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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Latomb, J.-C.: Robot Motion Planning. Kluwer Academic Publishers (1991).
Shiller, Z., Gwo, Y-R.: Dynamic motion planning of autonomous vehicles. IEEE Trans. Automation and Robotics, Vol. 2, (1991) 241–249.
Vadakkepat, P., Chen, T.K.: Evolutionary artificial potential fields and their application in real time robot path planning, proc. Congress on Evolutionary Computation, San Diego, CA. (2000), pp. 256–264.
Gallardo, D., Colomnia, O.: A genetic algorithm for robust motion planning, 11th Int. Conf. on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Castellon, Spain, (1998) 1150121.
Cazangi, R. R., Figuieredo, M.: Simultaneous emergence of conflicting basic behaviors and their coordination in an evolutionary autonomous navigation systems. Proc. IEEE Conf. on Evolutionary Computation, (2002).
Xiao, J., Michalewicz, Z., Zhang, L., Trojanowski, K.: Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans. Evolutionary Computation, Vol. 1, No. 1 (1997) 18–28.
Hocaoglu, C. Sanderson, A.C.: Planning multiple paths with evolutionary speciation. IEEE Trans. Evolutionary Computation, vol. 5, No. 3, (2001) 169- 191.
Sugihara, K. Smith, J.: Genetic algorithms for adaotive motion planning of an autonomous mobile robot. Proc. IEEE Int. Conf. on Computational Intelligence in Robotics and Automation, Monterey, CA, (1997), 138–146.
Tarokh, M., Shiller, Z., Hayati, S.: A comparison of two traversability based path planners for planetary rovers. Proc. 5th Int. Symposium on Artificial Intelligence, Robotics and Automation in Space (1999) 151–165.
Tarokh, M., Chan, R.W., Song, C.: Path planning of rovers using fuzzy logic and genetic algorithm. Proc. World Automation Conf., ISORA-026, Hawaii (2000) 1–7.
Hayati, S., et al.: The Rocky 7 rover: A Mars science craft prototype. Proc. IEEE Int. Conf. Robotics and Automation (1997) 2458–2464.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer
About this chapter
Cite this chapter
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
Download citation
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
eBook Packages: EngineeringEngineering (R0)