Monitoring trajectory optimization for unmanned surface vessel in sailboat race

This paper aims to optimize the monitoring trajectory of unmanned surface vessel (USV) attached with optical camera in applications such as sailboat race. It can be taken as the waypoints optimization problem with the index of minimal energy consumption and obstacle avoidance under water current. Grey wolf optimizer (GWO), which is a novel intelligent method imitating the leadership hierarchy and hunting mechanism of wolf swarm, is utilized to obtain the optimal trajectory. Considering the unsatisfactory searching ability of GWO in complex scenarios, the improved grey wolf optimizer (IGWO) is then proposed by introducing the grey wolf individual memory, the nonlinear convergence factor, and the selected initial population obtained from the tangent method. Finally the simulation results demonstrate the robustness, efficiency and feasibility of IGWO in different cases.