The document describes an analytical approach for obstacle avoidance in mobile robot navigation. A nonlinear supervised controller is proposed to reactively guide a robot to avoid obstacles while seeking a goal. The controller changes the robot's orientation to align with the tangent of any nearby obstacles, then follows the obstacle boundary to find a path to the goal. Simulations and experiments validate that the controller enables a robot to reach its goal after avoiding all obstacles. The controller is implemented on a real mobile robot equipped with a laser range scanner.
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.
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 document summarizes a research paper that proposes a method for motion detection in non-stationary backgrounds using ORB (Oriented FAST and Rotated BRIEF) feature matching and affine transformations for video surveillance systems. ORB is used to extract features from video frames in a fast and efficient manner. Mismatched feature pairs between frames are rejected to improve accuracy. Morphological operations are used to remove residues and ghosts. The proposed ORB feature matching method is compared to traditional frame differencing methods to evaluate accuracy and efficiency for moving object detection in videos with moving backgrounds.
EFFECTIVE REDIRECTING OF THE MOBILE ROBOT IN A MESSED ENVIRONMENT BASED ON TH...Wireilla
The use of fuzzy logic in redirecting mobile robot is based on two sets of received information. First set is
the instantaneous distance of the robot from the obstacle and second set is the instantaneous information of
the robot's position. For this purpose, the fuzzy rules base consists of forty-two bases, which is extracted
based on the robot's distance from obstacles, and the target position relative to the instantaneous
orientation of the robot. In the structure of fuzzy systems, minimal inference engine are considered. Also,
Extended Kalman filter is used for localization in a noisy environment. Accordingly, the inputs of the fuzzy
systems are determined based on the estimation of the localization process, the information of the obstacles
center and the target position. Also, the linear acceleration and instantaneous orientation of the mobile
robot are determined by the desired fuzzy structures which are applied to its kinematic model.
EFFECTIVE REDIRECTING OF THE MOBILE ROBOT IN A MESSED ENVIRONMENT BASED ON TH...ijfls
The use of fuzzy logic in redirecting mobile robot is based on two sets of received information. First set is the instantaneous distance of the robot from the obstacle and second set is the instantaneous information of the robot's position. For this purpose, the fuzzy rules base consists of forty-two bases, which is extracted based on the robot's distance from obstacles, and the target position relative to the instantaneous orientation of the robot. In the structure of fuzzy systems, minimal inference engine are considered. Also, Extended Kalman filter is used for localization in a noisy environment. Accordingly, the inputs of the fuzzy systems are determined based on the estimation of the localization process, the information of the obstacles center and the target position. Also, the linear acceleration and instantaneous orientation of the mobile robot are determined by the desired fuzzy structures which are applied to its kinematic model.
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.
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.
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.
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.
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 document summarizes a research paper that proposes a method for motion detection in non-stationary backgrounds using ORB (Oriented FAST and Rotated BRIEF) feature matching and affine transformations for video surveillance systems. ORB is used to extract features from video frames in a fast and efficient manner. Mismatched feature pairs between frames are rejected to improve accuracy. Morphological operations are used to remove residues and ghosts. The proposed ORB feature matching method is compared to traditional frame differencing methods to evaluate accuracy and efficiency for moving object detection in videos with moving backgrounds.
EFFECTIVE REDIRECTING OF THE MOBILE ROBOT IN A MESSED ENVIRONMENT BASED ON TH...Wireilla
The use of fuzzy logic in redirecting mobile robot is based on two sets of received information. First set is
the instantaneous distance of the robot from the obstacle and second set is the instantaneous information of
the robot's position. For this purpose, the fuzzy rules base consists of forty-two bases, which is extracted
based on the robot's distance from obstacles, and the target position relative to the instantaneous
orientation of the robot. In the structure of fuzzy systems, minimal inference engine are considered. Also,
Extended Kalman filter is used for localization in a noisy environment. Accordingly, the inputs of the fuzzy
systems are determined based on the estimation of the localization process, the information of the obstacles
center and the target position. Also, the linear acceleration and instantaneous orientation of the mobile
robot are determined by the desired fuzzy structures which are applied to its kinematic model.
EFFECTIVE REDIRECTING OF THE MOBILE ROBOT IN A MESSED ENVIRONMENT BASED ON TH...ijfls
The use of fuzzy logic in redirecting mobile robot is based on two sets of received information. First set is the instantaneous distance of the robot from the obstacle and second set is the instantaneous information of the robot's position. For this purpose, the fuzzy rules base consists of forty-two bases, which is extracted based on the robot's distance from obstacles, and the target position relative to the instantaneous orientation of the robot. In the structure of fuzzy systems, minimal inference engine are considered. Also, Extended Kalman filter is used for localization in a noisy environment. Accordingly, the inputs of the fuzzy systems are determined based on the estimation of the localization process, the information of the obstacles center and the target position. Also, the linear acceleration and instantaneous orientation of the mobile robot are determined by the desired fuzzy structures which are applied to its kinematic model.
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.
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.
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.
This document summarizes research on hydrographene, a material that is intermediate between graphene and graphane. Hydrographene is obtained by partially hydrogenating graphene. The document proposes modeling hydrographene using percolation theory, where hydrogenated carbon sites are removed from the honeycomb lattice. This predicts a phase transition from graphene to graphane at a critical hydrogenation density. It also predicts hydrographene will be ferromagnetic based on its carbon network structure. Two types of hydrogenation are considered: single-sided, where only one sublattice is hydrogenated, and double-sided, where both sublattices are hydrogenated. Single-sided hydrogenation is found to produce larger magnetic moments. The percolation model
Dokumen tersebut membahas dampak teknologi informasi dan komunikasi (TIK) terhadap kehidupan manusia, sumber daya alam, dan sumber daya manusia. Secara umum teknologi dapat memudahkan dan meningkatkan kinerja manusia, meski juga dapat menimbulkan masalah seperti pencemaran dan pengangguran. Oleh karena itu diperlukan pengawasan dan regulasi yang tepat dalam pengembangan teknologi.
This document summarizes a study on the electrical properties of electrodeposited zinc-copper-telluride (ZnCuTe) ternary nanowires embedded in polycarbonate membranes. Scanning electron microscopy confirmed the formation of uniform diameter nanowires equal to the pore diameters of 200nm, 100nm, and 50nm templates used. Electrical measurements found the nanowires exhibited linear and ohmic characteristics. Larger diameter nanowires showed higher electron transport than smaller ones. Temperature-dependent measurements from 308K-423K revealed electrical conductivity increased with temperature and decreased with smaller nanowire size, with ZnCuTe nanowires exhibiting negative temperature coefficients of resistance.
