Abstract
This paper proposes a multi-objective memetic algorithm (MOMA) for global path planning of wheeled robots. Particularly, MOMA is designed to simultaneously optimize the path length and smoothness. MOMA is featured with novel path encoding scheme, path rectification, and specific evolutionary operators. The experimental results on simulated maps show that MOMA is efficient in planning a set of valid trade-off paths in complex environments. © 2013 Springer-Verlag.
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CITATION STYLE
Wang, F., & Zhu, Z. (2013). Global path planning of wheeled robots using a multi-objective memetic algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8206 LNCS, pp. 437–444). https://doi.org/10.1007/978-3-642-41278-3_53
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