A distributed multi-robot approach for the detection and tracking of multiple dynamic anomalies

In many cases, large area disasters could be possibly be prevented if the incipient small-scale anomalies are detected in their early stages. A way to accomplish this would be to have multiple sensors deployed in disaster prone areas to detect anomalies. However, compared to static sensor networks, robotic sensor networks offer advantages such as active sensing, large area coverage and anomaly tracking. This paper addresses the problem of coordinating and controlling multiple robots for the detection of multiple dynamic anomalies in the environment. The main contribution of the work is a combined approach for the effective exploration under uncertainty, the anomaly tracking, and the autonomous on-line allocation of agents. Robots explore the work area maintaining the history of the sensed areas to reduce redundancy and to allow for full-map coverage. When an anomaly is detected, a robot autonomously determines how to either track the anomaly or to continue the exploration of the environment, depending on the size of the anomaly, which is estimated by the length of the perimeter of the enclosing polygon. We show results of our methodology both in simulation and with actual robots which have demonstrated that robots can autonomously and distributively be allocated to track or to explore depending on the behavior of the detected anomalies.