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基于无障碍凸区域的无人机在线航迹规划

李文博1,3,秦小林1,2,3,罗刚1,3   

  1. 1.中国科学院成都计算机应用研究所, 成都 610041; 2. 南昌理 工学院, 南昌 330044; 3. 中国科学院大学,北京 100049
  • 出版日期:2021-06-25 发布日期:2021-09-17

李文博, 秦小林, 罗刚. 基于无障碍凸区域的无人机在线航迹规划[J]. 系统科学与数学, 2021, 41(6): 1493-1506.

LI Wenbo, QIN Xiaolin, LUO Gang. Online Trajectory Planning of UAV Based on Convex Obstacle-Free Area[J]. Journal of Systems Science and Mathematical Sciences, 2021, 41(6): 1493-1506.

Online Trajectory Planning of UAV Based on Convex Obstacle-Free Area

LI Wenbo1,3 ,QIN Xiaolin1,2,3 ,LUO Gang1,3   

  1. 1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041; 2. Nanchang Institute of Technology, Nanchang 330044; 3. University of Chinese Academy of Sciences, Beijing 100049
  • Online:2021-06-25 Published:2021-09-17
针对多旋翼无人机的在线航迹规划问题,提出了一种基于无障碍凸 区域的方法(IRIS-Astar).该方法引入了基于概率路标图(probabilistic roadmap, PRM)的A*算法,用于 离线规划全局路径.在无人机在线航迹规划阶段,通过IRIS算法(interative regional inflation by semidefinite programming) 计算出当前航迹点的极大凸区域,找出该区域中距离当前航迹点最远的全局路径点作为局 部目标点.无人机在向局部目标点行进的过程中,实时计算当前位置的极大凸区域,并判断 局部目标点是否在该区域中,若在其中,继续向局部目标点行进;否则,重新计算局部目标点.实 验结果表明,使用文中方法可以有效解决无人机的避障问题并较大幅度地降低无人机的能耗.
A method based on obstacle-free convex area is proposed to solve the problem of UAV (Unmanned Aerial Vehicle) online trajectory planning. Firstly, A* algorithm based on probabilistic roadmap (PRM) is used to plan a global path off-line. Then, the path planned above is used for local planning by IRIS-Astar algorithm proposed in this paper. The large convex obstacle-free area of the current position is calculated by IRIS (Iterative Regional Inflation By Semidefinite Programming) algorithm, which is used to find a global path point farthest from the current track point to be taken as the local target point. As the UAV travels towards the local target point, the large convex area of the current position is calculated in real time, at the same time, whether the local target point is in this area is judged. If it is, continue to travel towards the local target point; otherwise, the local target point is recalculated. Experimental results show that compared with traditional algorithms, the proposed method can effectively solve the collision avoidance problem of UAV and greatly reduce the energy consumption of UAV.
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