Path planning of UAV using guided enhancement Q-learning algorithm

With the increasing application of the Unmanned Aerial Vehicle (UAV) technology, the energy consumption and computing capacity of UAV are faced with bottleneck problems, so path planning of UAV is becoming increasingly important. In many cases, the UAV cannot obtain the exact location of the target point and environmental information in advance, and thus is difficult to plan an effective flight path. To solve this problem, this paper proposes a path planning method for UAV using the guided enhancement Q-learning algorithm. This method uses Receiving Signal Strength (RSS) to define the reward value, and continuously optimizes the path by using the Q-learning algorithm. The principle of guided reinforcement is proposed to accelerate the convergence speed of the Q learning algorithm. The simulation results show that the method proposed can realize autonomous navigation and fast path planning for UAV. Compared with the traditional algorithm, it can greatly reduce the number of iterations and obtain a shorter planned path. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.