一种基于改进的快速扩展随机树的工业机器人路径避障规划算法

An Improved RRT Based Obstacle Avoidance Path Planning Algorithm for Industrial Robot

针对传统快速扩展随机树(RRT)算法在机械臂的运动规划上缺乏导向性,收敛速度慢等问题,文中在传统RRT的基础上,提出了一种扩展点选择策略和自适应步长策略,并且在算法陷入局部极小值时,采用避免回归机制,快速脱离极小值.然后结合Dijkstra算法对改进算法产生的路径进行优化,得到一条优化后的路径.最后,得到的机械臂末端有效路径再通过本文的机械臂规划模块,转化为一条机械臂最优位姿路径.将该改进算法与其他算法在Matlab和ROS中进行仿真实验,实验结果表明,该算法能有效指导RRT树的生长方向,避免陷入极小值,并且提高算法的收敛速度,并且提高了机械臂在仿真中运动规划效率.

To solve the problems associated with the traditional rapidly-exploring random trees(RRT)algorithm,including the lack of orientation and the slow convergence speed in the motion planning of the robot arm,we propose an extension-point selection strategy and adaptive step-size strategy based on the traditional RRT.In addition,when the algorithm is being trapped by a local minimum,it adopts an avoidance regression mechanism to quickly remove the minimum.Lastly,the Dijkstra algorithm is used to optimize the path generated by the improved algorithm,and the effective path at the end of the manipulator is transformed into an optimal pose path by the manipulator planning module.The improved algorithm and other algorithms are simulated in Matlab and ROS.The experimental results show that the proposed algorithm can effectively guide the direction of growth of the RRT tree,prevent falling into a minimum value,and improve the convergence speed of the algorithm and the motion planning efficiency of robot arm in simulation.