An Efficient Multi-agent Deep Deterministic Policy Gradient-Based 3D Dynamic Coverage Algorithm

This work studies the problem of dynamic coverage control of multiple Unmanned Aerial Vehicles (UAVs) in the 3 dimensional (3D) environment. In this work, an efficient multi-agent deep deterministic policy gradient-based dynamic surface coverage (MADDPG-DSC) algorithm is proposed. In MADDPG-DSC, a digital elevation-based surface area calculation method is introduced to effectively allocate the points of interest (PoIs). Next, a cooperative trajectory control policy with multi-agent deep deterministic policy gradient is developed to guide the UAVs. Comparing with existing works, MADDPG-DSC shows better performance in terms of larger coverage rate, higher connectivity and lower energy consumption.