Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

Aiming at the problems of low stability of path planning, inability to avoid dynamic obstacles, and long path planning for multi unmanned aerial vehicles (UAV) in mountainous environment, a path planning method for UAV was proposed based on the fusion of Sparrow Search Algorithm (SSA) and Bioinspired Neural Network (BINN). The method first scans the flight environment and smoothes the surface, then raises it to obtain the safe surface, and uses SSA to find a series of nodes with the lowest comprehensive cost on the safe surface. Then, B-spline curves are used to fit these nodes, so that the planned path is smooth to meet the flight requirements of the UAV. When the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path replanning to achieve the purpose of dynamic obstacle avoidance. Computer simulation results demonstrate that the fusion algorithm can plan a collision-free path in a mountainous environment, and the planned path is smooth and short. Compared with the Artificial Bee Colony Algorithm (ABC) and Dragonfly Algorithm (DA), the fusion algorithm has obvious advantages in the stability of path planning and planned path length, and has the ability of dynamic obstacle avoidance.