AUV underwater 3D path planning based on particle swarm optimization-adaptive step-size cuckoo search algorithm
Lei Wang, Jinghang Li, Junyan Qi, Junyi He · 2022
Aiming at the problems of unreachable search target, weak path finding and obstacle avoidance ability, and slow algorithm convergence speed when dealing with the 3D path planning of autonomous underwater vehicles (AUV) in the traditional cuckoo algorithm in complex waters, an AUV path planning algorithm PSO-ASCS (Particle Swarm Optimization-Adaptive Step-size Cuckoo Search Algorithm) is proposed, which combines the improved Adaptive Step-size Cuckoo Search and Particle Swarm Optimization. This research uses the idea of spatial layering to establish a three-dimensional model of complex waters to conduct path planning and obstacle avoidance experiments on the PSO-ASCS algorithm; The PSO-ASCS algorithm is tested and compared with the adaptive step size cuckoo search algorithm, standard cuckoo algorithm and particle swarm optimization by constructing a fitness function considering the three factors of path length, path smoothness and path hazard. Experiments show that the improved algorithm has strong global search ability and optimization performance, and the algorithm converges well, so that the AUV has the ability of efficient obstacle avoidance and path planning.