infrastructure, vehicle-to-vehicle, and an epidemic dissemination method. Challenges with scalability, mobility, and reliability are also discussed. The goal is to explore how vehicular networks can efficiently distribute large amounts of sensing data in dynamic mobile environments.
This document summarizes key aspects of robotics and a line following robot project. It discusses that robotics involves designing and building intelligent mechanical agents to perform tasks autonomously or with guidance. It then describes a line following robot that uses infrared sensors to detect and follow a black line on a white surface without human control. The robot is able to correct itself to stay on the track and uses different motor speeds to enable turns. Microcontrollers like the ATmega8L are used as the processing system to generate outputs from sensor inputs.
The document discusses the cuckoo search algorithm, which is a metaheuristic algorithm for global optimization inspired by the breeding behavior of some cuckoo species. It describes how cuckoos lay their eggs in other birds' nests, sometimes ejecting the host birds' eggs. The algorithm uses three rules - cuckoos lay one egg at a time in randomly chosen nests, the best nests carry over to future generations, and hosts can discover alien eggs with some probability. It also discusses Levy flights for random walks and the steps of the cuckoo search algorithm which involves generating nests, replacing eggs based on fitness, and abandoning nests to avoid local optimization. Finally, it lists some applications of the c
The document is a report on using fuzzy logic for robotic control. It discusses fuzzy sets and membership functions, fuzzy inference systems, and how fuzzy logic can be used for behaviors like obstacle avoidance, following edges, and target steering. The report provides examples of how fuzzy logic controllers allow incorporating human expertise to control systems without precise mathematical models. It also discusses applications of fuzzy logic for robot control that have been presented in other literature.
The document presents a presentation on robotics control using fuzzy logic. It introduces fuzzy logic and how it handles partial truths between completely true and completely false. It also discusses fuzzy sets and membership functions. The presentation then covers fuzzy logic control and its applications to robotics, including defining robots and discussing different types. It concludes with a summary of how the project uses fuzzy logic to realize reactive robot navigation behaviors.
Fuzzy logic is a form of multivalued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or high/low. It provides a mathematical framework for representing uncertainty and imprecision in measurement and human cognitive processes. Fuzzy logic systems use fuzzy membership functions and fuzzy "IF-THEN" rules to map inputs to outputs. They include fuzzification of inputs, an inference system to evaluate rules, aggregation of outputs, and defuzzification to produce a crisp output. Common applications include controllers that can handle complex or imprecise inputs better than conventional digital controllers.
Fuzzy logic is a form of logic that deals with reasoning that is approximate rather than precise. It allows intermediate values to be defined between conventional evaluations like true/false, and uses a continuum of truth values between 0 and 1. Fuzzy logic is useful for problems with imprecise or uncertain data, and can represent human reasoning that uses approximate terms like "warm" or "fast". It has been applied in various systems to control variables like temperature, speed, and focus based on fuzzy linguistic rules.
Artificial Intelligence lecture notes. AI summarized notes on uncertainty and handling it through fuzzy logic, tipping problem scenarios are seen in it, for reading and may be for self-learning, I think.
The advent of Mobile Robotics changed the definition of robotics and brought in some very interesting technologies paving the way for cutting edge sciences like AI, Behaviour Based Systems, etc
This document is a term paper report submitted by Priya Hada, a 5th semester B.Tech student in Electronics and Communication Engineering at Amity University Rajasthan. The report is about a line follower robot and includes an introduction, hardware description, working procedure, software skills used, and conclusions. The introduction provides background on line follower robots and describes their use in industrial applications to transport materials along predetermined paths. The hardware section details the basic components used including an AT89C51 microcontroller, IR sensors, motor driver circuitry, and a power supply.
This document discusses fuzzy systems and their applications. It introduces fuzzy logic as an extension of Boolean logic that allows for partial set memberships and uncertainties. It provides examples of fuzzy systems in washing machines, vacuum cleaners, rice cookers, and cars. Fuzzy logic is used in washing machines to adjust operations based on sensor readings. Vacuum cleaners use fuzzy logic to control motor speed based on distance sensors. Rice cookers employ neuro-fuzzy systems for precise heat adjustment. Cars can use fuzzy logic for automatic transmissions to shift gears like an experienced human driver.
This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
This document provides an overview of localization and mapping techniques for robotics, including:
- Markov localization and particle filters for estimating robot location as a probability distribution.
- The Kalman filter for optimally fusing uncertain sensor measurements and updating location estimates.
- Simultaneous localization and mapping (SLAM) and the "hen-egg" problem of needing a map to localize and a location to build a map.
- Feature-based SLAM approaches that build maps from distinct environmental features.
- FastSLAM which uses a particle filter to estimate robot location and build maps from sensor measurements.
- Key challenges in SLAM like recognizing previously visited places and handling dynamic environments.
1) The document discusses robot dynamics and defines equations for velocity and kinetic energy.
2) It presents equations to calculate the velocity of points on robot links using transformation matrices and derivatives with respect to joint variables.
3) Equations are provided to calculate the kinetic energy of elements of mass on robot links as a function of linear and angular velocities, allowing the total kinetic energy to be determined by summing over all links.
The document discusses fuzzy sets and fuzzy logic. It defines fuzzy as meaning not clear or precise, with blurred outlines. Fuzzy sets allow partial membership in a set, whereas classical sets have binary membership. Fuzzy sets are represented by membership functions that can take on values between 0 and 1. Common fuzzy set operations like union, intersection, and complement are defined. Fuzzy logic is then introduced as a way to represent imprecise concepts and approximate reasoning, extending conventional binary logic to allow intermediate truth values.
