Abstract
The paper develops a genetic algorithm approach to path planning for a mobile robot operating in rough environments. Path planning consists of a description of the environment using a fuzzy logic framework, and a two-stage planner. A global planner determines the path that optimizes a combination of terrain roughness and path curvature. A local planner uses sensory information, and in case of detection of previously unknown and unaccounted for obstacles, performs an on-line planning to get around the newly discovered obstacle. The adaptation of the genetic operators is achieved by adjusting the probabilities of the genetic operators based on a diversity measure of the population and traversability measure of the path. Path planning for an articulate rover in a rugged Mars terrain is presented to demonstrate the effectiveness of the proposed path planner.
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Tarokh, M. (2007). Genetic Path Planning with Fuzzy Logic Adaptation for Rovers Traversing Rough Terrain. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Hybrid Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37421-3_13
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DOI: https://doi.org/10.1007/978-3-540-37421-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37419-0
Online ISBN: 978-3-540-37421-3
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