Mobile robot path planning using hybrid genetic algorithm and traversability vectors method

The shortest/optimal path generation is essential for the efficient operation of a mobile robot. Recent advances in robotics and machine intelligence have led to the application of modern optimization method such as the genetic algorithm (GA), to solve the path-planning problem. However, the genetic...

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Main Authors:	Loo, C.K., Rajeswari, M., Wong, E.K., RaoTask, M.V.C.
Format:	Article
Published:	2004
Subjects:	T Technology (General)
Online Access:	http://pdf.aminer.org/000/252/008/optimum_path_planning_for_mobile_robots_based_on_a_hybrid.pdf http://pdf.aminer.org/000/252/008/optimum_path_planning_for_mobile_robots_based_on_a_hybrid.pdf http://eprints.um.edu.my/5174/1/Mobile_Robot_Path_Planning_Using_Hybrid_Genetic_Algorithm_and_Traversability_Vectors_Method.pdf

id	um-5174
recordtype	eprints
spelling	um-51742013-03-19T00:27:53Z Mobile robot path planning using hybrid genetic algorithm and traversability vectors method Loo, C.K. Rajeswari, M. Wong, E.K. RaoTask, M.V.C. T Technology (General) The shortest/optimal path generation is essential for the efficient operation of a mobile robot. Recent advances in robotics and machine intelligence have led to the application of modern optimization method such as the genetic algorithm (GA), to solve the path-planning problem. However, the genetic algorithm path planning approach in the previous works requires a preprocessing step that captures the connectivity of the free-space in a concise representation. In this paper, GA path-planning approach is enhanced with feasible path detection mechanism based on traversability vectors method. This novel idea eliminates the need of free-space connectivity representation. The feasible path detection is performed concurrently while the GA performs the search for the shortest path. The performance of the proposed GA approach is tested on three different environments consisting of polygonal obstacles with increasing complexity. In all experiments, the GA has successfully detected the near-optimal feasible traveling path for mobile. 2004 Article PeerReviewed application/pdf http://eprints.um.edu.my/5174/1/Mobile_Robot_Path_Planning_Using_Hybrid_Genetic_Algorithm_and_Traversability_Vectors_Method.pdf http://pdf.aminer.org/000/252/008/optimum_path_planning_for_mobile_robots_based_on_a_hybrid.pdf Loo, C.K.; Rajeswari, M.; Wong, E.K.; RaoTask, M.V.C. (2004) Mobile robot path planning using hybrid genetic algorithm and traversability vectors method. Intelligent Automation and Soft Computing <http://eprints.um.edu.my/view/publication/Intelligent_Automation_and_Soft_Computing.html>, 10 (1). pp. 51-64. ISSN 1079-8587 http://eprints.um.edu.my/5174/
repository_type	Digital Repository
institution_category	Local University
institution	University Malaya
building	UM Research Repository
collection	Online Access
topic	T Technology (General)
spellingShingle	T Technology (General) Loo, C.K. Rajeswari, M. Wong, E.K. RaoTask, M.V.C. Mobile robot path planning using hybrid genetic algorithm and traversability vectors method
description	The shortest/optimal path generation is essential for the efficient operation of a mobile robot. Recent advances in robotics and machine intelligence have led to the application of modern optimization method such as the genetic algorithm (GA), to solve the path-planning problem. However, the genetic algorithm path planning approach in the previous works requires a preprocessing step that captures the connectivity of the free-space in a concise representation. In this paper, GA path-planning approach is enhanced with feasible path detection mechanism based on traversability vectors method. This novel idea eliminates the need of free-space connectivity representation. The feasible path detection is performed concurrently while the GA performs the search for the shortest path. The performance of the proposed GA approach is tested on three different environments consisting of polygonal obstacles with increasing complexity. In all experiments, the GA has successfully detected the near-optimal feasible traveling path for mobile.
format	Article
author	Loo, C.K. Rajeswari, M. Wong, E.K. RaoTask, M.V.C.
author_facet	Loo, C.K. Rajeswari, M. Wong, E.K. RaoTask, M.V.C.
author_sort	Loo, C.K.
title	Mobile robot path planning using hybrid genetic algorithm and traversability vectors method
title_short	Mobile robot path planning using hybrid genetic algorithm and traversability vectors method
title_full	Mobile robot path planning using hybrid genetic algorithm and traversability vectors method
title_fullStr	Mobile robot path planning using hybrid genetic algorithm and traversability vectors method
title_full_unstemmed	Mobile robot path planning using hybrid genetic algorithm and traversability vectors method
title_sort	mobile robot path planning using hybrid genetic algorithm and traversability vectors method
publishDate	2004
url	http://pdf.aminer.org/000/252/008/optimum_path_planning_for_mobile_robots_based_on_a_hybrid.pdf http://pdf.aminer.org/000/252/008/optimum_path_planning_for_mobile_robots_based_on_a_hybrid.pdf http://eprints.um.edu.my/5174/1/Mobile_Robot_Path_Planning_Using_Hybrid_Genetic_Algorithm_and_Traversability_Vectors_Method.pdf
first_indexed	2018-09-05T16:50:01Z
last_indexed	2018-09-05T16:50:01Z
_version_	1610833550276296704

Mobile robot path planning using hybrid genetic algorithm and traversability vectors method

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