Multiple targets traversing for unmanned surface vehicles by bundled genetic optimization and fast-marching Q-Learning

Abstract

This paper discusses the use of unmanned surface vehicles (USVs) in the ocean for traversing multiple targets and proposes a novel multiple targets traversing algorithm that takes into account the multi-level priority of target assignment and path optimization. The algorithm includes a bundled genetic optimization target assignment method and a fast marching-Q-Learning path planning method. Comparative studies with existing methods show that the proposed algorithm have efficient performance, where the calculation time for target assignment has been reduced by 45.64%, and the path length has been further optimized by 15.3%. Considering the velocities of USVs navigation, path turning motion and multi-level priorities constraints are satisfied, and collision risk is avoided in dynamic ocean environment. Hence, the proposed multiple targets traversing algorithm plays an important role in the ocean engineering field of USVs.