Title

GA-based path planning for mobile robots: An empirical evaluation of seven techniques.

SelectedWorks Author Profiles:

Alison L. Watkins

Document Type

Article

Publication Date

2013

ISSN

1796-203X

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.

Language

en_US

Publisher

Academy Publisher

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.