A novel path planning method for multiple USVs to collect seabed-based data

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.

Automated summary

This paper proposes a global path planning method for collecting data from seabed-based observation networks using multiple unmanned surface vessels (USVs). The method can simultaneously solve the multiple traveling salesmen problem, close-enough traveling salesman problem, and obstacle avoidance problem. It consists of a low level probabilistic focused search method for path cost estimation, an improved partheno-genetic algorithm for task allocation, and an estimated solution method for optimal path point determination. The proposed method is verified to be valid and superior through benchmark experiments, effectively solving the path planning problem for multiple USVs in a sea area with obstacles.