This study endeavors to analyze the GA-Fuzzy approach and is an attempt to make it more practical, real-time and less computationally intensive. A new hybrid technique of using the GA-Fuzzy approach with a local search technique is presented and its efficacy viz a viz the GA-Fuzzy approach alone is demonstrated. It makes the existing technique more robust, efficient and computationally less expensive. The GA-Fuzzy approach converts an online problem of finding an obstacle-free, time optimal path for a mobile robot to an offline problem of optimizing a fuzzy rulebase using a genetic algorithm. Simulation results demonstrating the efficacy of the new design are presented.
CITATION STYLE
Mohan, A., & Deb, K. (2002). Genetic-fuzzy approach in robot motion planning revisited: Rigorous testing and towards an implementation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2275, pp. 414–420). Springer Verlag. https://doi.org/10.1007/3-540-45631-7_56
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