Volume 8 Number 8 (Aug. 2013)
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JCP 2013 Vol.8(8): 1912-1922 ISSN: 1796-203X
doi: 10.4304/jcp.8.8.1912-1922

GA-based Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques

Alison Watkins
College of Business, University of South Florida St. Petersburg, FL 33730

Abstract—Previous research suggests that genetic algorithms (GAs) offer a promising solution to path planning for mobile robots. We examine six simple GAs used in prior studies, comparing them to a new node sequence approach that includes a two-step fitness function. Through a series of repeated trials using a simple 16x16 grid, a 100x100 grid, a 600x600 Mars landscape, and a complex maze-like environment, we compare the chromosome structures and fitness functions of these seven methods. The results of our empirical testing indicate that the proposed dual goal approach, which uses a fixed length chromosome structure, outperformed both monotonic and other node sequence approaches, consistently finding a feasible path in even the most challenging of these environments.

Index Terms—Previous research suggests that genetic algorithms (GAs) offer a promising solution to path planning for mobile robots. We examine six simple GAs used in prior studies, comparing them to a new node sequence approach that includes a two-step fitness function. Through a series of repeated trials using a simple 16x16 grid, a 100x100 grid, a 600x600 Mars landscape, and a complex maze-like environment, we compare the chromosome structures and fitness functions of these seven methods. The results of our empirical testing indicate that the proposed dual goal approach, which uses a fixed length chromosome structure, outperformed both monotonic and other node sequence approaches, consistently finding a feasible path in even the most challenging of these environments.

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Cite: Alison Watkins, " GA-based Path Planning for Mobile Robots: An Empirical Evaluation of Seven Techniques," Journal of Computers vol. 8, no. 8, pp. 1912-1922, 2013.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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