Balancing Mission and Comprehensibility in Multi-Robot Systems for Disaster Response

Real-world disaster response or search and rescue operations require the seamless interaction of multiple teams and agencies. As multi-robot systems become more frequently used for disaster response due to the inherent dangerous environments, these systems must be controlled in way that balances the accomplishment of their mission with interaction with neighboring teams. In this paper, we address this problem by examining the balance of mission and comprehensibility. By mission, we refer to the overall task of the multi-robot system, which in a disaster response scenario is often searching an area and communicating results back to rescuers. By comprehensibility, we refer to a multi-robot system arranging itself in a way that a neighboring observer can understand what roles its members play, and react accordingly. When mission and comprehensibility are properly balanced, multi-robot teams will be more effective at working alongside one another. We propose a system of control laws for two robot roles, hubs and sensors, which provide communication and sensing, respectively. We propose additional control laws to maintain an understandable formation. Through extensive simulation of a variety of multi-robot system sizes and formations, we examine the effect of balancing mission and comprehensibility on concrete metrics for sensor coverage and role understanding.