Fuzzy logic was introduced by Lotfi Zadeh in 1965 to address problems with classical logic being too precise. Fuzzy logic allows for truth values between 0 and 1 rather than binary true/false. It involves fuzzy sets, membership functions, linguistic variables, and fuzzy rules. Fuzzy logic can be applied to knowledge representation and inference using concepts like fuzzy predicates, relations, modifiers and quantifiers. It has various applications including household appliances, animation, industrial automation, and more.
The document provides an overview of Multiple Hypothesis Tracking (MHT), which differs from Probabilistic Data Association (PDA) tracking algorithms by maintaining all association hypotheses over time rather than combining them. MHT addresses tracking challenges like delayed data association, track initiation/termination, and high clutter. It works by generating a tree of feasible data association hypotheses at each time step and pruning unlikely hypotheses to limit exponential growth. The probability of hypotheses is calculated recursively to rank them.
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
This document discusses artificial intelligence for game playing. It introduces different types of games and optimal strategies for games like minimax and alpha-beta pruning. It also discusses challenges for games of imperfect information that include elements of chance, as well as techniques for heuristic evaluation and expected value calculations when chance is involved.
The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms
This document discusses using vehicular networks to disseminate information for applications like traffic and parking management. It proposes that vehicles equipped with sensors and wireless connectivity could form ad hoc networks to share real-time data. This would allow more efficient routing, reduced wait times, and savings on fuel. Several data dissemination approaches are described, including vehicle-to-infrastructure, vehicle-to-vehicle, and an epidemic dissemination method. Challenges with scalability, mobility, and reliability are also discussed. The goal is to explore how vehicular networks can efficiently distribute large amounts of sensing data in dynamic mobile environments.
This document summarizes key aspects of robotics and a line following robot project. It discusses that robotics involves designing and building intelligent mechanical agents to perform tasks autonomously or with guidance. It then describes a line following robot that uses infrared sensors to detect and follow a black line on a white surface without human control. The robot is able to correct itself to stay on the track and uses different motor speeds to enable turns. Microcontrollers like the ATmega8L are used as the processing system to generate outputs from sensor inputs.
The document discusses the cuckoo search algorithm, which is a metaheuristic algorithm for global optimization inspired by the breeding behavior of some cuckoo species. It describes how cuckoos lay their eggs in other birds' nests, sometimes ejecting the host birds' eggs. The algorithm uses three rules - cuckoos lay one egg at a time in randomly chosen nests, the best nests carry over to future generations, and hosts can discover alien eggs with some probability. It also discusses Levy flights for random walks and the steps of the cuckoo search algorithm which involves generating nests, replacing eggs based on fitness, and abandoning nests to avoid local optimization. Finally, it lists some applications of the c
The document is a report on using fuzzy logic for robotic control. It discusses fuzzy sets and membership functions, fuzzy inference systems, and how fuzzy logic can be used for behaviors like obstacle avoidance, following edges, and target steering. The report provides examples of how fuzzy logic controllers allow incorporating human expertise to control systems without precise mathematical models. It also discusses applications of fuzzy logic for robot control that have been presented in other literature.
The document presents a presentation on robotics control using fuzzy logic. It introduces fuzzy logic and how it handles partial truths between completely true and completely false. It also discusses fuzzy sets and membership functions. The presentation then covers fuzzy logic control and its applications to robotics, including defining robots and discussing different types. It concludes with a summary of how the project uses fuzzy logic to realize reactive robot navigation behaviors.
Roboticists develop robotic devices that can move autonomously and be programmed to behave in certain ways. Robots are considered intelligent if they can safely interact with unstructured environments while achieving specified tasks. The word robotics was first used in a 1942 Isaac Asimov short story and he explored ideas like robotherapists. Asimov also established three laws of robotics concerning not allowing or causing harm to humans. There are different types of robots including mobile, rolling, walking, stationary, autonomous, and remote-controlled robots that can have various purposes like exploration, manual labor, or controlled tasks.
Fuzzy logic is a rule-based system that handles ambiguity and vagueness between two extremes. It allows systems to be defined using logic equations rather than complex math. The paper describes how a fuzzy logic system was used to control a solar tracking system. It discusses the history and key concepts of fuzzy logic, including membership functions, fuzzy rules and subsets, which allow systems to model real-world gray areas between black and white, true and false, definitions.
Learning Structure, Reusability And Real Time Modeling In Teams Of Autonomous...ahmad bassiouny
This document summarizes a research project on developing techniques for controlling teams of autonomous robots. The goals are to enable large robot teams, improve effectiveness in adversarial tasks, and enable rapid adaptability. The research will integrate low-level and high-level control, model opponents, and distribute planning and communication hierarchically. Milestones include demonstrations on 5-10 robots in Year 1 and 10-20 robots in Year 2.
Fusion Engines for Input Multimodal Interfaces: a SurveyJean Vanderdonckt
Fusion engines are fundamental components of multimodal interactive systems, to interpret temporal combinations of deterministic as well as non-deterministic inputs whose meaning can vary according to the context, user and task. While various surveys have already been released on the topic of multimodal interactive systems, the current paper focuses on the design, specification, construction and evaluation of fusion engines. The article first introduces the adopted terminology and the major challenges that fusion engines propose to solve. Further, a history of the work achieved in the field of fusion engines is presented according to the main phases of the BRETAM model. A classification of existing approaches for fusion engines is then presented. The classification dimensions include the types of applications, the fusion principles and the temporal aspects. Finally, unsolved challenges, such as software frameworks, quantitative evaluation, machine learning and adaptation, sketch future work in the field of fusion engines.
