Research on automatic UAV path planning technology for complex terrain under neural network perspective

UAV path planning originates from robot motion planning, which is the core content of the current UAV application research and plays a key role in improving the operational capability of UAS in low-altitude complex environments. In this paper, the neural network algorithm is utilized to study the path planning problem of UAVs in complex terrain environments. A three-dimensional map model of complex terrain is created using an interpolation composition method, and an adaptation function is introduced to address the smoothness issue in path planning. The UAV’s kinematic model is created by simplifying it into a three-degree-of-freedom mass and using both proportional feedback and feedforward to determine control inputs. The neural network structure adjusts the initial point, and the neural network of an obstacle penalty function and the energy function of the entire path is constructed. With comprehensive waypoint position analysis and the help of adaptive learning factors, this paper completes the path planning for UAVs in complex terrain conditions. This paper’s algorithm for forest fire patrol can reach 100% coverage rate when applying the path planning algorithm to forest fire aviation emergency rescue scenarios. In the forest fire emergency relief material distribution, the path planned by this paper’s algorithm can effectively distribute the relief materials to five target points while successfully avoiding all the obstacles.