Lapisan ozon berperan melindungi bumi dari sinar ultraviolet dan menentukan suhu bumi. Kerusakan lapisan ozon disebabkan oleh zat-zat seperti CFC yang melemahkan lapisan ozon, meningkatkan radiasi ultraviolet yang dapat menyebabkan berbagai penyakit dan gangguan ekosistem. Upaya pengendalian kerusakan lapisan ozon meliputi mengurangi penggunaan produk yang mengandung zat perusak ozon dan mengganti dengan alternatif ra
Journal Review: The Health Effects of Climate Change: A Survey of Recent Quan...Dadang Setiawan
Dokumen ini merupakan survei terbaru tentang penelitian kuantitatif yang meneliti dampak perubahan iklim terhadap kesehatan. Tujuannya adalah untuk meninjau kontribusi penelitian terkait dampak perubahan iklim pada penyebaran penyakit menular dengan menggunakan berbagai model seperti model time series, panel data, dan pendekatan non-statistik. Penelitian ini menggunakan kasus malaria di Afrika untuk menganalisis hubungan antara variabel
Jurnal Internasional – Dampak Energi Terbarukan Terhadap Ketenagakerjaan di I...Dani Gunawan
Sebuah permintaan global untuk energi telah memaksa banyak negara untuk mencari energi alternatif dan terbarukan . Efek diantisipasi pengembangan terbarukan adalah peningkatan lapangan kerja sebagai bagian dari penciptaan lapangan pekerjaan hijau baru , manfaat besar bagi Indonesia untuk mengatasi tingkat pengangguran yang tinggi . Makalah ini menjelaskan dampak pengembangan energi terbarukan pada penciptaan lapangan kerja di Indonesia .
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
This document summarizes a study of CEO succession events among the largest 100 U.S. corporations between 2005-2015. The study analyzed executives who were passed over for the CEO role ("succession losers") and their subsequent careers. It found that 74% of passed over executives left their companies, with 30% eventually becoming CEOs elsewhere. However, companies led by succession losers saw average stock price declines of 13% over 3 years, compared to gains for companies whose CEO selections remained unchanged. The findings suggest that boards generally identify the most qualified CEO candidates, though differences between internal and external hires complicate comparisons.
A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...CSCJournals
This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.
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.
Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...Waqas Tariq
For navigation in complex environments, a robot needs to reach a compromise between the need for having efficient and optimized trajectories and the need for reacting to unexpected events. This paper presents a new sensor-based Path Planner which results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Voronoi Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted areas in order to obtain the path. The method combines map-based and sensor-based planning operations to provide a reliable motion plan, while it operates at the sensor frequency. The main characteristics are speed and reliability, since the map dimensions are reduced to an almost unidimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments where other methods of planning based on Voronoi do not work.
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.
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.
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.
Motion planning and controlling algorithm for grasping and manipulating movin...ijscai
Many of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving
objects, researchers have been using real time captured images to locate objects dynamically. However,
this approach of controlling the grasping process is quite costly, implying a lot of resources and image
processing.Therefore, it is indispensable to seek other method of simpler handling… In this paper, we are
going to detail the requirements to manipulate a humanoid robot arm with 7 degree-of-freedom to grasp
and handle any moving objects in the 3-D environment in presence or not of obstacles and without using
the cameras. We use the OpenRAVE simulation environment, as well as, a robot arm instrumented with the
Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an
extent of RRT-JT that combines exploration, using a Rapidly-exploring Random Tree, with exploitation,
using Jacobian-based gradient descent, to instruct a 7-DoF WAM robotic arm, in order to grasp a moving
target, while avoiding possible encountered obstacles . We present a simulation of a scenario that starts
with tracking a moving mug then grasping it and finally placing the mug in a determined position, assuring
a maximum rate of success in a reasonable time.
Motion Planning and Controlling Algorithm for Grasping and Manipulating Movin...ijscai
Many of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving
objects, researchers have been using real time captured images to locate objects dynamically. However,
this approach of controlling the grasping process is quite costly, implying a lot of resources and image
processing.Therefore, it is indispensable to seek other method of simpler handling… In this paper, we are
going to detail the requirements to manipulate a humanoid robot arm with 7 degree-of-freedom to grasp
and handle any moving objects in the 3-D environment in presence or not of obstacles and without using
the cameras. We use the OpenRAVE simulation environment, as well as, a robot arm instrumented with the
Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an
extent of RRT-JT that combines exploration, using a Rapidly-exploring Random Tree, with exploitation,
using Jacobian-based gradient descent, to instruct a 7-DoF WAM robotic arm, in order to grasp a moving
target, while avoiding possible encountered obstacles . We present a simulation of a scenario that starts
with tracking a moving mug then grasping it and finally placing the mug in a determined position, assuring
a maximum rate of success in a reasonable time.
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.
Improving Posture Accuracy of Non-Holonomic Mobile Robot System with Variable...TELKOMNIKA JOURNAL
This paper presents a method to decrease imprecision and inaccuracy that have the tendency to
influence the posture of non-holonomic mobile robot by using the adaptive tuning of universe of discourse.
As such, the primary objective of the study is to force the posture error of , , and towards
zero. Hence, for each step of tuning the fuzzy domain, about 20% of imprecision and inaccuracy had been
added automatically into the variable universe fuzzy, while the control input was bound via scaling gain.
Furthermore, the simulation results showed that the tuning of universe fuzzy parameters could increase
the performance of the system from the aspects of response time and error for steady state through better
control of inaccuracy. Besides, the domains of universe fuzzy input [-4,4] and output [0,6] exhibited good
performance in inching towards zero values as the steady state error was about 1% for x(t) position, 0.02%
for y(t) position, and 0.16% for θ(t) orientation, whereas the posture error in the given reference was about
0.0002% .
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
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.
This document summarizes research on hydrographene, a material that is intermediate between graphene and graphane. Hydrographene is obtained by partially hydrogenating graphene. The document proposes modeling hydrographene using percolation theory, where hydrogenated carbon sites are removed from the honeycomb lattice. This predicts a phase transition from graphene to graphane at a critical hydrogenation density. It also predicts hydrographene will be ferromagnetic based on its carbon network structure. Two types of hydrogenation are considered: single-sided, where only one sublattice is hydrogenated, and double-sided, where both sublattices are hydrogenated. Single-sided hydrogenation is found to produce larger magnetic moments. The percolation model
Dokumen tersebut membahas dampak teknologi informasi dan komunikasi (TIK) terhadap kehidupan manusia, sumber daya alam, dan sumber daya manusia. Secara umum teknologi dapat memudahkan dan meningkatkan kinerja manusia, meski juga dapat menimbulkan masalah seperti pencemaran dan pengangguran. Oleh karena itu diperlukan pengawasan dan regulasi yang tepat dalam pengembangan teknologi.
This document summarizes a study on the electrical properties of electrodeposited zinc-copper-telluride (ZnCuTe) ternary nanowires embedded in polycarbonate membranes. Scanning electron microscopy confirmed the formation of uniform diameter nanowires equal to the pore diameters of 200nm, 100nm, and 50nm templates used. Electrical measurements found the nanowires exhibited linear and ohmic characteristics. Larger diameter nanowires showed higher electron transport than smaller ones. Temperature-dependent measurements from 308K-423K revealed electrical conductivity increased with temperature and decreased with smaller nanowire size, with ZnCuTe nanowires exhibiting negative temperature coefficients of resistance.
