Multi-Robot Searching with Sparse Binary Cues and Limited Space Perception

In this paper, we consider the problem of searching for a source that releases particles in a turbulent medium with searchers having binary sensors and limited space perception. To this aim, we extend an information-theoretic strategy, namely Mapless, to multiple searchers and demonstrate its efficiency both in simulation and robotic experiments. The search time is found to decay as 1/n for n cooperative robots as compared to 1/ √ n for independent robots so that significant gains in the search time are obtained with a small number of robots, e.g., n=3. Search efficiency results from pooling sensory information between robots to improve individual decision-making (three detections on average per searcher were sufficient to reach the source) while still maintaining the individual resistivity to various errors during the search. The method is robust to odometry errors and is thus relevant to robots searching in low-visibility conditions, e.g., firefighter robots exploring smoky environments.