Key points are not available for this paper at this time.
In this study, we focus on the Multi-robot Coverage Path Planning (MCPP) problem for maritime Search And Rescue (SAR) missions using a multiple Autonomous Underwater Vehicle (AUV) system, with the ultimate purpose of efficiently and accurately discovering the target from sonar images taken by Side-Scan Sonar (SSS) mounted on the AUVs. Considering the specificities of real maritime SAR projects, we propose a novel MCPP method, in which the MCPP problem is transformed into two sub-problems: Area partitioning and single-AUV coverage path planning. The structure of the task area is first defined using Morse decomposition of the spike pattern. The area partitioning problem is then formulated as an AUV ordering problem, which is solved by developing a customized backtracking method to balance the workload and to avoid segmentation of the possible target area. As for the single-AUV coverage path planning problem, the SAR-A* method is adopted, which generates a path that preferentially visits the possible target areas and reduces the number of turns to guarantee the high quality of the resulting sonar images. Simulation results demonstrate that the proposed method can maintain the workload balance and significantly improve the efficiency and accuracy of discovering the target. Moreover, our experimental results indicate that the proposed method is practical and the mentioned specificities are useful for discovering targets.
Cai et al. (Sat,) studied this question.