Fuzzy logic is a form of logic that deals with reasoning that is approximate rather than fixed and exact. It was introduced in 1965 with the proposal of fuzzy set theory by Lotfi Zadeh. Fuzzy logic uses fuzzy sets and membership functions to deal with imprecise or uncertain inputs and allows for reasoning that allows for partial truth of inputs between fully true and fully false. Fuzzy controllers combine fuzzy logic with control theory to control complex systems. They involve fuzzification of inputs, applying fuzzy rules through inference, and defuzzification of outputs to obtain a crisp control action.
International Journal of Fuzzy Logic Systems (IJFLS) ijfls
International Journal of Fuzzy Logic Systems (IJFLS) is an open access peer-reviewed journal that covers all topics in theoretical, experimental and applied fuzzy techniques and systems. It is aimed to bring together researchers and developers from both academia and industry to discuss the latest scientific and theoretical advances in this field, and to demonstrate the state-of-the-art systems. The journal solicits original technical papers that were not previously published and are not currently under review for publication elsewhere.
This document discusses fuzzy genetic algorithms (FGAs), which combine fuzzy logic and genetic algorithms. It provides definitions of fuzzy logic and genetic algorithms. Fuzzy logic handles imprecise variables between 0 and 1, while genetic algorithms use techniques like selection, crossover and mutation to evolve solutions. The document notes that FGAs use fuzzy logic techniques to improve genetic algorithm behavior and components. It describes different FGA approaches and lists application sectors like engineering and economics.
Dokumen tersebut membahas tentang logika fuzzy, mulai dari pengertian, sejarah, derajat kebenaran, variabel linguistik, kelebihan dan kekurangan, serta contoh aplikasi logika fuzzy seperti pada mesin cuci.
This document discusses the use of robots in power plants. It begins with introductions to artificial intelligence and its applications. There are three main types of power plants - thermal, hydroelectric, and nuclear. Robots can be used for mobile monitoring in thermal plants using sensors. One robot described is a "line scout" with a robotic arm that can inspect and perform maintenance on high-voltage transmission lines. Robots are also useful in nuclear plants for hazardous environments like areas with radiation. One robot highlighted is a "snake-arm" robot that can perform inspections and navigate confined spaces to reach areas that are difficult for humans, such as being used currently in nuclear plant maintenance and inspections.
This document discusses genetic algorithms and their components. It begins by explaining that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that uses techniques like inheritance, mutation, selection, and crossover. It then defines the key terms used in genetic algorithms, such as individuals, populations, chromosomes, genes, and fitness functions. The rest of the document provides more details on genetic algorithm components like representation of solutions, selection of individuals, crossover and mutation operations, and the general genetic algorithm process.
SOLAR TREE technical seminar PPT(by mohsin khan)Mohsin Khan
The document discusses solar trees, which are a decorative way to produce solar energy and electricity using multiple solar panels arranged in a tree-like structure on a tall pole. Solar trees offer advantages over traditional solar panels like requiring less land and being able to generate energy from both sunlight and wind. However, solar trees also have disadvantages such as high costs and potential hazards to wildlife. The document outlines potential applications of solar trees for street lighting and industrial power supply and envisions a future where solar trees help meet increasing energy demands in a sustainable way.
now a days power requirement is increasing day by day.
to meet the requirements new power plants constructing, for maintenance of these plants skilled man power is not sufficient. in such cases a robot which can maintain the power system satisfies the requirements.
The document describes a solar tree, which is a structure that produces solar energy in an efficient manner using solar panels arranged like leaves on a tree. It requires less land area than traditional solar panel systems to generate the same amount of energy. The key components of a solar tree are the solar panels, a tall tower, batteries, LED lights, and connecting stems. The solar panels are arranged in a spiralling pattern up the tower to maximize sunlight exposure. Solar trees provide clean energy with less space and have applications for street lighting, household power supply, and charging electric vehicles. However, they also have some disadvantages relating to cost, safety, and impacts on wildlife.
Human: Thank you for the summary. You captured the key
Robotics and Automation.
This slide describes the concepts of robotics and automation. Line follower is considered as the perfect start of automation robots.
Robo India here in this slide present Construction of Line follower using 8051 Micro controller. the same can be upgraded to obstacle avoiding robot or a wall follower robot.
We are hearing you. Please share your views, we are found at-
Website: http://roboindia.com
email:info@roboindia.com
DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM NITISH K
The document discusses the design and implementation of a path planning algorithm for a wheeled mobile robot in a known dynamic environment. It describes using an A* algorithm at a central control station to calculate the shortest path for the robot. If obstacles are detected, the robot's location and obstacle information is sent to update the environment map. The control station then recalculates the new shortest path for the robot. The system was tested experimentally and in simulation, showing it can effectively calculate the shortest path in a dynamic environment.
Design of Mobile Robot Navigation system using SLAM and Adaptive Tracking Con...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document describes a design for mobile robot navigation using simultaneous localization and mapping (SLAM) and an adaptive tracking controller with particle swarm optimization in indoor environments. An adaptive fuzzy tracking controller is designed using 9 fuzzy rules to calculate a reference path for navigation between a starting and goal point. Particle swarm optimization is then used to optimize and reduce the time required for the calculated path. The controller is simulated in two indoor environments containing obstacles, and particle swarm optimization is shown to reduce navigation time compared to without its use. This approach allows for efficient mobile robot navigation in indoor monitoring applications.
Autonomous Path Planning and Navigation of a Mobile Robot with Multi-Sensors ...CSCJournals
The mobile robot is applied widely and investigated deeply in industrial fields, meanwhile, mobile robot autonomous path planning and navigation algorithm is a hot research topic. In this paper, firstly mobile robot is introduced, the general path planning and navigation algorithms of the mobile robot are reviewed, then a fuzzy logic with filter smoothing is proposed based on the data from the laser scan sensor and GPS module, which is useful for mobile robot to find the best path to the destination automatically according to the position and size of the gaps between the obstacles in the dynamic environment, finally our designed mobile robot and corresponding Android APP are introduced, the path planning and navigation algorithms are tested on this mobile robot, the testing result shows that this algorithm is globally optimized, quickly responded, battery power and hardware cost saved compared with other algorithms, it is suitable for the mobile robot that is running on the embedded system and it can satisfy our design requirement.
