Global Smooth Path Planning for Mobile Robots Using a Novel Adaptive Particle Swarm Optimization
Guoming Zhang, Chunyu Li, Ming Gao, Li Wei Sheng · 2019
In this paper, a novel adaptive particle swarm optimization (PSO) algorithm is proposed for the smooth path planning of mobile robots when there exist obstacles in the workspace. By computing the success rate of the swarm in the current iteration, the situation of particles is determined in the search space. In the proposed PSO algorithm, the inertia weight and acceleration coefficient can be adaptively adjusted according to the situation of the swarm. Compared with some existing PSO algorithms, the adaptive PSO can enhance the search capability, which is verified by simulations on a standard collection of benchmark functions. Finally, the adaptive PSO algorithm is applied to the smooth path planning of mobile robots with constraints in the workspace, and the results show the effectiveness of the presented algorithm.