摘要
针对快速搜索随机树(RRT)算法在路径规划中存在目标导向性差、收敛速度慢、路径拐点多的问题,提出了一种改进的RRT算法。改进算法采用渐近区域采样并融合偏向目标采样机制,有效防止随机采样点的反向搜索,提高对采样空间的利用率,保证了采样的方向性;用动态圆规则化处理车辆有效避免碰撞和通过狭窄区域,采用基于三角不等式的方法对路径冗余点进行修剪,在此基础上采用三次B样条线平滑的方法对路径优化。最后通过MATLAB仿真对比分析,证明了上述算法的有效性,并且搜索次数更少,用时更短,规划出的路径效果更优。
Aiming at the problems of poor goal orientation,slow convergence speed and many inflection points in path planning of RRT algorithm,an improved RRT algorithm is proposed.The improved algorithm adopts the asymptotic region sampling and the biased target sampling mechanism,which effectively prevents the reverse search of random sampling points,improves the utilization of sampling space and ensures the direction of sampling;Dynamic circle regularization was used to deal with vehicle collision avoidance and passing through narrow area.The method based on triangle inequality was used to trim the redundant points of the path.On this basis,the method of cubic Bspline smoothing was used to optimize the path.Finally,through MATLAB simulation and comparative analysis,the effectiveness of the algorithm was proved.It shows that the search times are less,the time is shorter,and the planned path effect is better.
作者
姚相滢
许伦辉
林世城
YAO Xiang-ying;XU Lun-hui;LIN Shi-cheng(South China University of Technology,Institute of Civil Engineering and Transportation,Guangzhou Guangdong 510641,China)
出处
《计算机仿真》
北大核心
2023年第4期165-169,525,共6页
Computer Simulation
基金
国家自然科学基金委员会,青年基金项目(11702099)
国家自然科学基金(52072130)。
关键词
路径规划
渐近区域采样
动态圆
路径优化
Path planning
Asymptotic region sampling
Dynamic circle
Path optimization
