Target-driven dynamic coverage planning method for marsupial cluster system

Abstract

Using marsupial unmanned cluster systems can significantly improve underwater unexploded ordnance (UXO) clearance through strategic planning. This study examines the planning method for these systems. Current geographic information databases provide limited insights on UXO targets, and neither coverage path planning (CPP) nor multi-robot task allocation (MRTA) alone can effectively tackle UXO clearance complexities. A target-driven planning approach is proposed to enhance the system’s performance by utilizing known target information while ensuring adequate area coverage. A multi-agent decision rule is proposed, focusing on pre-planning and agent empathy to assign new targets in scenarios with limited communication effectively. These two aspects form a target-driven dynamic coverage planning method, with simulation experiments designed to compare the time required for UXO clearance across various planning methods. The most important new thing that this study adds is a new planning method tailored to the marsupial cluster system. This method increases the effectiveness of removing underwater UXO by 0.86% to 8.96% when the target known rate is above 30.3%. In addition, the simulation results indicate a direct correlation between the utilization of known information and system efficiency improvements. The article can also further support that the more information is known, the more intelligent planning methods make sense.