Lapisan ozon berperan melindungi bumi dari sinar ultraviolet dan menentukan suhu bumi. Kerusakan lapisan ozon disebabkan oleh zat-zat seperti CFC yang melemahkan lapisan ozon, meningkatkan radiasi ultraviolet yang dapat menyebabkan berbagai penyakit dan gangguan ekosistem. Upaya pengendalian kerusakan lapisan ozon meliputi mengurangi penggunaan produk yang mengandung zat perusak ozon dan mengganti dengan alternatif ra
Journal Review: The Health Effects of Climate Change: A Survey of Recent Quan...Dadang Setiawan
Dokumen ini merupakan survei terbaru tentang penelitian kuantitatif yang meneliti dampak perubahan iklim terhadap kesehatan. Tujuannya adalah untuk meninjau kontribusi penelitian terkait dampak perubahan iklim pada penyebaran penyakit menular dengan menggunakan berbagai model seperti model time series, panel data, dan pendekatan non-statistik. Penelitian ini menggunakan kasus malaria di Afrika untuk menganalisis hubungan antara variabel
Jurnal Internasional – Dampak Energi Terbarukan Terhadap Ketenagakerjaan di I...Dani Gunawan
Sebuah permintaan global untuk energi telah memaksa banyak negara untuk mencari energi alternatif dan terbarukan . Efek diantisipasi pengembangan terbarukan adalah peningkatan lapangan kerja sebagai bagian dari penciptaan lapangan pekerjaan hijau baru , manfaat besar bagi Indonesia untuk mengatasi tingkat pengangguran yang tinggi . Makalah ini menjelaskan dampak pengembangan energi terbarukan pada penciptaan lapangan kerja di Indonesia .
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
This document summarizes a study of CEO succession events among the largest 100 U.S. corporations between 2005-2015. The study analyzed executives who were passed over for the CEO role ("succession losers") and their subsequent careers. It found that 74% of passed over executives left their companies, with 30% eventually becoming CEOs elsewhere. However, companies led by succession losers saw average stock price declines of 13% over 3 years, compared to gains for companies whose CEO selections remained unchanged. The findings suggest that boards generally identify the most qualified CEO candidates, though differences between internal and external hires complicate comparisons.
A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...CSCJournals
This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.
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.
Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...Waqas Tariq
For navigation in complex environments, a robot needs to reach a compromise between the need for having efficient and optimized trajectories and the need for reacting to unexpected events. This paper presents a new sensor-based Path Planner which results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Voronoi Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted areas in order to obtain the path. The method combines map-based and sensor-based planning operations to provide a reliable motion plan, while it operates at the sensor frequency. The main characteristics are speed and reliability, since the map dimensions are reduced to an almost unidimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments where other methods of planning based on Voronoi do not work.
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.
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.
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.
Motion planning and controlling algorithm for grasping and manipulating movin...ijscai
Many of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving
objects, researchers have been using real time captured images to locate objects dynamically. However,
this approach of controlling the grasping process is quite costly, implying a lot of resources and image
processing.Therefore, it is indispensable to seek other method of simpler handling… In this paper, we are
going to detail the requirements to manipulate a humanoid robot arm with 7 degree-of-freedom to grasp
and handle any moving objects in the 3-D environment in presence or not of obstacles and without using
the cameras. We use the OpenRAVE simulation environment, as well as, a robot arm instrumented with the
Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an
extent of RRT-JT that combines exploration, using a Rapidly-exploring Random Tree, with exploitation,
using Jacobian-based gradient descent, to instruct a 7-DoF WAM robotic arm, in order to grasp a moving
target, while avoiding possible encountered obstacles . We present a simulation of a scenario that starts
with tracking a moving mug then grasping it and finally placing the mug in a determined position, assuring
a maximum rate of success in a reasonable time.
Motion Planning and Controlling Algorithm for Grasping and Manipulating Movin...ijscai
Many of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving
objects, researchers have been using real time captured images to locate objects dynamically. However,
this approach of controlling the grasping process is quite costly, implying a lot of resources and image
processing.Therefore, it is indispensable to seek other method of simpler handling… In this paper, we are
going to detail the requirements to manipulate a humanoid robot arm with 7 degree-of-freedom to grasp
and handle any moving objects in the 3-D environment in presence or not of obstacles and without using
the cameras. We use the OpenRAVE simulation environment, as well as, a robot arm instrumented with the
Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an
extent of RRT-JT that combines exploration, using a Rapidly-exploring Random Tree, with exploitation,
using Jacobian-based gradient descent, to instruct a 7-DoF WAM robotic arm, in order to grasp a moving
target, while avoiding possible encountered obstacles . We present a simulation of a scenario that starts
with tracking a moving mug then grasping it and finally placing the mug in a determined position, assuring
a maximum rate of success in a reasonable time.
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.
Improving Posture Accuracy of Non-Holonomic Mobile Robot System with Variable...TELKOMNIKA JOURNAL
This paper presents a method to decrease imprecision and inaccuracy that have the tendency to
influence the posture of non-holonomic mobile robot by using the adaptive tuning of universe of discourse.
As such, the primary objective of the study is to force the posture error of , , and towards
zero. Hence, for each step of tuning the fuzzy domain, about 20% of imprecision and inaccuracy had been
added automatically into the variable universe fuzzy, while the control input was bound via scaling gain.
Furthermore, the simulation results showed that the tuning of universe fuzzy parameters could increase
the performance of the system from the aspects of response time and error for steady state through better
control of inaccuracy. Besides, the domains of universe fuzzy input [-4,4] and output [0,6] exhibited good
performance in inching towards zero values as the steady state error was about 1% for x(t) position, 0.02%
for y(t) position, and 0.16% for θ(t) orientation, whereas the posture error in the given reference was about
0.0002% .
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
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.
Design and Fabrication of Obstacle Avoiding Robotic VehicleIRJET Journal
The document describes the design and fabrication of an obstacle avoiding robotic vehicle. Some key points:
- The robotic vehicle uses an Arduino microcontroller and ultrasonic sensors to detect obstacles in its path. It is able to maneuver autonomously in unknown environments without collisions.
- When an obstacle is detected, the microcontroller redirects the robot by controlling the motors to move in an alternate direction and avoid the obstacle.
- The low-cost components like the Arduino, ultrasonic sensors, motor driver and DC motors make the robot easily replicable. The robot is able to fulfill goals like autonomous obstacle detection and avoidance in real-time without external control.
This document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO mimics the movement of bacteria in searching for nutrients to find optimal paths. ACO is inspired by how ants find food by leaving and following pheromone trails. PSO is based on the social behavior of bird flocking and fish schooling, with robots updating their movements based on their own experiences and those of nearby robots. The document reviews the mechanisms and applications of each algorithm for solving multi-robot path planning problems.
