Local path optimization method for unmanned ship based on particle swarm acceleration calculation and dynamic optimal control

Dynamic local path optimization is an essential guarantee for unmanned ships to maintain navigation safety at all times. In existing studies, one noticeable limitation is the computational time, which is significantly increased with the number of obstacles, that makes it difficult to ensure the dynamic optimization of cruise paths for unmanned ships under time-varying conditions. And on this basis, a local path optimization method for unmanned ships based on particle swarm acceleration calculation and dynamic optimal control was proposed in this study. Firstly, an equation of motion of the ship was studied. The path constraints, boundary value constraints, and performance index functions were taken into account to established a mathematical model for the dynamic optimization of the local path. Secondly, based on the principles of navigation safety and timeliness of ships, the particle swarm optimization algorithm was applied to solve the model. Thirdly, the simulation results showed that the model was feasible and effective for local path optimization in dynamic navigation environments with multiple moving obstacle ships. It could provide a theoretical research foundation for studies regarding dynamic optimization of the local path for unmanned ships.