应用于无人机全局航迹规划的改进双向RRTs算法

An Improved Bidirectional RRTs Algorithm for UAV Global Path Planning

针对无人机复杂环境下的全局航迹规划问题,将人工势场法与双向RRTs(Rapidly-exploring random trees)算法结合,提出一种改进双向RRTs算法。首先,目标偏置策略引导采样点以一定概率顺着目标点生成,同时随机树新节点受到障碍物斥力和目标点引力的合力影响有效避开障碍物生长,提高航迹搜寻效率,其次对随机树的节点扩展考虑了无人机飞行性能约束条件,最后采用3阶贝塞尔函数进一步航迹优化。仿真结果表明:二维和三维复杂环境中改进双向RRTs算法相比传统RRT、双向RRTs算法航迹搜索耗时减少了71.3%、24.7%和41.0%、18.6%,验证了改进算法全局搜索能力的快速性和有效性,能很好的应用于无人机离线全局航迹规划场合。

Aiming at the global path planning problem of UAV in complex environment,an improved bidirectional RRTs algorithm is proposed by combining the artificial potential field method with bidirectional RRTs algorithm.Firstly,the target offset strategy guides the sampling points to be generated along the target points with a certain probability.At the same time,the new nodes of the random tree are affected by the combined force of obstacle repulsion and target point gravity to effectively avoid the growth of obstacles and improve the track search efficiency.Secondly,the constraints of UAV flight performance are considered for the node expansion of the random tree.Finally,the third-order Bessel function is used to further optimize the flight track.The simulation results show that compared with the traditional RRT and two-way RRTs algorithms,the improved two-way RRTs algorithm in twodimensional and three-dimensional complex environment reduces the track search time by 71.3%,24.7%,41.0%and 18.6%respectively,which verifies the rapidity and effectiveness of the global search ability of the improved algorithm and it can be well applied to the off-line global path planning of UAV.