用单平面和嵌入式传感器，31个姿势即可完成人形机器人全身标定。
Humanoid Robot Whole-body Geometric Calibration with Embedded Sensors and a Single Plane
- 通过单平面与力传感器，结合阻抗控制器实现免人工自动标定。
- 仅用31个最优姿态，全链路误差降低2.3倍，优于出厂模型。
- 提出IROC算法，智能筛选最少有效标定姿态，提升效率。
传统人形机器人全身几何标定依赖复杂实验，耗时且难以实施。本文提出一种新方法：利用单平面、嵌入式力传感器和阻抗控制器，实现无需人工干预的全自动全身运动学标定。针对人形机器人结构复杂性，提出IROC（信息排序优化标定姿态选择算法），从候选姿态池中构建归一化加权信息矩阵，自动确定最少数量的最优标定姿态。在TALOS人形机器人上验证，仅通过31个姿态、机器人夹爪三点接触桌面，即完成全链路标定。交叉验证显示，平均均方根误差相比出厂模型降低2.3倍。
原文摘要 · Abstract (English)
Whole-body geometric calibration of humanoid robots using classical robot calibration methods is a timeconsuming and experimentally burdensome task. However, despite its significance for accurate control and simulation, it is often overlooked in the humanoid robotics community. To address this issue, we propose a novel practical method that utilizes a single plane, embedded force sensors, and an admittance controller to calibrate the whole-body kinematics of humanoids without requiring manual intervention. Given the complexity of humanoid robots, it is crucial to generate and determine a minimal set of optimal calibration postures. To do so, we propose a new algorithm called IROC (Information Ranking algorithm for selecting Optimal Calibration postures). IROC requires a pool of feasible candidate postures to build a normalized weighted information matrix for each posture. Then, contrary to other algorithms from the literature, IROC will determine the minimal number of optimal postures that are to be played onto a robot for its calibration. Both IROC and the single-plane calibration method were experimentally validated on a TALOS humanoid robot. The total whole-body kinematics chain was calibrated using solely 31 optimal postures with 3-point contacts on a table by the robot gripper. In a cross-validation experiment, the average root-mean-square (RMS) error was reduced by a factor of 2.3 compared to the manufacturer's model.
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