Design and implementation of path planning algorithm for wheeled mobile robot...eSAT Journals
Abstract Path planning in mobile robots must ensure optimality of the path. The optimality achieved may be in path, time, energy consumed etc. Path planning in robots also depends on the environment in which it operates like, static or dynamic, known or unknown etc. Global path planning using A* algorithm and genetic algorithm is investigated in this paper. A known dynamic environment, in which a control station will compute the shortest path and communicate to the mobile robot and the mobile robot, will traverse through this path to reach the goal. The control station will keep track of the path traversed by the robot. The mobile robot navigates through the shortest path and if the robot detects any obstacle in the destined path, the mobile robot will update the information about the environment and this information together with the current location will be communicated to the control station. Then the control station, with the updated map of the environment and new starting location and destination recalculates the new shortest path, if any, and will communicate to the mobile robot so that it can reach the destination. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results demonstrate that the technique effectively calculates the shortest path in known dynamic environment and allows the robot to quickly accomplish the mission.
Design and implementation of path planning algorithm for wheeled mobile robot...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Attitude Estimation And Compensation In Odometric Localization of Mobile Robo...Waqas Tariq
The paper introduces the attitude estimation and compensation in odometric localization of a differential drive indoor mobile robot. A mobile robot navigates through an inclined indoor environment, wherein localization using only wheel encoder is erroneous. The robot uses inertial sensors such as gyroscope, accelerometer and magnetometer to calculate its attitude and acquires a three degree of rotational data. It is observed that the attitude update using gyroscopes alone are prone to diverge and hence error needs to be eliminated. The advantage of MEMS sensors is less-cost while complementary filter algorithm is low complexity in implementation. The performance of the proposed complementary filter algorithm for attitude estimation and compensation in odometric localization are shown by experiment and analysis of results.
Trajectory reconstruction for robot programming by demonstration IJECEIAES
The reproduction of hand movements by a robot remains difficult and conventional learning methods do not allow us to faithfully recreate these movements because it is very difficult when the number of crossing points is very large. Programming by Demonstration gives a better opportunity for solving this problem by tracking the user’s movements with a motion capture system and creating a robotic program to reproduce the performed tasks. This paper presents a Programming by Demonstration system in a trajectory level for the reproduction of hand/tool movement by a manipulator robot; this was realized by tracking the user’s movement with the ArToolkit and reconstructing the trajectories by using the constrained cubic spline. The results obtained with the constrained cubic spline were compared with cubic spline interpolation. Finally the obtained trajectories have been simulated in a virtual environment on the Puma 600 robot.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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The document discusses localization and mapping techniques for autonomous navigation using stereo vision. It describes using efficient stereo algorithms to build local maps, visual odometry for precise registration of robot motion and maps, and integrating local maps into a globally consistent map. The approach is tested in outdoor environments, where it is able to build accurate maps in real-time and outperform other teams in validation tests.
GENERATION AND DEPARTABILITY OF GVG FOR CAR-LIKE ROBOTcscpconf
This paper presents an algorithm, based on conventional GVG that enables a car-like robot find a collision free path from depart configuration to some goal position in an environment
containing some convex obstacles. Prior research on GVG prescribed path for a circular robot. The circular robot is holonomic system, but this time GVG is used in nonholonomic system. The
proposed algorithm enables the car-like robot depart the GVG to the goal position with the nonholonomic path.
The document proposes a framework that uses intelligent mobile devices to enable indoor wireless location tracking, navigation, and mobile augmented reality (AR). It discusses using mobile devices equipped with inertial measurement units (IMU) and multi-touch screens to provide user feedback to correct positioning errors. The framework also uses mobile AR through device cameras to help navigate users in complex 3D indoor environments and provide interactive location-based services. A prototype system was developed to demonstrate the feasibility of the proposed application framework.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract Robots are expected to be new tools for the operations and observations in the extreme environments where humans have difficulties in direct access. One of the important matters to realize mobile robots for extreme environments is to establish systems in their structures which are strong enough to disturbances. Also, while considering surveillance in inaccessible remote areas, a need arises for the presence of a robot capable of intruding into small crevices as well as provides proper surveillance. This work aims at the implementation of a snake robot for surveillance operations in remote areas. A biologically inspired robot with various motion patterns is taken into consideration. An important problem in the control of locomotion of robots with multiple degrees of freedom is in adapting the locomotors patterns to the properties of the environment. This has been overcome by using control techniques capable of integrating the motion patterns of a snake. Here an attempt is taken to focus on the creeping locomotion of a living snake. In hybrid model, the optimal locomotion of the snake robot is tried to achieve by comparing it with that of a living snake. A wireless real time vision processing is also employed within the robot to improve its performance. The presence of Video acquisition along with processing will be an added advantage for implementation of the robot for highly precise and difficult surveillance applications. Real time processing of video enables proper and efficient control towards obstacle avoidance pattern of the robot. This ensures that the locomotion of the robot is in a bio-inspired highly efficient path towards the target. Keywords: Collision-free behavior, neural oscillator, snake locomotion, steering, real time vision processing
This document discusses various machine vision techniques used to estimate the self-position of mobile robots in industries. It describes techniques such as GPS, vision-based localization using cameras and image processing, 2D and 3D map-based localization, and memory-based localization using image autocorrelation. Vision-based techniques analyze camera images to detect landmarks, match detected landmarks to a database, and calculate the robot's position. Map-based localization uses 2D overhead maps captured by laser sensors or 3D models to localize the robot. Memory-based localization generates unique autocorrelation images from camera views and matches them to stored images to estimate the position.
