Abstract:
Abstract
Autonomous navigation and formation control of multi-UAV systems pose a significant challenge for the robotic systems that operate in partially observable, dynamic and continuous environments. This paper addresses the problem of multi-UAV cooperative sensing and coverage of a flood-struck region to identify serviceable paths to critical locations for waterborne vehicles (WBV) in real time. A serviceable path is defined as a location that is obstacle free and has adequate water level for possible movement of WBVs. We develop a deep reinforcement learning model to learn a cooperative multi-UAV policy for real-time coverage of a flooded region. The coverage information gathered by the UAVs captures the presence of obstacles present in the path connecting the start and target/critical locations given by the shortest Manhattan distance. This coverage information is utilized by the path planning algorithm, i.e., MEA*, to minimize the number of expansion nodes and identify a serviceable path quickly. To conserve energy, UAVs initially follow a guided path to explore the optimal route. If obstacles are encountered, the UAVs search nearby areas for an alternate path to reach the critical location(s). The proposed approach, MEA* MADDPG, is compared with other prevalent techniques from the literature over real-world inspired simulated flood environments. The results highlight the significance of the proposed model as it outperforms other techniques when compared over various performance metrics.
