Robot self-calibration using multiple kinematic chains -- a simulation study on the iCub humanoid robot

TL;DR

This study evaluates how using multiple kinematic chains, through self-observation and self-touch, improves robot self-calibration accuracy and efficiency on the simulated iCub humanoid robot.

Contribution

It demonstrates that multi-chain calibration enhances observability and reduces the number of poses needed for accurate calibration compared to single-chain methods.

Findings

Multi-chain calibration outperforms single-chain in accuracy and observability.

Fewer poses are needed for similar calibration quality with multi-chain methods.

Calibration of all chains' parameters achieves around 1-2 mm end-effector error.

Abstract

Mechanism calibration is an important and non-trivial task in robotics. Advances in sensor technology make affordable but increasingly accurate devices such as cameras and tactile sensors available, making it possible to perform automated self-contained calibration relying on redundant information in these sensory streams. In this work, we use a simulated iCub humanoid robot with a stereo camera system and end-effector contact emulation to quantitatively compare the performance of kinematic calibration by employing different combinations of intersecting kinematic chains -- either through self-observation or self-touch. The parameters varied were: (i) type and number of intersecting kinematic chains used for calibration, (ii) parameters and chains subject to optimization, (iii) amount of initial perturbation of kinematic parameters, (iv) number of poses/configurations used for optimization, (v) amount of measurement noise in end-effector positions / cameras. The main findings are: (1) calibrating parameters of a single chain (e.g. one arm) by employing multiple kinematic chains ("self-observation" and "self-touch") is superior in terms of optimization results as well as observability; (2) when using multi-chain calibration, fewer poses suffice to get similar performance compared to when for example only observation from a single camera is used; (3) parameters of all chains (here 86 DH parameters) can be subject to calibration simultaneously and with 50 (100) poses, end-effector error of around 2 (1) mm can be achieved; (4) adding noise to a sensory modality degrades performance of all calibrations employing the chains relying on this information.