Abstract:
The disaster relief operations during floods require time critical information of the flooded area to save lives. Finding critical regions of the disaster struck area in a limited time frame is crucial for effective relief planning. In this paper, we propose a multi-UAV based system with directed explorations of flooded area to gather time-critical ground information using deep reinforcement learning based controls. We learn an exploration policy for the multi-UAV system with limited battery for autonomous coverage of the flooded region. Further, we integrate D8 flow algorithm that approximates the water flow direction based on image pixel information of a sub-region in the UAVs’ exploration strategy. The results show that our proposed method for multi-UAV exploration of flooded area outperforms other methods from the literature. Moreover, the learnt multi-UAV exploration policy is able to generalize to unseen flooded regions without any retraining.
