基于改进RRT算法的无人机航迹规划

针对快速扩展随机树（RRT）算法用于无人机自主在线航迹规划时，只能快速获得可行的航迹，无法获得接近于最短航迹的较优航迹的缺点，提出了一种改进的RRT算法.该算法将无人机动力学约束融入到节点扩展过程中，通过改进离随机采样点最近的根节点的选取策略和引入航迹距离约束，搜索树将沿着航迹距离较短的方向朝着目标点进行扩展，使得规划出来的航迹接近最优，并采用基于B样条曲线的航迹平滑方法生成平滑可跟踪的航迹.仿真结果表明该算法能够快速地搜索安全并且满足无人机动力学约束的较优航迹.

An Improved RRT Algorithm for UAV Path Planning

To solve the problem that the basic RRT algorithm for UAV path planning can only quickly get feasible path,but cannot obtain near optimal path,an improved RRT algorithm is proposed.The algorithm takes into account the dynamic constraints of UAV,by introducing the path length constraint and improving the selection strategy for root node that is nearest to the random sample point,the search tree will explore along the direction of the near optimal path.Flyable path is generated by using B-spline curves for path smoothing.Simulation results demonstrate that this proposed method can complete UAV path planning mission quickly and effectively.