Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions
This addresses robot-aided rescue missions, offering incremental improvements in efficiency and robustness for multi-robot systems.
The paper tackles the problem of improving multi-robot cooperation in urban search and rescue missions by proposing a needs-driven mechanism using Behavior Trees, showing that heterogeneous systems achieve higher group utility and better performance than homogeneous ones.
This paper focuses on the teaming aspects and the role of heterogeneity in a multi-robot system applied to robot-aided urban search and rescue (USAR) missions. We propose a needs-driven multi-robot cooperation mechanism represented through a Behavior Tree structure and evaluate the system's performance in terms of the group utility and energy cost to achieve the rescue mission in a limited time. From the theoretical analysis, we prove that the needs-driven cooperation in a heterogeneous robot system enables higher group utility than a homogeneous robot system. We also perform simulation experiments to verify the proposed needs-driven collaboration and show that the heterogeneous multi-robot cooperation can achieve better performance and increase system robustness by reducing uncertainty in task execution. Finally, we discuss the application to human-robot teaming.