基于稀疏A*搜索和改进人工势场的无人机动态航迹规划

针对不同属性的障碍物所构成的威胁分布模型, 本文提出了一种基于稀疏A*搜索算法预规划和改进人工势场相结合的无人机动态避障算法. 该算法首先对威胁分布建立栅格化模型; 然后根据静态威胁, 基于稀疏A*搜索算法进行全局航迹规划; 最后结合预规划路径和动态威胁分布, 利用改进人工势场法完成无人机的动态避障. 仿真结果表明, 该方法能够规划出给定威胁指标下的全局最优路径并达到良好的动态规避性能.

Dynamic trajectory planning for unmanned aerial vehicle based on sparse A* search and improved artificial potential field

Based on the sparse A* search algorithm for path planning and the improved artificial potential field, we propose a method of dynamic trajectory planning for unmanned aerial vehicle(UAV) in the threat model composed of obstacles with different attributes. This method first builds a grid model of the threat distribution; and then, it makes the global path planning by sparse A* search algorithm according to the static obstacles; Finally, combining the pre-determined route and the dynamic obstacles, UAV can accomplish the dynamic trajectory planning by using the improved artificial potential field. Simulation results indicate that the proposed method can find a global optimal path with the given risk index and achieve a good performance of dynamic obstacle avoidance.