Path Planning Method for Unmanned Aerial Vehicle Based on Signal Reception Strength

With the widespread application of UAVs (Unmanned Aerial Vehicle) in military and civil fields, the related path planning problems have also received extensive attention from researchers in various countries, but the existing algorithms may have many problems, such as local optimization and slow convergence speed. In this paper, the UAV path planning method based on signal reception intensity is studied, and the UAV automatic path planning is guided by combining the Boltzmann probability selection strategy, and the principle of “guided reinforcement” is introduced to enhance the learning probability of Q-Learning algorithm according to the characteristics of the UAV flight environment, which improves the convergence speed of UAV path planning, greatly reduces the number of iterations of the control method, and improves the efficiency of UAV real-time path planning. In the future, UAV path planning systems based on signal strength are expected to play a crucial role in the UAV’s execution of combat missions.