This document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO mimics the movement of bacteria in search of food to find optimal paths. ACO is inspired by how ants find food paths using pheromone trails; the algorithm uses heuristic information and state transition rules. PSO is based on the social behaviors of bird flocking and fish schooling; it represents solutions as particles that fly through the problem space following certain rules.
Obstacle detection for autonomous systems using stereoscopic images and bacte...IJECEIAES
This paper presents a low cost strategy for real-time estimation of the position of ob- stacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.
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.
This document describes a new approach for behavioral control of swarm robots using implicit communication. The key aspects of the proposed algorithm are:
1) Robots can operate in either a centralized or decentralized mode. In centralized mode, one robot acts as the leader and others follow. In decentralized mode, each robot plans its path individually.
2) Robots can switch between these modes depending on obstacles. If an obstacle disrupts communication between the leader and follower, they switch to decentralized mode.
3) Robots are equipped with sensors like IR sensors for obstacle avoidance and an IR seeker to navigate toward a goal. RF transceivers enable implicit communication between robots.
4) The algorithm was implemented using
In this report, one of the main applications of fuzzy logic is proposed i.e in robotic navigation.
Starting from scratch to building up the fuzzy logic and its validation using the MATLAB fuzzy logic toolbox , everything is covered in this report. If you find it helpful do like and share it with your friends. Fuzzy logic finds its application in AGVs and autonomous vehicles etc. Nowadays it is employed to find out the instantaneous power split ratio between the Engine and battery in the parallel hybrid EV.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
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!
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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.
2. However, a weakness of such a class of approaches is that
after planning the entire path, the environment cannot
change further. If that happens (for instance, an obstacle
not included in the environmental map appears), free‐of‐
collision navigation is no longer guaranteed.
Other approaches to avoid obstacles are based on the
reactive paradigm [1], whose basic assumption is that the
robot has no a priori knowledge about the environment
surrounding it. It simply reacts to such an environment
according to its perception of it. As a consequence,
reactive navigation demands a suitable sensing system
and also much less computation for working with local
information (the presence/absence of an obstacle close to
the robot). Environment changes are no longer a problem:
the robot perceives the changes and reacts to them (for
instance, the presence of a static obstacle that was not in
the environment in a previous navigation). Therefore,
reactive navigation is more suitable to weakly structured
environments, while deliberative navigation is more
suitable to strongly structured environments. Edge
detection [6] and potential fields [7] are classical examples
of reactive navigation strategies (more recent examples of
reactive navigation are reported in [8–11]).
To overcome the problem of not being able to deal with
unpredicted obstacles, some deliberative approaches [4, 5,
12] temporarily change the planned path, reacting to the
presence of an obstacle. Therefore, they would be better
classified as hybrid approaches [1], involving both
deliberative and reactive paradigms.
In such a context, this paper revisits the problem of
obstacle avoidance in mobile robot navigation, and
proposes a strictly reactive, simple and fast‐to‐compute
approach, hereinafter referred to as tangential escape, to
guide the robot to reach a pre‐defined goal after leaving
behind any obstacle appearing in its path. In essence,
such an approach is quite different to other previously
proposed approaches to performing the same task, as it
will become clear. For instance, it demands a far simpler
feature extraction, in terms of the sensorial data,
compared to the approach proposed in [8]: our proposal
requires just the knowledge of the minimum robot‐
obstacle distance. Compared to the approach proposed in
[13], the advantage of the approach proposed here is that
no decision‐tree is computed, thus allowing a faster
reaction in the presence of obstacles. Compared to the
approach proposed in [11], derived from the potential
field method, an additional random force is adopted
there to allow the robot to escape from the obstacle,
whereas in our strategy this is performed by a supervisor
added to the controller.
On the other hand, the approach here discussed is
conceptually similar to the tangent bug algorithm [14, 15]:
both are globally convergent and assure that the path
2
Int J Adv Robotic Sy, 2013, Vol. 10, 278:2013
followed by the robot in the free‐space is optimum (it
moves along the shortest path linking its current position
and the goal, when well‐oriented with respect to it).
However, the implementation proposed here is quite
different: a single supervised nonlinear Lyapunov‐based
controller is adopted to implement the behaviours ʺto
navigate towards the goalʺ and ʺto follow the obstacle
boundaryʺ [10, 14]. The global convergence is also
guaranteed in our approach, since the control system
implemented is proven to be globally asymptotically
stable in the absence of obstacles and is able to leave all
obstacles behind (see Subsection 2.2 and Section 3,
respectively). Therefore, after leaving all obstacles
behind, it is guaranteed that the robot will reach the goal
(supposing it can be reached). In addition, the use of the
Lyapunov theory to design such a controller also
guarantees that the robot moves towards the goal
following the least‐energy path, in the absence of
obstacles.
As for the version of the tangential escape published in
[16], this manuscript describes all aspects of the
technique, whereas the first published version discusses
just the basic idea. In terms of novelty, most content of
Section 3, the simulations and most experiments of
Section 4 are being presented for the first time. Actually,
using the version of the tangential escape strategy
published in [16], the simulations and the most difficult
experiments reported in Section 4 would not attain the
same result, for that version did not deal with the
possibility that the robot can get trapped in the obstacle.
Therefore, this version of the tangential escape algorithm
is complete, whereas the one in [16] is the first step in the
method.
To describe and validate the tangential escape approach,
the paper is hereinafter split in four sections. The kinematic
model of the mobile robot and a nonlinear controller able
to guide it to the goal in the absence of obstacles are
presented in Section 2. Section 3 describes the
implementation of the tangential escape approach through
a nonlinear control system, using the range measurements
provided by a laser scanner onboard the robot. Notice that
any other range sensor could be used instead of a laser
range finder (the proposed approach relies on the range
measurements, not on the range sensor). However, as the
laser scanner allows getting range measurements with
quite a good angular resolution, it was adopted here. The
results of the simulations and real experiments run using
such a control system are presented and discussed in
Section 4. Finally, Section 5 highlights the main conclusions
of the work.
2. Seeking the Goal in Free‐space
The strategy we propose encompasses two subtasks the
robot should accomplish: reduce its distance to the goal, if
www.intechopen.com
3. there are no obstacles inside a semi‐circle of radius dobs in
front of it, and change its current orientation to avoid the
closest obstacle, if there are obstacles inside the same
semi‐circle. In the second case, after leaving the obstacle
behind the robot resumes the first subtask, and the
distance robot‐goal is continuously reduced. As a result
of such a combination, the robot reaches its goal after
leaving all obstacles behind. In addition, if the robot
should assume a given orientation after reaching the goal,
its final orientation is corrected, while keeping its
position. Such a strategy is illustrated by the basic flow
diagram of Figure 1, which will be analysed in depth in
the sequel.
