Distributed Multi-robot Information Gathering Using Path-Based Sensors in Entropy-Weighted Voronoi Regions

Alkesh Kumar Srivastava, George P. Kontoudis, Donald Sofge, Michael W. Otte

Abstract: In this paper, we present a distributed information-gathering algorithm for multi-robot systems that use multiple path-based sensors to infer the locations of hazards within the environment. Path-based sensors output binary observations, reporting whether or not an event (like robot destruction) has occurred somewhere along a path, but without the ability to discern where along a path an event has occurred. Prior work has shown that path-based sensors can be used for search and rescue in hazardous communication-denied environments—sending robots into the environment one-at-a-time. We extend this idea to enable multiple robots to search the environment simultaneously. The search space contains targets (human survivors) amidst hazards that can destroy robots (triggering a path-based hazard sensor). We consider a case where communication from the unknown field is prohibited due to communication loss, jamming, or stealth. The search effort is distributed among multiple robots using an entropy-weighted Voronoi partitioning of the environment, such that during each search round all regions have approximately equal information entropy. In each round, every robot is assigned a region in which its search path is calculated. Numerical Monte Carlo simulations are used to compare this idea to other ways of using path-based sensors on multiple robots. The experiments show that dividing search effort using entropy-weighted Voronoi partitioning outperforms the other methods in terms of the information gathered and computational cost.