首页> 外文会议>Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on >An identification technique for evaluating static body segment parameters in the upper extremity
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An identification technique for evaluating static body segment parameters in the upper extremity

机译:一种评估上肢静态身体节段参数的识别技术

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In this paper we present a method for identifying the static biomechanical parameters of all three upper extremity body segments. The experiment is based on coupling the human arm with an industrial robot which is then used for imposing a specified sagittal plane trajectory. Joint angles and forces in the contact point are collected during this process. An optimization based identification procedure was developed, which assumes the upper extremity model of a 3-degree of freedom (3DOF) rigid body planar structure in a closed kinematic chain configuration with the robot. The solution is based on fitting the joint torques calculated from contact forces to those predicted by the inverse dynamic model of the linkage. In order to verify the developed identification procedure the experiment was first performed on a 2DOF mechanical arm with dimensions similar to those of the actual arm. This mechanical model was designed using CAD software that provides an accurate assessment of all necessary dynamic parameters. A suitable low velocity trajectory was imposed into all joints, with very small angular deviations. The outcome of the identification is an estimate of masses and center of gravity (COG) coordinates for the lower arm and palm segments, their products for the upper arm and the passive moments around the measured angle of all joints in the sagittal plane. Finally, the results obtained for the human arm are compared to the literature estimates which are based on average population.
机译:在本文中,我们提出了一种用于识别所有三个上肢身体节段的静态生物力学参数的方法。该实验基于将手臂与工业机器人耦合,然后将其用于施加指定的矢状平面轨迹。在此过程中会收集接触点的关节角度和作用力。开发了基于优化的识别程序,该程序假定机器人在闭合运动链配置中采用3自由度(3DOF)刚体平面结构的上肢模型。该解决方案基于将根据接触力计算得出的关节扭矩与连杆的逆动态模型预测的扭矩相匹配。为了验证开发的识别程序,首先在尺寸与实际手臂类似的2DOF机械手臂上进行了实验。该机械模型是使用CAD软件设计的,该软件可以对所有必要的动态参数进行准确的评估。一条合适的低速轨迹被施加到所有关节上,并且角度偏差很小。识别的结果是估计下臂和手掌段的质量和重心(COG)坐标,上臂的乘积以及在矢状面中所有关节的测量角度周围的被动力矩。最后,将人类手臂获得的结果与基于平均人口的文献估计值进行比较。

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