Local Path Planning for USV Based on Improved Quantum Particle Swarm Optimization

Unmanned surface vehicle (USV) plans the global path before the mission starts. Since there are some unexpected threats during the voyage, the global path that the USV sailing along needs to be adjusted locally. In this study, a local path planning algorithm combines velocity obstacle method (VOM) with improved quantum particle swarm optimization (IQPSO) algorithm is proposed. By VOM, the USV local path planning is transformed into a multi-objective and multi-constrained optimization problem, and the IQPSO algorithm is used for optimization. IQPSO is an improved algorithm which benefits from quantum computing and quantum-behaved particle swarm optimization. The results of simulations verify that the optimal velocity and course of the USV can be obtained by IQPSO to avoid the obstacle effectively.