Multiple Task Assignment and Path Planning of a Multiple Unmanned Surface Vehicles System Based on Improved Self-Organizing Mapping and Improved Genetic Algorithm
Guoqing Xia, Xianxin Sun, Xiaoming Xia
Abstract
This paper addresses multiple task assignment and path-planning problems for a multiple unmanned surface vehicle (USVs) system. Since it is difficult to solve multi-task allocation and path planning together, we divide them into two sub-problems, multiple task allocation and path planning, and study them separately. First, to resolve the multi-task assignment problem, an improved self-organizing mapping (ISOM) is proposed. The method can allocate all tasks in the mission area, and obtain the set of task nodes that each USV needs to access. Second, aiming at the path planning of the USV accessing the task nodes, an improved genetic algorithm (IGA) with the shortest path is proposed. To avoid USV collision during navigation, an artificial potential field function (APFF) is proposed. A multiple USV system with multi-task allocation and path planning is simulated. Simulation results verify the effectiveness of the proposed algorithms.