Integral Backstepping Approach for Mobile Robot ControlTELKOMNIKA JOURNAL
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IRJET- Simultaneous Localization and Mapping for Automatic Chair Re-Arran...IRJET Journal
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This document describes the development of an autonomous mobile robot for wall following using a fuzzy incremental controller. Two ultrasonic sensors are used to sense the distance to the wall and provide input to the controller. The controller determines the speed of two DC motors to guide the robot along the wall. Experimental results showed the fuzzy controller successfully controlled the robot to follow the wall, performing better than a PID controller. The robot is intended for applications like cleaning air ducts or corridors by autonomously navigating while maintaining a set distance from the wall.
Wall follower autonomous robot development applying fuzzy incremental controllerrajabco
This paper presents the design of an autonomous robot as a basic development of an intelligent wheeled mobile robot for air duct or corridor cleaning. The robot navigation is based on wall following algorithm. The robot is controlled us- ing fuzzy incremental controller (FIC) and embedded in PIC18F4550 microcontroller. FIC guides the robot to move along a wall in a desired direction by maintaining a constant distance to the wall. Two ultrasonic sensors are installed in the left side of the robot to sense the wall distance. The signals from these sensors are fed to FIC that then used to de- termine the speed control of two DC motors. The robot movement is obtained through differentiating the speed of these two motors. The experimental results show that FIC is successfully controlling the robot to follow the wall as a guid- ance line and has good performance compare with PID controller.
A Simple Integrative Solution For Simultaneous Localization And MappingWaqas Tariq
Simultaneous Localization and Mapping is a method used to find the location of a mobile robot while at the same time build a constructive map of its surrounding environment. This paper gives a brief description about a simple integrative SLAM technique using a Laser Range Finder (LRF) and Odometry data, primarily for indoor environments. In this project, a solution for the SLAM problem was implemented on a differential drive mobile robot equipped with a SICK laser scanner.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
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Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
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Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
2. FUZZY LOGIC IN
ROBOT NAVIGATION
COURSE TITLE: - MAL-215
FUZZY LOGIC AND ITS APPLICATIONS
SUPERVISED BY: - ARVIND KUMAR GUPTA
1
3. We would also wish to express our sincere thanks to the Director of college Prof. M.K.
Surappa for providing us with all the necessary facilities.
I place on record, my sincere gratitude to Dr. Harpreet Singh, Coordinator of
Department of Mechanical Engineering, for his constant encouragement.
We would like to pledge our gratitude and deep obligation towards Professor Dr.
Arvind Kumar Gupta of Mathematics Department for his expert, sincere and valuable
guidance and encouragement extended to us.
We are indebted to our teachers of Mathematics Department at INDIAN INSTITUTE
OF TECHNOLOGY ROPAR, who have helped, inspired and given a moral support and
encouragement in various ways, in completing this project. We are pleased to
acknowledge the helpful comments and suggestions provided by the subject faculty.
Then we are also thankful to our parents for their moral support and giving inspiration to
achieve this task.
Furthermore we would like to extend our special thanks to one and all who directly or
indirectly were helpful in the success of our report.
AASHISH KHOLIYA
ROHAN MISHRA
2
4. TABLE OF CONTENTS
1) ABSTRACT ………………………………………………………………………………………………….4
2) INTRODUCTION ………………………………………………………………………………………5-7
3) ULTRASONIC SENSORS …………………………………………………………...................8-9
4) FUZZY LOGIC NAVIGATION SCHEME ……………………………………….………………..10
5) DESCRIPTION OF REACTIVE BEHAVIOURS USING FUZZY LOGIC ……………11-13
a) Obstacle Avoidance and Decelerating at Curved and
Narrow Roads ……………………………………………………………………………………..11
b) Following Edge ……………………………………………………………………………………12
c) Target Steer ………………………………………………………………………………………..12
6) MULTIPLE BEHAVIOURS FUSION BY FUZZY REASONING ………………………14-16
7) SIMULATIONS OF ROBOT NAVIGATION USING
ULTRASONIC SENSORS ………………………………………………………………………..17-20
a) Moving To A Target Inside A U-Shaped Object …………………………………….17
b) Moving in a Cluttered Environment …………………………………………………….18
c) Following Wall Edges …………………………………………………………………………..19
d) Decelerating at Curved and Narrow Roads ………………………………………….20
8) VISION SYSTEM …………………………………………………………………………………..21-23
9) CONCLUSION ……………………………………………………………………………………………24
10)
REFERENCES ……….………………………………………………………………………….25-26
3
5. ABSTRACT
This paper presents a strategy for fuzzy logic based robot navigation in uncertain
environments by multisensory integration. The main idea of the study is to coordinate
conflicts and competitions among multiple reactive behaviors efficiently by fuzzy sets and a
rule base. To achieve this objective, an array of ultrasonic sensors and a vision system are
mounted on a mobile robot. The ultrasonic sensors provide distance information between
the robot and obstacles for behavior control of the mobile robot, while the vision system
identifies some subgoals for determining a good motion direction to avoid robot trap in local
region.
The simulation results show that the proposed strategy, by integrating ultrasonic sensors and
the vision system, can be efficiently applied to robot navigation in complex and uncertain
environments by using different behaviors, such as avoiding obstacles, decelerating at curved
and narrow roads, escaping from a U-shaped object, and moving to target and so on.
