Searching and Tracking Anomalies with Multiple Robots: A Probabilistic Approach

This paper describes a probabilistic technique to coordinate multiple robots in perimeter searching and tracking tasks, which are typical when they have to detect, and follow anomalies in an environment (e.g. Fire in a forest). The proposed method is based on particle filter technique, it uses multiple robots to fuse distributed sensor information and estimate the shape of an anomaly. Complementary sensor fusion is used to coordinate robot navigation and reduce detection time when an anomaly appears. Validation of our approach is obtained both in simulation and with real robots. Five different scenarios were designed to evaluate and compare efficiency in both exploration and tracking tasks. The results have demonstrated that, when compared to state-of-the art methods in the literature, the proposed method is able to detect anomalies with or without a-priori information and reduce the detection time.