Information-Based Control of Robots in Search-and-Rescue Missions With Human Prior Knowledge

Abstract

Multirobot systems provide a scalable and robust solution for monitoring tasks. In time-intensive missions such as search and rescue, the inclusion of a human has the potential advantage of incorporating prior knowledge about the target location or dynamics. In this article, we develop a general information-theoretic framework to control multiple autonomous robots in search and rescue missions that include a human teleoperator. Human prior knowledge is modeled to capture the target location and dynamics, and mutual-information-based control is formulated to let autonomous robots weigh between two strategies: independent search or assisting the human by staying in proximity. The control actions optimize a weighted sum of normalized mutual information calculated using particle-filtered estimates of the target and the reference robot. We implement the framework to simulate two widely different scenarios designed after search-and-rescue missions from literature, and incorporate varying levels of accuracy in human prior knowledge. Our results indicate that the mission performance depends on how robots weigh between the two strategies, with the amount of the optimal control effort shared between strategies affected by prior knowledge and the number of robots. Comparison with existing strategies points to the benefits of an information-based control in situations where human prior knowledge is inaccurate. The proposed information-theoretic abstraction of the human–robot interaction can be implemented on a wide variety of scenarios and the results highlight the role of human prior knowledge toward effective robotic assistance in time-intensive missions.