Research on USV Smooth Path Planning Method Based on IAC-QPSO Algorithm

Aiming at the problem of unmanned surface vehicle (USV) smooth path planning in complex obstacle environment, a USV smooth path planning method based on improved ant colony and quantum particle swarm optimization (IAC-QPSO) algorithm is proposed. An improved ant colony (IAC) algorithm is used to plan the global optimal polyline path, and the optimal turning regions are obtained by redundant optimization and merging strategies. The particle dimension is determined adaptively according to the number of optimal turning regions, which avoids the blindness and redundancy of size determination. To improve the quality of the initial solution, particle position initialization is carried out in the turning regions, so that it is easier to find the global optimal path. The boundary constraint is determined according to the extension of the optimal path turning regions, which greatly reduces the solution space, thus reducing the solution difficulty, and improving the search efficiency. Additionally, Levy flight, random particle and spiral search are introduced into quantum particle swarm optimization (QPSO) to enhance the optimization ability of the algorithm. Using the weighted sum of path length, safety degree and smoothness as fitness function, the optimal smooth path is obtained by the improved quantum particle swarm optimization (IQPSO) algorithm and cubic spline interpolation. Simulation results show the effectiveness of the proposed algorithm.