混合种群RRT无人机航迹规划方法

快速扩展随机树（rapidly-exploring random tree，RRT）无人机航迹规划方法能够快速获得满足约束要求的可行航迹，但是无法获得接近最短航迹的较优航迹。针对航迹的最优性问题，提出了混合种群RRT无人机航迹规划方法。在基于环境势场的RRT算法的基础上，设计了一种种群优化方法，通过引入自优化种群和协同优化种群改善航迹段，使算法同时具有局部和全局寻优能力。在得到航迹节点的基础上，采用B样条曲线的平滑方法生成曲率连续的可跟踪航迹。仿真结果表明，所提算法能够综合考虑无人机航程代价和雷达威胁代价，快速地收敛得到接近最优且满足无人机动力学约束的可行航迹，在不同环境下也能有满意的收敛效率。

Mixed population RRT algorithm for UAV path planning

The unmanned aerial vehicle (UAV) path planning algorithm based on the rapidly-exploring random tree (RRT) can only quickly get a feasible path, but cannot obtain a near shortest path. To solve the path optimization problem, a mixed population RRT algorithm is proposed on the basis of the environmental potential field based RRT. The algorithm optimizes the path section to shorten the initial path with self-optimizing colony and synergy-optimizing colony. Meanwhile, the self-optimizing colony will search globally on the mission space, which makes the algorithm obtain a global optimal path. Afterward the B-spline is used to smooth the path node for a trackable path that meets the UAV dynamic constraints. Simulation results demonstrate that the proposed method can get a near optimal path with the fast convergence rate considering radar thread and the length of path, and the convergence efficiency is satisfactory in different mission environments.