• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2019, Vol. 55 ›› Issue (1): 42-51.doi: 10.3901/JME.2019.01.042

• 机械动力学 • 上一篇    下一篇

机械臂高斯运动轨迹规划及操作成功概率预估计方法

祁若龙1,2, 张珂1, 周维佳2, 王铁军2   

  1. 1. 沈阳建筑大学机械工程学院 沈阳 110168;
    2. 中国科学院沈阳自动化研究所机器人学国家重点实验室 沈阳 110016
  • 收稿日期:2018-01-28 修回日期:2018-05-23 出版日期:2019-01-05 发布日期:2019-01-05
  • 通讯作者: 祁若龙(通信作者),男,1983年出生,博士,副教授,硕士研究生导师。主要研究方向为机器人自主运动与智能决策方法。E-mail:qiruolong@126.com
  • 基金资助:
    国家自然科学基金(51605474)资助项目。

Trajectory Planning and Success Probability Estimation of Operation for Gaussian Motion Manipulators

QI Ruolong1,2, ZHANG Ke1, ZHOU Weijia2, WANG Tiejun2   

  1. 1. School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168;
    2. The State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016
  • Received:2018-01-28 Revised:2018-05-23 Online:2019-01-05 Published:2019-01-05

摘要: 当机械臂系统的运动存在过程噪声,或其外部闭环反馈传感器存在观测噪声时,机械臂的单次实际运动轨迹会随机地偏离预定义轨迹,但多次重复运动时又服从一定概率分布,也就是系统存在高斯运动。以自然界最为普遍的高斯分布描述机械臂系统运动状态的非确定性。用概率论的方法结合机械臂本身的线性控制及卡尔曼滤波对机械臂可行轨迹进行规划和先验概率的评估,从而得到机械臂运动误差的先验概率估计。采用线性控制方法和卡尔曼滤波相结合,进行高斯运动系统误差建模;用高斯运动模型对预规划轨迹进行迭代估计,得到单周期轨迹点的误差分布,以此定性评估其运动过程的安全性以及定量计算机械臂到达目标区域的成功概率。通过仿真和试验数据的对比,验证了算法的有效性和实用性。

关键词: 概率, 轨迹规划, 机械臂, 最优决策

Abstract: When the manipulator's movement has process noise, or its external closed-loop feedback sensors have specific observation noise, the single actual movement trajectory of the manipulator can deviate from the predefined trajectory randomly. However, the movement error of the manipulator has a probability distribution when it repeats the same action for many times. The non-deterministic movement state is described by the Gaussian distribution which is widespread in nature. The probability theory combing with the manipulator's linear control and Kalman filter estimation is used to plan the trajectory and evaluate the apriori probability distribution of movement error of the manipulator. Firstly, Linear control method is used in combination with Kalman filter to establish error model of Gaussian motion system. Then, a predefined trajectory is assessed iteratively with the Gaussian motion model to calculates the error distributions of the entire trajectory. Through Gaussian movement prior probability estimates, the security can be estimated qualitatively and the successful probability of arriving at the object region can be calculated quantificationally. At last, the effectiveness and practicability of the algorithm proposed can be verified by the comparison between simulation and experiment data.

Key words: manipulator, optimal decision, probability, trajectory planning

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