基于循环寻优RRT算法的无人机航迹规划

针对快速扩展随机树（RRT）算法在无人机在线自主航迹规划中的寻优性问题，提出基于循环寻优RRT算法。将航迹长度代价约束作为启发条件引入RRT算法，可以有效地剪除搜索空间的无用节点，获得较优航迹。通过引入已规划可行航迹的航迹长度代价约束作为下一次算法运行的启发条件，采用循环迭代策略有效地剪除搜索空间的无用节点，使得算法每次运行后的航迹长度代价减小，多次运行后最终得到的航迹接近最优航迹，充分利用航迹长度代价的启发性，克服了RRT算法的缺点，同时获得了一系列不同航迹代价的可行备选航迹，在协同任务中可以根据协同到达时间进行快速选择。仿真结果表明该算法能够快速生成安全并且满足无人机动力学约束的较优航迹。

A Cyclic Optimizing RRT Algorithm for UAV Path Planning

To solve the optimizing problem of rapidly-exploring random tree algorithm (RRT), a cyclic optimizing RRT algorithm is proposed. By introducing path length constraint into RRT algorithm as heuristic condition, useless points in search space are exterminated, near optimal path can be obtained. By introducing path length constraint of planned feasible path into next algorithm execution as heuristic condition, useless points in search space are exterminated through cyclic iterative strategy， path length is shortened after each operation. The near optimal path will be found after multiple operations, then the problem that the basic RRT algorithm for UAV path planning is overcome, path length constraint as heuristic condition sufficiency is used， meanwhile, a series of feasible alternative path are obtained, they can be quickly chosen through cooperative arrival time in coordination mission. Simulation results demonstrated that this proposed method can fast get safe and near optimal path that takes into account the dynamic constraints of UAV.