4
6. INTRODUCTION
If a mobile robot moves in unknown environments to reach a specified target without
collisions with obstacles, sensors must be used to acquire information about the real world.
Using such information, it is very difficult to build a precise world model in real-time for
preplanning a collision-free path. On the basis of situational reactive behaviours, behaviour
based control [1] [2] [3] has been proposed for robot navigation. Since this method does not
need building an entire world model and complex reasoning process, it is suitable for robot
control in dynamic environments.
A key issue in behaviour based control is how to coordinate conflicts and competitions among
multiple reactive behaviours efficiently. The example in Fig.1 shows that the robot must
efficiently weight multiple reactive behaviours, such as avoiding obstacle, following edge, and
moving to target and so on, according to range information, when it reaches a target inside a
U-shaped object. The usual approach for implementing behaviour control is artificial potential
fields [4][5][6].
A drawback to this approach is that during preprograming much effort must be made to test
and to adjust some thresholds regarding potential fields for avoiding obstacle, wandering,
and moving to target and so on. In particular, these thresholds frequently depend on
Environments. In [7] [8], we present an approach for fuzzy logic based behaviour control of a
mobile robot. Unlike behaviour control based
on artificial potential fields, this method is to compute weights of multiple reactive behaviours
in dynamic environments by a fuzzy logic
algorithm rather than simply to inhibit some reactive behaviours with lower levels. In this
paper, we further present a strategy for fuzzy logic based behaviour control of a mobile robot
by multi sensor integration.
To achieve this objective, an array of ultrasonic sensors and a vision system are mounted on
a mobile robot. The ultrasonic sensors provide distance information between the robot and
obstacles for robot navigation by reactive behaviours, such as avoiding obstacles and
following edges, while the vision system identifies some sub goals for determining a good
motion direction to avoid robot trap in local region. This method differs from the fuzzy control
approaches for obstacle avoidance in [9] [10] [11]. Since perception and decision units in this
method are integrated in one module by the use of the idea of reactive behaviours and are
5
7. directly oriented to a dynamic environment, this strategy has the better real-time response
and reliability.
To demonstrate the effectiveness and the robustness of the proposed strategy, we report a
lot of simulation results on robot navigation in uncertain environments, such as avoiding
obstacle in real-time, decelerating at curved and narrow roads, escaping from a U shaped
object and moving to target and so on.
6
8. Fig: this graph shows the membership grades for the
Fuzzy sets distance and direction
7
9. ULTRASONIC SENSORS
In order to acquire information about dynamic environments, 15 ultrasonic sensors are
mounted on the THMR-I1 mobile robot [12], as shown in Fig.2. The sonar reflection from a
sensor i represents the distance di, measured by the sensor i, between the robot and
obstacles in the real world.
These ultrasonic sensors are divided into three groups to detect obstacles to the right ( sensor
i = 1, ..., 6 ), front ( sensor i = 7, ..., 9 ), and left locations ( sensor i = 10, ..., 15 ). Using such
information, obviously, it is difficult to build a precise and entire world model in real-time for
preplanning a collision-free path. Here, we use the sonar data di (i = 1, ..., 15) to build a simple
model for representation of the distances between the robot and obstacles in the real world
as follows:
Right- obs = Min {di} i = 1, ..., 6
(1)
Front- obs = Min {di} i = 7, ..., 9
(2)
Left-obs = Min {di) i = 10,…., 15 (3)
Where the minimum values, right-obs, front-obs, and left-obs, derived from the sensor data
di (i = 1, ..., 15), express the distances between the robot and obstacles to the right, front, and
left locations, respectively. The mobile robot is equipped with two wheel encode units to
determine its current coordinates.
At a start position, a counter is reset to zero. When the robot moves, its current coordinates
can be roughly computed by counting the numbers of pulses from the wheel encodes that
are attached on driving motors.
The THMR-I1 mobile robot with 1.0m length and 0.8m width is equipped with two driving
wheels and one driven wheel. The velocities of the driving wheels are controlled by a motor
drive unit.
8
11. FUZZY LOGIC NAVIGATION SCHEME
The input signals to fuzzy logic scheme are the distances between the robot and obstacles to
the left, front, and right locations as well as the heading angle between the robot and a
specified target, denoted by left-obs, front-obs, right-obs and head-ang, respectively, as
shown in Fig.3a. When the target is located to the left side of the mobile robot, a heading
angle head-ang is defined as negative; while the target is located to the right side of the
mobile robot, a heading angle head-ang is defined as positive, as shown in Fig.3b.
According to acquired range information, reactive behaviours are weighted by the fuzzy logic
algorithm to control the velocities of the two driving wheels of the robot, denoted by left-v
and right-v, respectively. The linguistic variables far, med (medium) and near are chosen to
fuzzify left-obs, Front-obs and right-obs. The linguistic variables P (positive), 2 (zero) and N
(negative) are used to fuzzify head-ang; the linguistic variables fast, med, and slow are used
to fuzz@ the velocities of the driving wheels left-v and right-v.
In analogy to artificial potential fields, the distances between the robot and obstacles serve
as a repulsive force for avoiding obstacle, while the heading angle serves as an attractive force
for moving to target.
Fig: describing the various behaviours for robot
10
12. Description Of Reactive Behaviours Using Fuzzy Logic
In order to reach a specified target in a complex environment, the mobile robot at least needs
the following reactive behaviours:
1. Obstacle avoidance and decelerating at curved and narrow roads;
2. Following edges;
3. Target steer.
Because the real world is a complex, using sensors it is very difficult to acquire precise
information about dynamic environments. In this case, a set of fuzzy logic rules is used to
describe the reactive behaviours mentioned above [13] [14]. Now, we only list parts of fuzzy
rules from the rule base to explain, in principle, how these reactive behaviours are realized.
