Path planning of UAVs based on improved ant colony system

Multi-UAV Cooperative Path Planning is important for improving the efficiency of UAVs in completing reconnaissance, surveillance, and search missions due to its parallelism and fault tolerance. This paper investigates the problem of path planning for multi-mission UAVs under complex constraints, and proposes a new method of UAV path optimization that minimizes task completion time. First, with models of separated regions and heterogeneous UAVs, the objective function and flight constraints are analyzed to fully search the best solutions for the path planning problem. Then, considering a dense target group model, a path planning method based on an Improved Ant Colony System algorithm (IACS) is proposed. The IACS algorithm is able to achieve an efficiency enhancement in time consumption on targets detection and minimize the total time of UAV mission execution by adopting a novel target exploring scheme called BI-directional simplified search strategy in the target selection process. Finally, the effectiveness of the proposed method is verified through simulation experiments with randomly generated targets.