Global Path Planning of Wheeled Robots Using a Multi-Objective Memetic Algorithm

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.

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