To implement the proposed approach, such behaviours
are embedded in a nonlinear control system having two
nested loops. The inner one is in charge of reducing the
distance robot‐goal in the absence of obstacles, whereas
the outer one is responsible for steering the robot away
from the obstacle closest to it. In this section, the inner
control loop is discussed: a controller to guide the robot
to reach the goal is proposed, and the equilibrium of the
closed‐loop control system thus implemented is proven
to be globally asymptotically stable. Thus, the robot will
reach the goal after leaving all obstacles behind, assuring
the global convergence [14].
of interest (whose position should be controlled), is the
robot orientation with respect to the x‐axis of the global
frame o , and a is the distance from the point whose
position is being controlled (the position of the laser
scanner) to the middle of the virtual axle linking the
driven wheels (see Figure 2). The mathematical
description of the navigation towards the goal in an
environment free of obstacles, in polar coordinates, is
given by [17]
cos
a sin
sin a cos 1 u
H
sin
cos
a
(1)
xd cos yd sin
xd sin yd cos Kv ,
yd cos
xd sin
0
where is the distance robot‐goal (the goal is marked as
g in Figure 2, and its coordinates in o are defined by
the user). The direction of movement is always
orthogonal to the virtual axle, and causes the orientation
error with respect to the goal position. The angle
defines the orientation desired when the robot reaches the
goal, and (see Figure 2). The inertial system of
coordinates shown in the figure, o , corresponds to the
robot initial position. Notice that the rightmost term of (1)
is null, because only position control is considered in this
work, meaning that the goal is static, i.e., xd yd 0.
Figure 1. Flow diagram of the proposed strategy.
2.1 The Kinematic Model of the Robot
The mobile robot used in the experiments here reported is
a differential drive platform, whose kinematic model is
x cos
y sin
0
a sin
u
a cos ,
1
where u and are, respectively, the linear and angular
velocities, x and y are the current coordinates of the point
www.intechopen.com
Figure 2. The mobile robot seeking the goal g .
Notice that the robot deals with a local polar depth map,
similar to the one used in [18]. Notice also that cannot be
zero, because this would make and undefined. Thus,
the robot is considered in the goal when , where is
an arbitrarily small positive value (hereinafter 30 mm).
Alexandre Santos Brandão, Mário Sarcinelli-Filho and Ricardo Carelli:
An Analytical Approach to Avoid Obstacles in Mobile Robot Navigation
3
4. 2.2 Position Control in the Free‐Space
From Figure 2 one can see that the robot can be effectively
controlled by suitably controlling the values of u and .
Then, a control system aiming at making 0 and
0 is proposed. A control law that guarantees the
additional condition d , after getting the conditions
0 and 0, if necessary, is discussed in the next
subsection.
Therefore, the objective here is to make the state vector
[ ]T go asymptotically to zero, from any initial
H
value. To design a controller guaranteeing that, the
Lyapunov candidate function
V
1 T
H H0
2
is adopted, and the control law designed should make the
first temporal derivative of V ,V HT H , be negative for
all nonzero values of H to guarantee the stability of the
closed‐loop system (to guarantee that H [0 0]T ).
Taking into account the kinematic model in (1), one gets
tends to a bounded value, showing that the robot
reaches the goal with a well‐behaved arriving angle,
although not defined by the designer.
The function tanh is used to saturate the values of u and
ω at the maximum values, u max and max , obtained from
the robot data sheet. Such saturation is adopted to
prevent the possibility that the physical actuators of the
mobile robot saturate, which would occur in the case of a
control signal of large magnitude.
The control system proposed to guide the robot to reach
the goal in the absence of obstacles is illustrated in Figure
3. There X d x d
X x
y d T defines the goal position,
y T defines the current robot position, obtained
through the robot odometry (as well as the angle ),
T
T
X x y xd x yd y , so that x 2 y 2
y
and arctan .
x
V H T Kv
which is negative for any nonzero value of H for the
control law
v K 1 ( H d κ tanh H ) (2)
where κ is a diagonal positive definite matrix. Therefore,
considering Hd 0 (because in position control the goal is
static),
V H T κ tanh H 0
thus demonstrating the global asymptotic convergence of
H0, meaning that the robot will always reach its goal in
the absence of obstacles. It should be observed that when
the robot reaches the goal it stays there, because the
values of u and become zero, as one can see from
cos
0 a cos
u u max
max sin
0
a cos
a sin
a cos tanh (3)
,
tanh
a
1
a cos
which is obtained from (2), when Hd 0.
To complete the stability analysis, it is important to check
the behaviour of the angle when the robot reaches the
goal. Knowing that and that the system is
asymptotically convergent, 0 when the robot reaches
the goal. Taking into account that (tanh )/ 1 when
0, it results that 0 when t . Therefore,
4
Int J Adv Robotic Sy, 2013, Vol. 10, 278:2013
Figure 3. The control system in charge of guiding the robot to the
goal in the absence of obstacles.
2.3 Orientation Control in the Goal
After reaching the goal position, the robot can be badly
oriented to perform some specific task, such as grasping
an object using an onboard end‐effector, for instance. In
this case, it is necessary to control just the angle , not
allowing any additional displacement (taking advantage
of the fact that the mobile platform is a unicycle one). To
do that, it is now designed an additional controller to
impose a suitable angular velocity to the robot, while
keeping its linear velocity u null (to keep it in the goal).
From Figure 2 one can notice that d , so that
d is the final orientation error. Thus, for a
constant final orientation it results that is the
model of the robot when manoeuvrearound to correct the
orientation without moving ahead or back. In such a case,
one can choose the Lyapunov candidate function
1
V 2 , (4)
2
whose first time derivative is
sin
cos
V u
a
. (5)
Now, imposing that
www.intechopen.com
5. u 0
(6)
max tanh ,
with max 0, and remembering that the robot is already
in the goal, or , one gets
a
V max tanh 1 cos , (7)
which is negative definite if| | / 2. Thus, the
orientation error converges asymptotically to zero, and
hence converges asymptotically to d , the desired
value.
2.4 Stability During the Switching of the Control Systems
After reaching the goal, the robot should orientate itself to
reach the desired final pose. Therefore, it is necessary to
switch from the position controller to the orientation one,
in order to correct the robot orientation on arrival. In such
a case, the stability of the control system during the
switching should also be guaranteed. This is done using
the direct extension of the theorem of Lyapunov, which
guarantees the stability during the switching when the
individual controllers are designed using the same
Lyapunov candidate function [19].
In the present case, as , it is straightforward to
demonstrate that (2) and (4) represent the same
Lyapunov function. Therefore, the stability during the
switching from the position error controller to the
orientation controller is guaranteed.
To conclude this section, it is worth mentioning that one
could design a control law involving , and
simultaneously [17], instead of using the strategy of
correcting the arrival angle only after reaching the goal.
3. The Proposed Approach
A nonlinear controller to guide the robot while avoiding
obstacles is now proposed. The strategy adopted to
design such a controller consists in choosing an escape
path that is tangent to the obstacle border, in a way quite
similar to the navigation adopted by human beings in
unknown environments. Such a controller also includes a
supervisor, which is in charge of changing the robot
orientation right after the occurrence of some specific
situations, allowing the vehicle to escape of typical local
minima, as discussed in the sequel.
