A novel path planning method for multiple USVs to collect seabed-based data. (1st February 2023) - Sun, Xu; Zhang, Ling; Song, Dalei; Wu, Q.M. Jonathan
Abstract: This paper proposes a global path planning method for collecting data from seabed-based observation networks using multiple unmanned surface vessels (USVs). This method can solve the multiple traveling salesmen problem, close-enough traveling salesman problem, and obstacle avoidance problem simultaneously. The method consists of a low level where an innovative probabilistic focused search method is proposed to obtain the cost of path in an environment with obstacles, a middle level where the partheno-genetic algorithm is improved to perform task allocation for multiple USVs, and a high level where the estimated solution method is proposed to obtain the optimal path point in every communication region in a short time. In the process of task allocation, the total path length and the workload balance between USVs are considered. The validity and superiority of the proposed method is verified through several benchmark experiments. The experimental results show that this method can effectively solve the problem of path planning for multiple USVs in a sea area with obstacles. Highlights: A global path planning method for collecting data using multiple unmanned surface vessels is proposed. This method can solve multiple traveling salesmen problem and close enough traveling salesman problem simultaneously. This method can design paths for multiple USVs that are short, and as balanced as possible in terms of USV workload. This method can obtain the optimal path points in the communication region.