NDLI: Optimization of a mobile robot's path tracking

Content Provider	IEEE Xplore Digital Library
Author	Hayet, T. M'sirdi Nacer, K. Jilani, K.
Copyright Year	2011
Description	Author affiliation: LSIS: departement Informatique Industrielle, Polytechnique Marseille, ENIT, France (M'sirdi Nacer, K.; Jilani, K.) \|\| LARA: departement Génie Electrique, Ecole Nationale des Ingénieurs de Tunis (ENIT), Tunisie (Hayet, T.)
Abstract	The optimal path tracking is essential for the efficient motion of a mobile robot in its workspace. That's why several optimization methods have used such as the ant colony, the simulated annealing and the genetic algorithms. In our work we have focused on the evolutionary method which is the genetic algorithms, to solve the path-planning problem. This method was enhanced by the use of the artificial potential field in order to reach the goals correctly. In dead the workspace of the studied mobile robot is a free space, equipped by artificial beacons. Some of them mark a set of surveyed sites. The site that hasn't visited yet represents a goal to reach. Whereas the mobile robot must be away form these were visited. The hybridization of the genetic algorithms by the artificial potential field approach has successfully carried out. However, we have noticed that the hybrid genetic algorithm path planning run in the previous work, generated better path in ulterior generation than in the final one. In this paper, we aim to improve this hybrid algorithm by using a back-tracking on the previous generation.
Starting Page	1
Ending Page	6
File Size	286797
Page Count	6
File Format	PDF
ISBN	9781424497959
e-ISBN	9781424497966
DOI	10.1109/CCCA.2011.6031540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-03
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Path tracking Robot kinematics Force Wheels Path planning Mobile robots Artificial potential field Mobile robot Optimization Backtracking algorithm Genetic algorithms
Content Type	Text
Resource Type	Article

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