Coverage path planning for multi-AUV considering ocean currents and sonar performance
Xukai Mu, Wei Gao
Abstract
Coverage path planning (CPP) for target search by autonomous unmanned vehicle (AUV) involves two crucial aspects: (1) the sonar performance of the AUV is sensitive to ocean environment, such as changes in terrain; and (2) the ocean currents significantly influence AUV dynamics AUV dynamics. To address the CPP of multiple AUVs (multi-AUV) considering both sonar performance and ocean currents, we propose a new integrated algorithm based on the improved Dijkstra algorithm, Particle Swarm Optimization (PSO), and the ELKAI Solve. First, the necessary sampling points for the area coverage are identified based on the sonar detection range at different locations, which is calculated by combining the ocean acoustics model with the sonar equation. Second, an improved Dijkstra algorithm is presented to solve the adjacency matrix of the graph formed by all sampling points under the influence of ocean currents. Third, the PSO algorithm is utilized for task allocation, and the ELKAI solver determines the optimal path for each AUV. Finally, multi-AUV path planning is achieved through iterations of the PSO algorithm and the ELKAI solver. Simulation results demonstrate the outstanding performance and robustness of our integrated algorithm.