基于动作捕捉的仿人机器人REEM-C静态姿态模型识别

Felix Aller, M. Harant, K. Mombaur
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引用次数: 2

摘要

本文提出了一种仿人机器人REEM-C的模型识别方法。与以前的工作相反,我们并不试图同时确定所有的动态参数。目前尚不清楚这种方法是否会导致优化问题中的冗余。我们故意把自己限制在非常精确地确定质心(COM)和单个刚体的质量。因此,我们没有使用持续刺激(PE)轨迹,而是基于172个静态姿势的动作捕捉和力板测量来进行识别。这样可以得到更精确的实验数据,并通过优化问题更精确地更新静态参数。惯性参数没有更新,必须使用经典方法进行调整,而是基于已经改进的静态参数。我们通过比较地面投影质心(GCOM)与基于原始模型和优化模型的模型信息的测量GCOM的距离来报告优化的性能。通过记录动态蹲下运动和分析作用在机器人浮基座上的剩余力矩和力,进一步反映了优化模型的改进。识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motion Capture Based Model Identification of the Humanoid Robot REEM-C Using Static Poses
In this paper, we describe an approach for the model identification of the humanoid robot REEM-C. In contrast to previous work, we do not attempt to determine all dynamic parameters simultaneously. It is not clear whether such approaches can lead to redundancies in the optimization problem. We deliberately restrict ourselves to a very precise determination of the center-of-mass (COM) and the mass of the individual rigid bodies. As a result, we do not use Persistent Exciting (PE) trajectories and perform the identification based on motion capture and force plate measurements of 172 static poses. This results in more accurate experimental data and allows a more precise update of static parameters by means of an optimization problem. The inertial parameters are not updated and have to be adjusted using classical approaches, but based on the already improved static parameters. We report the performance of optimization by comparing the distance of the ground-projected-center-of-mass (GCOM) against the measured GCOM from the model information of the original and optimized model for each static pose. The improvement of the optimized model is furthermore reflected by means of a recorded dynamic squat motion and by analyzing the residual torques and forces acting at the floating base of the robot. identification.
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