Abstract
Generally workspaces of mobile robots are cluttered with obstacles of different sizes and shapes. Majority of the path planning algorithms get stuck in non-convex obstacles pertaining to local minima. Particle Swarm Optimization (PSO) is by comparison simple and readily intelligible yet a very powerful optimization technique which makes it an apt choice for path finding problems in complex environments. This paper presents a particle swarm optimization based path planning algorithm developed for finding a shortest collision-free path for a mobile robot in an environment strewed with non-convex obstacles. The proposed method uses random sampling and finds the optimal path while avoiding non-convex obstacles without exhaustive search. Detailed simulation results show the functionality and effectiveness of the proposed algorithm in different scenarios.
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