Abstract: As the route constraints of the unmanned aerial vehicle (UAV) are neglected in most of the existed route planning algorithms based on reinforcement learning, the resulted route is always infeasible for the UAV. This paper proposed an efficient 3-D route planning algorithm for UAV based on Q-learning. The route constraints of UAV are efficiently used to guide the discretization of the planning space in the proposed algorithm, which not only reduces the scale of the resulted discrete planning problem, but also improves the feasibility of the resulted route for UAV. A Reward shaping mechanism, which is commonly used in reinforcement learning problem that can significantly improve the convergence property, is adopted to construct a more proper reward function. The simulation results of the typical 3-D route planning problem of UAV demonstrate that the proposed algorithm can efficiently address the 3-D route planning mission of UAV.
Key words：

Key words: unmanned aerial vehicle, three-dimensional route planning, heuristic information, route constraint, Q-learning