Investigation On The UAV Path Planning Problem Of Area Reconnaissance

Unmanned Aerial reconnaissance vehicles can not only be used to reconnaissance and surveillance on the fixed targets, but also to scout about an area of interesting (ROI), including area coverage and area search. The mission planning and control system need to generate a flyable path for each UAV before starting the reconnaissance task, which can guide them to cover the ROI or search targets in the area.The UAV path planning problem of area reconnaissance was studied in this paper, including area coverage and targets search. The'Z'pattern route was applied upon the area coverage which is a relatively easier problem. When the distance between two attached camera's footprints is wider than two times of the UAV's minimal turning radius, the'Z'pattern route method needs to handle the turning problem, which was analyzed in this thesis. Some optimized solutions were present on this problem. The multi-UAV cooperative coverage problem was discussed at last.This paper focuses on the area search problem. Based on the area search model, the single UAV area search path planning problem and multi-UAV cooperative search problem were studied. An off-line path planning method was designed using the Particle Swarm Optimization Algorithm for the single UAV problem. The result of a typical example shows that this approach is able to plan an effective path. The high quality of the PSO path planning method is proved by the comparison with other path planning algorithms in addition. This thesis put forward a new path planning method of area search which combine the fast characteristic of Artificial Potential Field method and the Receding Horizon Optimization that can generate paths when running the earlier solutions. This method which can be used as an on-line algorithm is able to adapt to dynamic environment.The above two methods were expanded to the Multi-UAV cooperative area search problem. The targets detected gain function was improved to the cooperative detected gain. And the collision-avoiding should be considerate on the multi-UAV problem. There are some differences on the details between single UAV algorithm and multi-UAV algorithm. The result of examples shows that the path planning method using PSO and the RHO method guided by APF are also suitable to the multi-UAV problem. Finally, the situation when some UAVs were failed was discussed.