Humanoid Robot Whole-body Geometric Calibration with Embedded Sensors and a Single Plane

TL;DR

This paper introduces a practical, efficient method for humanoid robot whole-body calibration using a single plane, embedded sensors, and an optimal posture selection algorithm, significantly improving accuracy with minimal calibration postures.

Contribution

It presents a novel calibration approach combining a single-plane method with an optimal posture selection algorithm, reducing calibration effort and enhancing accuracy.

Findings

Calibration error reduced by a factor of 2.3

Only 31 optimal postures needed for effective calibration

Validated on TALOS humanoid robot with positive results

Abstract

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.