Large area disasters are usually triggered by small-scale anomalies in small areas which could possibly be detected in their early stages. In this work, we study three important tasks to achieve an autonomous system that can monitor the environment and prevent catastrophes. The first task concerns the disaster detection. The challenge is to coordinate multiple robots to explore the environment in order to find anomalies. The second task concerns the subsequent disaster tracking. Once the anomaly is detected, the robots must coordinate themselves to track the behavior of the environmental boundary. In the third task, the resulting tracking information from the second task is used to estimate the current and to predict its future shape. The combination of these three tasks integrates a monitoring system that can alert and mitigate the risk suffered by human and animal beings.

Prof. Mario Fernando Montenegro Campos – Advisor (DCC – UFMG)
Prof. Renato Martins Assunção – Co-advisor (DCC – UFMG)
Prof. Ani Hsieh (Drexel University)
Prof. Luciano Cunha de Araújo Pimenta (DELT – UFMG)
Prof. Luiz Chaimowicz (DCC – UFMG)