PLANNING THE OPTIMUM PATH FOR A MOBILE ROBOT USING GENETIC ALGORITHM

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Mayada F. Abdul-Halim
Wathiq Nagah Abdullah

Abstract

One aspect of interest in robotics is planning the optimum path for a mobile robot or the optimum trajectory for link movements of a stationary robot in order to increase their efficiency. The objective of this paper is to identify the sequence of steps and processes needed for construction off line path planning system using genetic algorithm (as we coined GPPS). In off-line path planning. the robot is given a map with the location of all obstacles in a given world. The goal is to construct the shortest possible path between a pre-defined start and goal positions and then follow this path without running into the obstacles. In addition to the three basic genetic operators, a new operator is proposed here which is coined as repair operator. Repair operator eliminates infeasible path segments and removes path points from nearby obstacles. However, the shortest possible path resulted from applying genetic operators and repair operator may contain overlapping and redundant segments. Hence, to eliminate these drawbacks, a new operator is proposed which is coined as enhancement operator. Eighty experiments are tested on GPPS with different cases. These cases are taken from different perspectives: number and distribution of obstacles, size of obstacies, and number of experiments per a workspace, All experiments with these different cases give, as possible, an acceptable feasible path.

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“PLANNING THE OPTIMUM PATH FOR A MOBILE ROBOT USING GENETIC ALGORITHM” (2005) Journal of Engineering, 11(02), pp. 429–439. doi:10.31026/j.eng.2005.02.15.
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Articles

How to Cite

“PLANNING THE OPTIMUM PATH FOR A MOBILE ROBOT USING GENETIC ALGORITHM” (2005) Journal of Engineering, 11(02), pp. 429–439. doi:10.31026/j.eng.2005.02.15.

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References

Podsedkowski. L.(1999). Very Well Informed A Searching Algorithm and its Application for Nonholonomic Mobile Robot Motion Planning. International Journal of Seience & Technology. 10(2). 11(1,2), pp. 33-43.

Mchenry, M. C.(1998), Slice - Based Path Planning, Ph.D. Dissertation, Faculty of the Graduate School. University of Southern California.

Konar A.(2000). Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of The Human Brain.

Kortenkamp, D., Bonasso R. P., and Murphy, R.(1997), Artificial Intelligence and Mobile Robot: Case Studies of Successful Robot Systems.

Cantu-Paz B.(1999). Designing Efficient and Accurat Parallel Genetic Algorithms, IlliGAL Technical Repori No. 99017.

Michalewicz, Z.(1999), Genetic Algorithms + Data Struetures = Evolution Programs. New York: Springer Verlag.

Salmon, R.. Slater. M.(1987): Computer Graphics: Systems and Concepts. Reading. MA: Addison Wescly

Goldbery D. E. (1989), Genetic Algorithms in Search. Optimization, and Machine Learning Addison Wesley.

Ciallardo. D., Colomina, O., Flrez, F., and Rizo, R (1998), A Genetie Algorithm for Robust Motion Planning

Abdullah. W. N. (2003), Genetie-based Path-Planning System. M.Se. Dissertaition, Department of

Computer Science. University of Baghdad.