Q-learning based Path Planning Method for UAVs using Priority Shifting

Path planning is a crucial part of autonomous navigation when regarding autonomous aerial vehicles, often demanding different priorities such as the length, safety or energy consumption. Dynamic programming and geometric methods have been applied to solve this problem, but in recent years, more work has been developed using artificial intelligence approaches, such as reinforcement learning. In this paper we propose an offline path planning method for static environments using Q-learning. An optimal policy is found weighting three important factors: path length, safety and energy consumption. Due to a well balanced exploring/exploiting ratio, the proposed method can lead the agent to the desired destination starting from anywhere in the map. Simulations are done in different scenarios to address the performance of the proposed method and it showcased that the algorithm is able to find feasible paths in each scenario while regarding different set of priorities.