(In fact, much more fuzzy rules have been used in our navigation algorithms)
A. Obstacle Avoidance and Decelerating at Curved and Narrow Roads
When the acquired information from the ultrasonic sensors shows that there exist obstacles
nearby robot or the robot moves at curved and narrow roads, it must reduce its speed to
avoid obstacles. In this case, its main reactive behaviour is decelerating for obstacle
avoidance. We give the first and second of fuzzy rules for realizing this behaviour as follows:
If
(left-obs is near and front-obs is near and
right-obs is near and head-ang is any)
Then (left-v is fast and right-v is slow).
If
(left-obs is med and front-obs is near and
right-obs is near and head-ang is any)
Then (left-v is slow and right-v is fast).
Such fuzzy rules represent that the robot only pays attention to obstacle avoidance and
moves slowly when it is very close to obstacles or at curved and narrow roads.
11
13. B. Following Edge
When the robot is moving to a specified target inside a room (Fig.1), it must reflect following
edge behaviour. The first and second rules for describing this behaviour are listed as follows:
If
(left-obs is far and front-obs is far and
right-obs is near and head-ang is P)
Then (left-v is med and right-v is med).
If
(left-obs is near and front-obs is far and
right-obs is far and head-ang is N)
Then (left-v is med and right-v is med).
These fuzzy rules show that the robot shall follow an edge of an obstacle when the obstacle
is very close to the left (or the right) of the robot, and also the target is located to the left (or
the right).
C. Target Steer
When the acquired information from the ultrasonic sensors shows that there are no obstacles
around robot, its main reactive behaviour is target steer. Here, we list the first and second of
fuzzy rules for realizing this behaviour as follows:
I f (left-obs is far and front-obs is far and
right-obs is far and head-ang is Z)
Then (left-v is fast and right-v is fast).
I f (left-obs is far and front-obs is far and
right-obs is far and head-ang is N)
Then (left-v is slow and right-v is fast).
These fuzzy logic rules show that the robot mainly adjusts its motion direction and quickly
moves to the target if there are no obstacles around the robot.
12
14. Fig: table showing variation in speed with distance
Fig: graph representing the membership grade
Of distance as measured by sensor
13
15. Multiple Behaviours Fusion By Fuzzy Reasoning
A key issue of behaviour-based control is how to efficiently coordinate conflicts and
competitions among different reactive behaviours to achieve a good performance. In [1], a
priority strategy is used to activate a reactive behaviour according to its urgency level. This
strategy is highly contentious for robot navigation in complex environments.
For example, it is difficult to determine exactly which one of the reactive behaviours, obstacle
avoidance, or following edges, or target steer, should be fired when the robot moves through
the entrance of the U-shaped object to a target, as shown in Fig. 1. To reach the given target,
in fact, all the three reactive behaviours must be efficiently integrated. The following are some
deficiencies of the priority strategy noted in our experiments:
1. Much effort must be made to test and to adjust some thresholds
or firing reactive behaviours during pre-programming.
2. These thresholds depend heavily on environments, i.e., a set of
thresholds, determined in a given environment, may not be
suitable for other environments.
3. Robot motion with unstable oscillations between different
behaviours may occur in some cases. This is because just only one
behaviour could be activated at a given instant and two behaviours
with neighbouring priority, e.g., obstacle avoidance and target
steer, are fired in turn.
In the proposed control strategy, reactive behaviours are formulated by fuzzy sets and fuzzy
rules, and these fuzzy rules are integrated in one rule base. The coordination of different
reactive behaviours can thus be easily performed by fuzzy reasoning.
The following is an illustration of how this problem is dealt with by the Min-Max inference
algorithm and the centroid defuzzification method in Eq.(1).
For instance, the inputs, left-obs=d1, front-obs=d2, right-obs=d3, head-ang=Ɵ1, are fuzzified
by their membership functions to fire fuzzy rules associated with them simultaneously.
Assume that Rule i (see below), formulating the obstacle avoidance behaviour, and Rule j (see
below), formulating the following edge behaviour, are fired according to the fuzzified inputs
(in fact, much more fuzzy rules may be activated):
Rule i: I f (left-obs is near and front-obs is near
and right-obs is near and head-ang is N)
Then (left-v is fast and right-v is slow).
14
16. Rule j: I f (left-obs is near and front-obs is med
and right-obs is med and head-ang is N)
Then (left-v is med and right-v is med).
By fuzzy reasoning and the centroid defuzzification method, both Rule i and Rule j , related to
the obstacle avoidance and following edge behaviours respectively, are weighted to
determine an appropriate control action, i.e., the velocities, left-v and right-v, of the robot's
rear wheels, as shown Fig.4.
15
18. Simulations of Robot Navigation Using
Ultrasonic Sensors
In this section we report several simulation results on robot navigation, only using ultrasonic
sensors, in different environments.
A. Moving To A Target Inside A U-Shaped Object
Fig.1 illustrates robot motion to a target inside a U-shaped object. At start stage, the robot
moves to the target with a high speed since the moving to target behaviour is strong due to
the large free space around the robot.
When the robot approaches to the U-shaped object, it is decelerating by automatically
reducing the weight of moving to target Behaviour and increasing the weight of avoiding
obstacle and following edge behaviours. When the robot finds out the entry of the U-shaped
object, it slowly reaches the target by reasonably integrating avoiding obstacle and moving to
target behaviours.
17
19. B. Moving in a Cluttered Environment
Fig.5a-b shows robot motion in a cluttered environment. We choose at random several
targets that are located among different obstacle distribution.
