用手引导方式无需标定即可估计机械臂负载惯性参数。
Estimation of Payload Inertial Parameters from Human Demonstrations by Hand Guiding
- 利用手引导演示中的非接触运动段，通过已有估计算法推算负载惯性参数。
- 负载质量估计准确，质心与惯性张量受噪声和激励不足影响。
- 适合无编程基础用户灵活更换工具，提升协作机器人易用性。
随着协作机器人普及，如何让无编程经验的用户高效操作成为关键。当前编程常采用手引导等直观交互方式，但执行接触式运动需已知机械臂末端负载的惯性参数（PIP），而传统标定过程繁琐。本文提出在手引导过程中，利用演示中非接触阶段的运动数据，结合现有估计算法自动获取负载的惯性参数，从而避免专用标定流程。实验表明，负载质量估计精度较高，但质心与惯性张量受测量噪声和运动激励不足影响较大。结果验证了该方法在手引导中实现惯性参数估计的可行性，同时强调了充分负载加速度对精确估计的重要性。
原文摘要 · Abstract (English)
As the availability of cobots increases, it is essential to address the needs of users with little to no programming knowledge to operate such systems efficiently. Programming concepts often use intuitive interaction modalities, such as hand guiding, to address this. When programming in-contact motions, such frameworks require knowledge of the robot tool's payload inertial parameters (PIP) in addition to the demonstrated velocities and forces to ensure effective hybrid motion-force control. This paper aims to enable non-expert users to program in-contact motions more efficiently by eliminating the need for a dedicated PIP calibration, thereby enabling flexible robot tool changes. Since demonstrated tasks generally also contain motions with non-contact, our approach uses these parts to estimate the robot's PIP using established estimation techniques. The results show that the estimation of the payload's mass is accurate, whereas the center of mass and the inertia tensor are affected by noise and a lack of excitation. Overall, these findings show the feasibility of PIP estimation during hand guiding but also highlight the need for sufficient payload accelerations for an accurate estimation.
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