基于改进RRT算法的机械臂路径规划
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摘要
针对快速探索随机树(RRT)路径规划算法缺乏导向性和规划空间增大时算法时间复杂度高的问题,提出一种目标概率偏置与步长控制的改进RRT算法(I-RRT)。I-RRT结合目标概率偏置,以一定概率使采样点偏置为目标点,提高路径规划的导向性,并引入步长控制优化算法,提高运算效率,优化路径。在MATLAB平台建立了算法的仿真实验,结果表明:I-RRT的导向性与算法时间复杂度均优于经典的RRT算法;并在ROS平台上搭建了六自由度机械臂的避障规划与控制实验,实验验证了该算法的有效性。
An improved rapidly-exploring random tree( I-RRT) algorithm with target probability offset and step size control is proposed to solve the problem that RRT path planning algorithm lacks guidance and has high time complexity when the planning space increases. I-RRT combines with target probability offset,takes a certain probability to offset the sampling point as the target point,improves the directivity of path planning,and introduces step-size control optimization algorithm to improve the operation efficiency and optimize the path. The simulation experiment of the algorithm is established on the platform of MATLAB,and the results show that the directivity and time complexity of I-RRT are better than those of classical RRT algorithm. The obstacle avoidance planning and control experiment of 6-degree of freedom( DOF) manipulator is set up on the platform of ROS,and the validity of the algorithm is verified by the experiment.
An improved rapidly-exploring random tree( I-RRT) algorithm with target probability offset and step size control is proposed to solve the problem that RRT path planning algorithm lacks guidance and has high time complexity when the planning space increases. I-RRT combines with target probability offset,takes a certain probability to offset the sampling point as the target point,improves the directivity of path planning,and introduces step-size control optimization algorithm to improve the operation efficiency and optimize the path. The simulation experiment of the algorithm is established on the platform of MATLAB,and the results show that the directivity and time complexity of I-RRT are better than those of classical RRT algorithm. The obstacle avoidance planning and control experiment of 6-degree of freedom( DOF) manipulator is set up on the platform of ROS,and the validity of the algorithm is verified by the experiment.
引文
[1]孙丰财,张亚楠,史旭华.改进的快速扩展随机树路径规划算法[J].传感器与微系统,2017,36(9):129-131,135.
[2] BURGET F,BENNEWITZ M,BURGARD W. BI2RRT*:An efficient sampling-based path planning framework for task-constrained mobile manipulation[C]∥IEEE/RSJ International Conference on Intelligent Robots&Systems,2016,(1):3714-3721.
[3] DU M B,MEI T,CHEN J J,et al. RRT-based motion planning algorithm for intelligent vehicle in complex environments[J].Robot,2015,37(4):443-450.
[4]王春华,邱立鹏,潘德文.改进蚁群算法的机器人焊接路径规划[J].传感器与微系统,2017,36(2):75-77.
[5]王道威,朱明富,刘慧.动态步长的RRT路径规划算法[J].计算机技术与发展,2016,26(3):105-107,112.
[6]周芳,朱齐丹,赵国良.基于改进快速搜索随机树法的机械手路径优化[J].机械工程学报,2011,47(11):30-35.
[7]乔慧芬.机器人路径规划算法研究[D].广州:中北大学,2015.
[8]鹿霖,谢树新. ROS环境下的机器人仿真模型构建方法研究[J].现代电子技术,2018,41(7):102-105,110.
[9]何兆楚,何元烈,曾碧. RRT与人工势场法结合的机械臂避障规划[J].工业工程,2017,20(2):56-63.
[2] BURGET F,BENNEWITZ M,BURGARD W. BI2RRT*:An efficient sampling-based path planning framework for task-constrained mobile manipulation[C]∥IEEE/RSJ International Conference on Intelligent Robots&Systems,2016,(1):3714-3721.
[3] DU M B,MEI T,CHEN J J,et al. RRT-based motion planning algorithm for intelligent vehicle in complex environments[J].Robot,2015,37(4):443-450.
[4]王春华,邱立鹏,潘德文.改进蚁群算法的机器人焊接路径规划[J].传感器与微系统,2017,36(2):75-77.
[5]王道威,朱明富,刘慧.动态步长的RRT路径规划算法[J].计算机技术与发展,2016,26(3):105-107,112.
[6]周芳,朱齐丹,赵国良.基于改进快速搜索随机树法的机械手路径优化[J].机械工程学报,2011,47(11):30-35.
[7]乔慧芬.机器人路径规划算法研究[D].广州:中北大学,2015.
[8]鹿霖,谢树新. ROS环境下的机器人仿真模型构建方法研究[J].现代电子技术,2018,41(7):102-105,110.
[9]何兆楚,何元烈,曾碧. RRT与人工势场法结合的机械臂避障规划[J].工业工程,2017,20(2):56-63.