Deep Reinforcement Learning Enabled UAVs Coverage Path Planning in Dam Inspection

Inspection plays a vital role in dam operations, facilitating safety assurance and environmental monitoring. As an emerging technique, multi-Unmanned Aerial Vehicle (UAV) coordination technique brings a promising prospect to dam inspection, in which the core of the technique is UAVs Coverage Path Panning (UAVs-CPP), i.e., designing flight routes for UAVs to ensure comprehensive coverage of all point of interests within a dam region. Unfortunately, the dam region inevitably encounters natural disasters such as, the thunderstorm, strong wind, and earthquake, which is called abnormal environment. The abnormal environment triggers two issues: 1) the natural disasters leads to communication obstacles between UAVs due to communication link interruption and signal loss, and 2) the coverage and energy consumption of UAVs cannot be neglected. The existing coverage path planning algorithms are insufficient in the abnormal environment. To tackle these issue, a novel Deep Reinforcement Learning (DRL)-based architecture is devised for UAVs-CPP in the dam inspection under the abnormal environment, called Trace Pheromone Multi-Agent Deep Deterministic Policy Gradient (TP-MADDPG). Specifically, we first integrate a trace pheromone mechanism and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The trace pheromone mechanism can simulate the natural pheromone, which promotes the inner indirect communication between UAVs. Furthermore, we employ a MADDPG to perform UAVs-CPP, in which a reward function is designed to conjointly optimize coverage and energy consumption. Finally, the experimental results demonstrate that TP-MADDPG shows significant UAVs-CPP performance contrasted with the advanced benchmarks.