Path 1 in Fig.5b represents robot motion from the start position to target 1 located in a narrow
road;
Path 2 in Fig.5b represents robot motion from target 1 to target 2 that is behind more
obstacles; and
Path 3 represents robot motion from target 2 to target 3 that is placed in the region where
start position is located. It can be observed that, only using ultrasonic sensors to acquire
information about environments, the robot can successfully reach all targets by reasonably
weighting more reactive behaviours using the proposed fuzzy logic navigation algorithm. Fig.
18
20. C. Following Wall Edges
In some applications, a mobile robot should be able to move from a room to another room.
Fig.6 shows that a start position and a target position are located in different rooms. Using
the fuzzy navigation algorithm, the robot can automatically act following edge behaviour (in
our algorithm the right-oriented principle is implemented) as so to reach the target when it
"hits" the wall.
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21. D. Decelerating at Curved and Narrow
Roads
When the mobile robot operates in outdoor environments, it should be able to tack roads to
reach a target. The example in Fig.7 shows robot navigation at curved and narrow roads. The
robot begins from its start position and is automatically decelerating at the first curved road
with 900. Then it moves into a very narrow road with a slow speed. At the following curved
roads with go", the robot automatically makes turns to keep on the roads.
Finally, the robot gets the road where the target is located and move to the target with
obstacle avoidance, using local information acquired by ultrasonic sensor and the heading
angle between the robot and the target.
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22. Vision System
The simulation results show that the proposed method, only using ultrasonic sensors, can
perform robot navigation in complex and uncertain environments by weighting multiple
reactive behaviours, such as avoiding obstacles, decelerating at curved and narrow roads, and
moving to target and so on.
However, only ultrasonic sensors do not guarantee to provide a good path for robot
navigation (in Fig.5b) in some case since complete information on environments is not
available. Here, a vision system is used to improve navigation performance. This vision system
consists of a TV camera and an image processing unit [10]. This unit analyses the image data
to recognize the distribution of obstacles in local region. According to information on the
obstacles' distribution, the robot identifies some sub goals for determining a good motion
direction to avoid robot trap in local region.
Fig.8a shows robot motion from a start position to target position by following right edge
behaviour. A trap motion occurs during robot navigation due to a U-shaped object. To avoid
the trap motion, the vision system identifies a sub goal to determine a good motion direction,
as shown in Fig.8b.
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23. Experiment:
Fig: the arena for testing robot motion
The robot is marked with two colours and the target is the red spot.
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24. Fig: Showing the path taken by the robot during motion
Fig: max. probability graph for the motion of robot
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25. CONCLUSION
In this project, we use fuzzy logic to realize the reactive behaviours for robot navigation. The
method can effectively coordinate conflicts and competitions among multiple reactive
behaviours by weighting them and this coordination ability is nearly independent of a
dynamic environment due to it robustness.
The navigation algorithm has better reliability and real-time response since perception and
decision units in the algorithm are integrated in one module and are directly oriented to a
dynamic environment. The simulation results show that the proposed method for robot
navigation by multi sensor integration can automatically perform avoiding obstacles,
decelerating at curved and narrow roads, escaping from a U-shaped object, and moving to
target and so on in complex and uncertain environments.
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26. REFERENCES
[1] R.A. Brooks, "A robust layered control system for a mobile robot" IEEE J. of Robotics and
Automation, RA pp. 14-23, April 1986.
[2] Ronald C. Arkin, and Robin R. Murphy, "Autonomous navigation in a manufacturing
environment", IEEE Tran. On Robotics and Automation, vo1.6, no.4, pp.445-454, 1990.
[3] M.D. Adams, Housheng Hu and P.J. Probort, "Towards areal-time architecture for obstacle
avoidance and path planning in mobile robot", Proc. IEEE Int. Con$ on Robotics and
automation, pp.584-589, March 1990.
[4] B.H. Krogh, "A generalized potential field approach to obstacle avoidance control", SMERI Technical Paper 2, MS84-484, 1984.
[5] 0. Khatib, "Real-time obstacle avoidance for manipulators and automobile robots", Int. J.
of Robotics Research, vo1.5, no.1, 1986.
[6] Xun Feng, "Potential field based behaviour control of mobile robot", Technical Report,
Department of Computer Science, Tsinghua University, 1993, unpublished.
[7] Wei Li: "Fuzzy logic based 'perception action' behaviour control of an mobile robot in
uncertain environments" IEEE World Congress on Computational Intelligence, in press, 1994.
[8] Wei Li: "perception action behaviour control of a mobile robot in uncertain environments
using fuzzy logic". IEEELRSI International Conference on Intelligent Robots and Systems,
in press, 1994.
[9] M. Sugeno and M. Nishida, "Fuzzy control of model car", Fuzzy Sets and Systems, vo1.16,
pp.103-113, 1985.
[10] T. Takeuchi; Y. Nagai and N. Enomoto, "Fuzzy control of a mobile robot for obstacle
avoidance", Information Science, vo1.45, pp.231-248, 1988.
[11] M. Maeda; Y.Maeda and S. Murakami, "Fuzzy drive control of an autonomous mobile
robot", Fuzzy Sets and Systems, vo1.39, pp.195-204, 1991.
[12] Wei Li and Kezhong He: "Sensor-based robot navigation in uncertain environments using
fuzzy controller" The 1994 ASME International Computers in Engineering Conference, in
press, 1994.
25
27. [13] Wei Li: "Fuzzy logic based reactive behaviour control of an autonomous mobile system
in unknown environments" International Journal of Engineering Application of Artificial
Intelligence, Pergamon Press, in press, 1994.
[14] Wei Li and X. Feng.: "Behaviour fusion for robot navigation in uncertain environments
using fuzzy logic", The 1994 IEEE International Conference on Systems, Man and
Cybernetics, in press, 1994.
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