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• 智能设计与数字化设计 • 上一篇    下一篇

基于分级规划策略的拟人机械臂仿人运动规划算法研究

  

  1. 1. 三明学院机电工程学院，福建 三明 365004；
    2. 北京工业大学机械工程及应用电子技术学院，北京 100124；
    3. 河南科技大学车辆与交通工程学院，河南 洛阳 471003
  • 出版日期:2018-06-30 发布日期:2018-07-10
  • 基金资助:
    国家自然科学基金项目(51475016)；福建省自然科学基金项目(2016J01741)；福建省教育厅科技项目(JAT170531)；福建省引导性项目
    (2016N0029)；三明市科技项目(2014-G-6)

On Human-Like Motion Planning Algorithm of Anthropomorphic Mechanical Arms Based on Hierarchical Planning Strategy

  1. 1. School of Mechanical & Electronic Engineering, Sanming University, Sanming Fujian 365004, China;
    2. School of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China;
    3. School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang Henan 471003, China
  • Online:2018-06-30 Published:2018-07-10

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摘要： 为使拟人机械臂具有高精度的仿人运动，提出一种通过触发条件和分级规划策略
的仿人运动新方法。将人臂运动过程离散为不同运动阶段，在每一个运动阶段都有与之对应的
规划层，在不同的规划层中，拟人机械臂的运动特点不同。利用各自的特点建立不同规划层下
的运动模型及臂姿预测指标，对拟人机械臂臂姿进行预测。最后，以NAO 机器人为实验平台，
比较所提方法与最小势能法(MTPE)的静态臂姿与动态臂姿预测，并与运动捕捉系统(OptiTrack)
采集的真实人臂运动数据进行比较。实验表明，该方法具有较小的静态臂姿和动态臂姿预测误
差，能使拟人机械臂产生高度逼真的仿人运动。

关键词: 拟人机械臂, 仿人运动, 分级规划策略, 臂姿预测

Abstract: In order to make anthropomorphic mechanical arms generate human-like movements
accurately, a novel human-like motion planning method is proposed, which combines the trigger
conditions and hierarchical planning strategy (HPS). The method decomposes the complete arm
movements into a set of different motion processes, each of which has corresponding planning
hierarchies. The anthropomorphic mechanical arms reveal different characteristics in different
planning hierarchies. The motion models and posture prediction indicators in varying planning
hierarchies are built based on the respective characteristics to predict the postures of anthropomorphic
mechanical arms. The experiment is acted on humanoid robot NAO as the platform, and then the
prediction results of static and dynamic arm postures is performed by the proposed method and the
minimum total potential energy (MTPE) are compared. In addition, the prediction results are compared with the real arm motion data collected by motion capture system (OptiTrack). The
experimental results show that the errors of static and dynamic posture prediction of proposed method
could be reduced, and the anthropomorphic mechanical arms can generate the human-like movements
accurately through the proposed method.

Key words: anthropomorphic mechanical arms, human-like movements, hierarchical planning strategy, arm posture prediction