Research on Quadrotor UAV Path Planning Optimization Based on Multi-source Information Fusion Technology of Ant Colony Optimization Algorithm

Abstract

In order to solve the problem of the lack of perceived environmental capability of the traditional quadrotor UAV(Unmanned Aerial Vehicle), a multi-sensor information fusion quadrotor UAV path planning technique based on Ant Colony Optimization Algorithm (ACO) algorithm is proposed. The path planning problem is transformed into a penalty function optimization, and global optimization is sought through the ACO algorithm. For the adaptive function including the obstacle limit and the length of the path information, the ACO algorithm finally obtains the shortest path. Through the integration of design ideas, sensor data processing data fusion and quadrotor UAV control and other key technologies, the quadrotor UAV responded in real time according to environmental changes, realizing the self-control of the quadrotor UAV. The experimental analysis shows that the multi-sensor information fusion control system used in this paper can sense the changes of the surrounding environment in real time, adjust the path planning of the quadrotor UAV in real time, and effectively improve the reliability and work efficiency of the quadrotor UAV.