Online path planning for unmanned aerial vehicles considering wireless charging
Recently,unmanned aerial vehicles(UAVs)have shown great potentials in the fields of logistics,communica-tion,military mission,disaster rescue,etc.However,the poor endurance of UAVs is a major problem that restricts their use.With the development of wireless charging,this paper proposes an online UAV path planning method considering wireless charging based on the deep reinforcement learning.The mission capability of UAVs can be improved by applying wireless charging.We first construct the UAV power consumption model and the wireless charging model.A deep neural network model with dynamic context is designed according to the power constraints of the UAV.The UAV path can be constructed by the encoder-decoder architecture of the model.The model is trained offline through deep reinforcement learning,and is applied to the online optimization of the UAV path.Experimental results show that,the solving speed of the proposed method is more than four times and a hundred times faster than traditional optimization and deep reinforcement learning methods on CPU and GPU,respectively.
deep reinforcement learningunmanned aerial vehiclesintelligent optimizationwireless charging
万方数据
维普