The proposed controller uses the scheme discussed in
Section 2 for reducing the distance robot‐goal. However,
the goal position is redefined whenever an obstacle is
detected at a distance smaller than or equal to dobs from
the robot. Thus, dobs defines the obstacle avoidance zone
shown in Figure 4, and when the robot enters such a zone
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it should start an evasive manoeuvre. In the same figure,
dmin is
the
smallest
robot‐obstacle
distance,
correspondent to a certain range scan. In the presence of
an obstacle d min d obs a temporary target, X v , called
virtual goal, is created, whose position is obtained from
the position of the real goal, X d , through a rotation
matrix. Notice that the line linking the point being
controlled and the virtual goal is tangent to the border of
the detected obstacle, as shown in Figure 4.
The rotation angle , which defines the rotation matrix
that creates the virtual goal, is obtained from the set of
range measurements provided by an onboard
rangefinder. To define , it is necessary to know , which
defines the position of the closest obstacle. After each
range scan, the proposed system checks if there is at least
one distance d dobs , and the angle correspondent to
such a value is the angle . The laser scanner here
adopted as the rangefinder delivers range measurements
for angles in the range [0°, 180°], in steps of 1°. Such
angles are mapped to the range [−90°, +90◦], so that 0
when the obstacle is to the right of the robot and 0
when the obstacle is to the left. Given one gets
90 if 0
(8)
90 if 0
where 0 when the goal is at the right side of the axis
of movement of the robot (see Figure 4). Notice that in
Figure 4, the angle is positive, causing the rotation of
the real goal to the left, considering the axis of movement
of the robot.
As the robot navigates in a plane, the angle is used to
get
cos
Xv
sin
sin
X , (9)
cos d
where X d and X v are, respectively, the position of the real
goal (desired) and the virtual goal (temporary). Having
the coordinates of X v the position controller guides the
robot to the new goal, following the tangent to the
obstacle border. Notice that in the absence of obstacles
there is no change in the position of the real goal ( 0),
and the robot continues getting closer to it.
It should be emphasized that this strategy is different
from the one presented in [20], for instance, in the sense
that there is no fictitious forces characterizing the
interaction robot‐environment. Thus, no impedance
parameters should be selected for the present strategy,
like in [20], making it easier to set the system parameters.
The designer just has to define the distance dobs that
characterizes the obstacle avoidance zone shown in
Figure 4. This is an important characteristic of the
Alexandre Santos Brandão, Mário Sarcinelli-Filho and Ricardo Carelli:
An Analytical Approach to Avoid Obstacles in Mobile Robot Navigation
5
6. strategy here proposed, because the system based on
fictitious forces may cause the robot to get stuck in certain
situations [16, 21].
In addition, as the robot should perform a strong turn
around manoeuvre, it is also necessary to change how the
virtual goal is defined, to guarantee that u is low enough
to minimize the risk of a collision during the evasive
manoeuvre (cautious navigation). From (3), a possible
solution to reduce the value of u is to reduce the value of
1/ 2
X v X
during obstacle avoidance. Our proposal
is to use
Figure 4. The tangential escape approach. Notice that 0
and 0 , resulting in 0.
2
,
The control system proposed to implement the tangential
escape approach is depicted in Figure 5, where
u, , , X d and X v are defined in Subsection 2.2, whereas d
represents the set of range measurements provided in each
scan of the laser range sensor, and is the rotation angle.
Notice that the block pose controller is active all the time, i.e.,
the robot does not stop to manoeuvre in order to deviate
from the obstacle. Based on such a diagram, and taking into
account the asymptotic stability of the pose controller, one
can conclude that after leaving all obstacles behind the robot
always reaches its goal, unless it is not reachable.
To describe the tangential escape strategy a little further,
the following subsections present particular cases of
obstacle configurations that the robot can face during
navigation, highlighting how the system proposed here
deals with each one.
cos
Xv
sin
sin d min cos
(11)
cos d min sin
as the virtual goal, instead of (9), which corresponds to
considering the virtual goal closer to the robot, in the
same direction. The result is that the robot turns around
with a lower linear velocity, thus reducing the risk of
collision during the evasive manoeuvre.
Figure 5. Block diagram of the control system based on the
tangential escape approach.
3.1 Dealing with Corner‐like Obstacles
There are situations in which the robot should perform more
aggressive manoeuvres in order to overcome an obstacle.
One such situation is when it should overcome a corner‐like
obstacle, as in the case of L‐, V‐ or U‐shaped obstacles. This
situation is illustrated in Figure 6, where the real goal is
below the horizontal part of the L‐shaped obstacle.
In these cases, the value of the angle in (8) does not
guarantee that the robot will escape the obstacle.
Actually, after turning around according to the angle , in
Figure 6, the robot faces the vertical part of the obstacle,
and should continue turning around. To make such a
manoeuvre faster, we change the value of in (8) to
180 if 0
(10)
180 if 0
whenever the distances d90 (the range measurement
correspondent to the angular position 90 , if 0, or
90 , if 0 ‐ see Figure 6) and dmin are
simultaneously less than dobs (90° is added to , with the
same signal).
6
Int J Adv Robotic Sy, 2013, Vol. 10, 278:2013
Figure 6. Changing the tangential escape approach to take into
account corner‐like obstacles.
3.2. Dealing with Concave Obstacles
Another particular situation that deserves special attention is
when the robot is badly oriented with respect to the goal. A
good example is when the robot finishes following one side
of a U‐shaped obstacle, as illustrated by the darker sketch in
Figure 7. The robot navigates from the white sketch
straightforwardly to the real target, entering the U‐shaped
obstacle. When it gets close to the bottom of the U‐shaped
obstacle it turns left and follows the obstacle border. When it
gets close to the upper part of the U‐shaped obstacle it turns
left again, according to (9) or (11), and follows the side of the
U‐shaped obstacle, until reaching the position correspondent
to the darker robot sketch. In such a position, the
orientation error α illustrated in the figure shows that the
robot is badly oriented with respect to the real goal.
www.intechopen.com
7. starts seeking this temporary goal, using the position
controller depicted in Figure 3. However, if it detects an
obstacle during this temporary goal‐seeking, it resumes
its original goal and stops seeking the temporary one.
Otherwise, after reaching the temporary goal the robot
corrects its orientation, in order to get oriented as in the
intermediary grey robot sketch in Figure 7. Such a correction
is performed using the orientation controller discussed in
Subsection 2.3, for which the desired orientation angle is
Figure 7. Turning around 90° to overcome the obstacle extremity.
After getting rid of the obstacle, the robot could rotate to
the left, to resume the navigation towards the real goal,
getting trapped inside the U‐shaped obstacle (a typical case
of local minimum). To escape from such a local minimum,
an auxiliary flag Pobs is created, to indicate the presence (or
absence) of an obstacle. After getting a new scan of the
laser rangefinder, Pobs is set to 1 if at least one range
measurement is less than dobs , and reset to 0 if all the range
measurements in a laser scan are greater than dobs .
