Collision Avoidance Planning Method of USV Based on Improved Ant Colony Optimization Algorithm

In order to solve the problem of insufficient search ability of the unmanned surface vehicle (USV) collision avoidance planning algorithm, this paper proposes an improved ant colony optimization algorithm (ACO). First, aiming at the static unknown environment, in order to improve the real-time performance of USV online planning, and considering the environmental characteristics of USV operation for improving ACO to search for the optimal path, a dynamic viewable method is proposed for the local environment model. Second, according to the known dynamic environment, based on the motion velocity model and International Regulations for Preventing Collisions at Sea (COLREGS), a reverse eccentric expansion method is designed to deal with the dynamic obstacles. Then, aiming at the problem that ACO has a slow convergence speed, an improved pseudo-random proportional rule is proposed to select the ant state transition. And the wolf pack allocation principle and the maximum-minimum ant system are used to update the global pheromone to avoid the search falling into local optimum. Finally, the convergence, real-time performance, and stability of the improved ACO are verified through the simulation experiment of USV collision avoidance in the static unknown and dynamic known environment.