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Adaptive route optimization for mobile robot navigation using evolutionary algorithm

As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior tow...

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Main Authors: Kit Guan Lim, Guan Lim, Yoong Hean Lee, Hean Lee, Min Keng Tan, Keng Tan, Hou, Pin Yoong, Tienlei, Wang, Tze, Kenneth Kin Teo
Format: Proceedings
Language:English
English
Published: Institute of Electrical and Electronics Engineers 2021
Subjects:
QA1-43 General
TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Online Access:https://eprints.ums.edu.my/id/eprint/32534/1/Adaptive%20route%20optimization%20for%20mobile%20robot%20navigation%20using%20evolutionary%20algorithm.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32534/2/Adaptive%20Route%20Optimization%20for%20Mobile%20Robot%20Navigation%20using%20Evolutionary%20Algorithm.pdf
https://eprints.ums.edu.my/id/eprint/32534/
https://ieeexplore.ieee.org/document/9573543
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spelling my.ums.eprints.325342022-05-03T14:11:02Z https://eprints.ums.edu.my/id/eprint/32534/ Adaptive route optimization for mobile robot navigation using evolutionary algorithm Kit Guan Lim, Guan Lim Yoong Hean Lee, Hean Lee Min Keng Tan, Keng Tan Hou, Pin Yoong Tienlei, Wang Tze, Kenneth Kin Teo QA1-43 General TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning. Institute of Electrical and Electronics Engineers 2021-10-28 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32534/1/Adaptive%20route%20optimization%20for%20mobile%20robot%20navigation%20using%20evolutionary%20algorithm.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/32534/2/Adaptive%20Route%20Optimization%20for%20Mobile%20Robot%20Navigation%20using%20Evolutionary%20Algorithm.pdf Kit Guan Lim, Guan Lim and Yoong Hean Lee, Hean Lee and Min Keng Tan, Keng Tan and Hou, Pin Yoong and Tienlei, Wang and Tze, Kenneth Kin Teo (2021) Adaptive route optimization for mobile robot navigation using evolutionary algorithm. https://ieeexplore.ieee.org/document/9573543
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QA1-43 General
TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
spellingShingle QA1-43 General
TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Kit Guan Lim, Guan Lim
Yoong Hean Lee, Hean Lee
Min Keng Tan, Keng Tan
Hou, Pin Yoong
Tienlei, Wang
Tze, Kenneth Kin Teo
Adaptive route optimization for mobile robot navigation using evolutionary algorithm
description As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning.
format Proceedings
author Kit Guan Lim, Guan Lim
Yoong Hean Lee, Hean Lee
Min Keng Tan, Keng Tan
Hou, Pin Yoong
Tienlei, Wang
Tze, Kenneth Kin Teo
author_facet Kit Guan Lim, Guan Lim
Yoong Hean Lee, Hean Lee
Min Keng Tan, Keng Tan
Hou, Pin Yoong
Tienlei, Wang
Tze, Kenneth Kin Teo
author_sort Kit Guan Lim, Guan Lim
title Adaptive route optimization for mobile robot navigation using evolutionary algorithm
title_short Adaptive route optimization for mobile robot navigation using evolutionary algorithm
title_full Adaptive route optimization for mobile robot navigation using evolutionary algorithm
title_fullStr Adaptive route optimization for mobile robot navigation using evolutionary algorithm
title_full_unstemmed Adaptive route optimization for mobile robot navigation using evolutionary algorithm
title_sort adaptive route optimization for mobile robot navigation using evolutionary algorithm
publisher Institute of Electrical and Electronics Engineers
publishDate 2021
url https://eprints.ums.edu.my/id/eprint/32534/1/Adaptive%20route%20optimization%20for%20mobile%20robot%20navigation%20using%20evolutionary%20algorithm.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32534/2/Adaptive%20Route%20Optimization%20for%20Mobile%20Robot%20Navigation%20using%20Evolutionary%20Algorithm.pdf
https://eprints.ums.edu.my/id/eprint/32534/
https://ieeexplore.ieee.org/document/9573543
_version_ 1760231039550095360
score 13.222552

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