TY - JOUR
T1 - Target-driven dynamic coverage planning method for marsupial cluster system
AU - Lu, Zhiyao
AU - Liang, Chongyu
AU - Bai, Chen
AU - Wu, Weichao
AU - Pan, Aigang
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2025/3
Y1 - 2025/3
N2 - 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.
AB - 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.
KW - Coverage path planning
KW - Marsupial unmanned cluster system
KW - Multi-robot task allocation
KW - Underwater unexploded ordnance
UR - http://www.scopus.com/inward/record.url?scp=85213956816&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2024.103071
DO - 10.1016/j.aei.2024.103071
M3 - Article
AN - SCOPUS:85213956816
SN - 1474-0346
VL - 64
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
M1 - 103071
ER -