Analysing the transition of Pobs , it is possible to determine
when the robot faces an obstacle (a positive‐edge
transition) or leaves it behind (a negative‐edge transition).
Notice that if the robot follows the obstacle border, at least
one range measurement in a laser scan will be less than
dobs , thus not causing new negative‐edge transitions in
Pobs before finishing following the obstacle border.
After overcoming the obstacle, to avoid getting trapped
the robot mimics human behaviour when navigating in
an unknown environment containing walls: it rotates
over the obstacle extremity. When the flag Pobs is reset the
value of is checked: if it is greater than 90°, as depicted
in Figure 7, the robot manoeuvres in order to contour the
extremity of the U‐shaped obstacle (following the dashed
line of Figure 7). This is done by creating a temporary
goal and using the tangential escape approach to navigate
towards it, as shown in Figure 8. Such new temporary
T
goal x tg y tg is defined as
x
sin
d min
x tg y
cos
sin
y tg x
y dmin cos
cos 1
if 0
sin 1
cos 1
if 0,
sin 1
(12)
where X [x y]T corresponds to the current robot
position referred to the inertial frame, is its current
orientation, dmin is the minimum distance robot‐obstacle
in the range scan obtained right before resetting Pobs , and
is the angle correspondent to d min . Then, the robot
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90 if 0
(13)
tg
90 if 0,
where is the robot orientation just before resetting Pobs .
After this re‐orientation, the robot is in the temporary
goal and better oriented with respect to the real goal, so
that navigation towards the real goal is resumed.
3.3 Using Stored Information about Obstacles Previously Detected
There may be situations in which the robot reaches a
position in its working environment from where it had
already detected the presence of an obstacle. In such cases,
an obstacle has been detected the second time, meaning that
the robot has travelled along a whole loop without finding a
path to its goal, and is about to enter such a loop again.
To deal with such a situation, the supervised controller
proposed here uses a memory buffer to store the
positions of the robot in the time instants it detects an
obstacle (whenever Pobs goes from 0 to 1). Therefore, such
a buffer contains all the positions in the workspace of the
robot, from where it detected an obstacle. Each sampling
time (each 100 ms, for the experimental setup here
adopted) the controller checks the current robot position
against all the positions stored in such a buffer (within a
certain tolerance). If there is no coincidence (or if the
buffer is empty), the robot simply continues moving.
Upon finding any coincidence, the robot ʺknowsʺ it has
detected an obstacle from that position before, as many
times as the number of coincidences, and stops moving to
make a decision: if there is just one coincidence, the robot
makes a half turn, using the orientation controller of
Section 2.3, to try to find a way to the goal in the opposite
direction, stores such a position in the buffer once more,
and restarts moving; if there are two coincidences, this
means that the robot has passed by that position twice,
once in each direction, not finding a way to get to the
goal. As a consequence, it simply stops the navigation,
because the goal it is seeking is not reachable. These
actions of the supervisor, integrated into the whole
tangential escape approach, are depicted in the pseudo
code presented in Algorithm 1.
Alexandre Santos Brandão, Mário Sarcinelli-Filho and Ricardo Carelli:
An Analytical Approach to Avoid Obstacles in Mobile Robot Navigation
7
8. 1: Initialize: Pobs , ,dobs , Memory buffer;
2: while true do
3: if then
4:
Read laser scanner;
5:
Get dmin and .
6:
if min dobs then
d
7:
Set Pobs and store robot position;
8:
Check robot position in buffer;
9:
if Such position is already in the buffer then
10:
if It is the first coincidence then
11:
Make t 180;
12:
while e t do
13:
Run orientation controller;
14:
end while
15:
else
16:
Stop navigation | Goal not reached;
17:
end if
18:
else
19:
Calculate and rotate the original goal;
20:
end if
21:
else
22:
if Pobs 1 then
23:
Reset Pobs ;
24:
Call OBSTACLE EXTREMITY;
25:
end if
26:
end if
27:
Execute the position controller;
28:
Resume the original goal;
29: else
30:
if d then
31:
Run the orientation controller;
32:
else
33:
Stop navigation | Goal reached;
34:
end if
35: end if
36: end while
37: function OBSTACLE EXTREMITY
38: if 0 then
39:
Create a temporary goal NW of the robot;
40: else
41:
Create a temporary goal NE of the robot;
42: end if
43: while true do
44:
if tg then
45:
Read laser scanner;
46:
Get dmin and ;
47:
if min dobs then
d
48:
Execute the position controller;
49:
else
50:
Return;
51:
end if
52:
else
53:
if d then
54:
Execute the orientation controller;
55:
else
56:
Return;
57:
end if
58:
end if
59: end while
60: end function
Algorithm 1. The tangential escape strategy
8
Int J Adv Robotic Sy, 2013, Vol. 10, 278:2013
Figure 8. Flow diagram of the whole tangential escape
algorithm.
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9. Notice that such checking is not demanding in terms of
computation, since only after the robot gets rid of an
obstacle (the flag Pobs is reset to 0) and detects a new one
(the flag Pobs is set to 1) a new position is stored in the
memory buffer, resulting in just a few values to check. In
Figure 10, the points where the robot detects a new
obstacle, thus storing the correspondent positions in the
memory buffer, are marked (they are sequentially
numbered, with the number presented inside either a
black circle or a dashed‐line). As one can check, just a few
positions are stored, so that the time spent checking the
current position against those stored in the memory is
quite low. In addition, the obstacles with the number
inside a black circle correspond to the positions that are
stored twice in the memory buffer. In other words, they
correspond to the positions where the robot turned
around 180°, to try another path to the goal.
However, it should be emphasized that the above
mentioned position checking demands a good
localization subsystem, since the vehicle should be able to
compare its current position to those stored in the
memory. In the simulations presented in the sequel, only
the robot odometry was used. In real situations, however,
a SLAM algorithm [22, 23] is a good option to obtain
better position estimates.
Concluding this section, the main contribution of this
paper is emphasized: the design and validation of a
supervised nonlinear controller able to guide the robot
to a goal, avoiding all obstacles in its path. The
proposed supervisor accomplishes three tasks, all of
them generating new ʺgoalsʺ for the robot: it checks if
d90 d obs , to make a decision between using (8) and
(10) to compute the angle that generates the
coordinates of the virtual goal; if| | 90 , to turn or not
over the extremity of the obstacle, as depicted in Figure
7; and if any detected obstacle was detected before,
either to stop the navigation or to add ( or not) 180° to
the current robot orientation before continuing the
navigation.
controller available onboard the robot, is 10Hz. For the
simulations, the models of the robot and the laser
scanner, provided by the manufacturer, are used.
