Abstract
This paper presents a hybrid approach to the path planning problem of autonomous robots that combines potential field (PF) method and genetic algorithm (GA). The proposed PF+GA approach takes the strength of both potential field and genetic algorithm to find global optimal collision-free paths. In this integrated frame, the PF is designed as gradient-based searching strategy to exploit local optimal, and the GA is used to explore over the whole problem space. Different implementation strategies are examined through simulations in 2D scenarios. The conducted experiments show that global optimal path can be achieved effectively using the proposed approach with a strategy of high diversity and memorization.
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Miao, YQ., Khamis, A., Karray, F.O., Kamel, M.S. (2011). Global Optimal Path Planning for Mobile Robots Based on Hybrid Approach with High Diversity and Memorization. In: Kamel, M., Karray, F., Gueaieb, W., Khamis, A. (eds) Autonomous and Intelligent Systems. AIS 2011. Lecture Notes in Computer Science(), vol 6752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21538-4_1
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DOI: https://doi.org/10.1007/978-3-642-21538-4_1
Publisher Name: Springer, Berlin, Heidelberg
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