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.
Highlights
Compared to manned surface ships, unmanned surface vehicles (USVs) have significant advantages when carrying out dangerous and boring tasks
N is the number of cluster centers in the improved selforganizing mapping (ISOM) network, and the size is consistent with the number of USVs in multi-USV system
We studied a multiple USV system for multi-task assignment and path planning
Summary
Compared to manned surface ships, unmanned surface vehicles (USVs) have significant advantages when carrying out dangerous and boring tasks. To research the multi-USV system more conveniently, it is divided into two sub-problems: multiple task assignment and path planning. In [5], an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path-planning algorithm is proposed by combing the improved self-organizing map neural network with a novel velocity synthesis approach. In the second stage, which aims at the path-planning problem of the USV accessing tasks, an improved genetic algorithm (IGA) is proposed. An improved self-organizing mapping algorithm is proposed In this case, all tasks are distributed uniformly using the ISOM, and a set of tasks corresponds to each USV. To avoid obstacles in USV path planning, an APFF based on position information measured by the sensor system is proposed.
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