Figure 10 illustrates the simulation results for some
typical local‐minimum environments (Crown‐, Zig‐Zag
and G‐shaped obstacles) in which the proposed
algorithm has shown to be able to guide the robot to the
goal, leaving the obstacles behind. In Figure 10(a) a Zig‐
Zag obstacle is considered, and the robot path towards
the goal is shown. Notice that in this simulation six
obstacles are detected, although none of them is
detected more than once. In Figures 10(b) and 10(c), in
their turn, it is important to highlight the instances in
which the mobile robot reaches a point it had already
visited (marked with a black circle). To find a new
feasible path to the goal, it changes its current
orientation to the opposite direction, using the
orientation controller of Subsection 2.3, before
resuming the navigation. In Figure 10(b), such a
manoeuvre occurs when the robot is in the coordinates
(2.5m, 2m) and (5.5m, 2m), whereas in Figure 10(c) it
occurs when the robot is in the coordinates (1.5m, 0m).
As one can notice from the figures, the proposed
strategy is effectively able to guide the robot to the goal
avoiding such obstacles.
As for the simulator adopted to generate the results of
Figure 10, it is a MATLAB® code correspondent to the
tangential approach, which includes models of the
Pioneer 3‐DX and its laser rangefinder provided by the
manufacturer.
4. Simulated and Experimental Results
Figure 9. Experimental setup: (a) the mobile robot Pioneer 3‐ DX
with the laser scanner SICK LMS100 onboard; (b) a sketch of the
range measurements obtained each sampling time.
Several simulated and real experiments were run using
the control system discussed in Section 3, in order to
validate the approach proposed here to avoid obstacles
during goal‐seeking, and some of them are now
discussed. The experiments were run using a Pioneer 3‐
DX unicycle robot (shown in Figure 9(a)), equipped with
a laser scanner SICK LMS100, provided by the same
manufacturer, which delivers 181 range measurements at
each scan, covering a semi‐circle ahead of the robot,
centred on its axis of movement, as shown in Figure 9(b).
The angular resolution of the laser scanner is 1°, and its
sampling rate, as well as the one of the low‐level
www.intechopen.com
(a) (b)
In the sequel, four real experiments are reported and
discussed. In the first one the objective is to reach a goal
positioned behind a U‐shaped obstacle, using the
algorithm represented in Figure 8. Figure 11 shows the
path the robot followed (part a), the position error, the
orientation error and the robot orientation (part b), and
the control signals u and along the experiment (part c).
A description of what happens along the experiment is
quite similar to the description of Figure 6, as the
experiment aims just to check the effectiveness of the
Alexandre Santos Brandão, Mário Sarcinelli-Filho and Ricardo Carelli:
An Analytical Approach to Avoid Obstacles in Mobile Robot Navigation
9
10. proposed algorithm to overcome U‐shaped obstacles. As
one can see in the graphics after the robot reaches the real
goal (after t = 80 s) effectively 0 and . From this
moment on, the position error remains unchanged, the
linear velocity remains constant at the value u 0 and is
varied until the desired final orientation d 0 is reached,
becoming permanently null from this moment on, thus
concluding the navigation, as shown in Figures 11(b) and
11(c).
The paths the robot followed during the other three
experiments are shown in Figure 12. In such
experiments the robot should navigate through a Z‐
shaped corridor with obstacles in it, and to leave behind
an L‐shaped and a V‐shaped obstacle, respectively. The
result is that the tangential escape is effectively able to
guide the robot to the goal while avoiding collisions and
not getting stuck. A description of the manoeuvres the
robot performed along the experiments correspondent
to overcome the V and L‐shaped obstacles is quite
similar to the description associated with Figure 7: the
robot starts navigating straightforwardly to the target,
as it is well oriented, until entering the obstacle. Then, it
manoeuvres to the left and continues navigating. When
it ʺperceivesʺ that both dmin and d90 are lower than dobs ,
it rotates 90° more to the left, and starts following the
direction of the obstacle wall. At the end of the obstacle
wall, the robot executes the manoeuvre of Figure 7,
against the end of the obstacle wall, thus leaving the
obstacle behind and taking its way to the target, now in
free‐space. In terms of the navigation in a Z‐corridor
with obstacles along it, the robot starts taking a path to
go to the target directly, compensating the initial
orientation error. Doing that, it naturally avoids the first
obstacle, as it navigates towards the left wall of the
corridor. Then, it manoeuvres to the right to avoid such
wall, and takes a straightforward path to the target
again. On going on, the robot detects the right wall of
the corridor, and rotates left to avoid it. On doing that, it
detects the second obstacle along the corridor and
rotates to the right to avoid it, and to the left again, to
avoid the end of the corridor wall. After those
manoeuvres, it takes a straightforward path to the
target, now in the free‐space, and continues navigating
until reaching the target.
Referring to the parameter dobs involved in the
implementation of the tangential escape approach, for
the cases of the U‐, L‐ and V‐shaped obstacles, its value
was 700 mm, while in the case of the Z‐shaped corridor
its value was 500 mm (due to the small distance
between the obstacles along the corridor and the
corridor walls). Such values were arbitrarily chosen and
could be the same for all simulations and experiments
described here.
10 Int J Adv Robotic Sy, 2013, Vol. 10, 278:2013
Therefore, considering the simulations and the
experimental results, the conclusion is that the tangential
escape is an attractive approach to avoid obstacles when a
mobile robot is seeking a goal, because of its low
computational cost and its capability of preventing the
robot from becoming stuck in local minima. Moreover,
the controllers responsible for reducing the distance
robot‐goal and correcting the final robot orientation ‐ (3)
and (6), respectively ‐ are quite easy to design.
Figure 10. Distinct obstacle‐avoidance simulations.
www.intechopen.com
12. 5. Concluding Remarks
A simple and effective approach is proposed here to
avoid obstacles when a mobile robot is seeking a goal,
which is referred to as the tangential escape. The proposal
consists of a supervised nonlinear controller that guides
the robot to follow the direction of the tangent to the
obstacle border, whenever an obstacle is detected closer
to the robot than a specified distance. Through the
supervisor, a strategy quite similar to the one adopted by
human beings is implemented.
The control system thus implemented is shown to be
globally asymptotically stable in the Lyapunov sense, in
the absence of obstacles, and able to leave all obstacles
behind. Therefore, the proposed approach guarantees
that the robot will reach any reachable goal.
Several simulations and experiments, including the
hardest obstacles known in the literature, were run using
the proposed system, with range measurements provided
by a laser scanner. Their results have shown that the
proposed controller is effectively able to guide the robot
to the goal without colliding with any obstacle or getting
stuck in local minima, thus validating the proposal.
6. Acknowledgements
The authors thank CNPq, the Brazilian National Council
of Scientific and Technological Development, for the
financial support given to this work. They also thank
CAPES ‐ a foundation of the Brazilian Ministry of
Education ‐ and SPU ‐ a secretary of the Argentine
Ministry of Education ‐ for financing the partnership
between the Federal University of Espirito Santo, Brazil,
and the National University of San Juan, Argentina.
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Alexandre Santos Brandão, Mário Sarcinelli-Filho and Ricardo Carelli:
An Analytical Approach to Avoid Obstacles in Mobile Robot Navigation
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