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郑凯林,韩宝玲. 基于改进TEB算法的阿克曼机器人运动规划系统[J]. 科学技术与工程, 2020, 20(10): 3997-4003.
ZHENG Kai-lin.Research of Ackerman robot motion planning system based on improved TEB algorithm[J].Science Technology and Engineering,2020,20(10):3997-4003.
基于改进TEB算法的阿克曼机器人运动规划系统
Research of Ackerman robot motion planning system based on improved TEB algorithm
投稿时间:2019-11-27  修订日期:2020-04-09
DOI:
中文关键词:  运动规划  改进TEB算法  局部路径规划;阿克曼机器人
英文关键词:motion  planning improved  TEB algorithm  local path  planning Ackerman  robot
基金项目:
     
作者单位
郑凯林 北京理工大学机械与车辆学院
韩宝玲 北京理工大学机械与车辆学院
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中文摘要:
      针对非完整约束的阿克曼机器人运动规划的问题,Christoph R?smann等人提出了Timed Elastic Band(TEB)算法用来实现机器人的局部路径规划器,TEB算法通过修饰全局路径规划生成的初始路径来优化机器人轨迹。加速度的变化率过大会使电机力矩突变引起机器人受到冲击震荡,加加速度的约束可使加速度的变化率限定在一个合理较小的范围。本文在TEB算法中缺少加加速度约束的基础上,对原始TEB算法进行改进,在轨迹优化过程中构建了具体的加加速度的约束,并在Stage仿真平台对改进TEB算法进行了仿真和在真实的阿克曼机器人上进行了实现,仿真和实验结果表明:得到的轨迹满足运动学,动力学,避开障碍物的要求而且平滑,真实机器人运动平顺。可见,改进的TEB算法适用于阿克曼机器人,规划的轨迹效果较好。
英文摘要:
      Aiming at the problem of nonholonomically constrained motion planning of Ackerman robot, a Timed Elastic Band (TEB) algorithm was proposed by Christoph R?smann et al. to implement the local path planner of the robot. The TEB algorithm optimized the robot trajectory by modifying the initial trajectory generated by global path planning. When the rate of change of the acceleration was too large, the sudden change in the torque output by the robot's chassis motor would cause the robot to be shocked. The constraint of jerk could limit the rate of change of acceleration to a reasonable range. Based on the lack of jerk con-straint in the TEB method, in the trajectory optimization process, specific jerk constraint was constructed in this paper. The improved TEB algorithm was simulated on the Stage simulation platform and implemented on a real Ackerman robot. The simulation and experimental result showed that the obtained smooth tra-jectory met the requirement of kinematics, kinodynamics, and avoided obstacles. It can be seen that the improved TEB algorithm is suitable for Ackerman robots, and the effect of planned trajectory is good.
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