Research on UAV Route Planning Based on Reinforcement Learning
The energy consumption of a UAV(Unmanned Aerial Vehicle)determines the length of its operational cycle.To address the issue of low communication-to-energy consumption ratio,a reinforcement learning-based UAV path planning solution is proposed to reduce energy consumption while maintaining high communication quality.The continuous flight space is divided into multi-layer two-dimensional grids to facilitate the generation of UAV state points,and a reward function based on communication quality parameters and energy consumption parameters is established.The Q-Learning algorithm is employed to learn and obtain the path with the optimal communication-to-energy consumption ratio.Experimental results show that the path planned by this learning model can achieve a higher communication-to-energy consumption ratio,demonstrating its practical value.
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