Self-Contained Kinematic Calibration of a Novel Whole-Body Artificial Skin for Human-Robot Collaboration

TL;DR

This paper introduces a self-contained accelerometer-based calibration method for a novel artificial skin with IMUs, achieving high positional accuracy for human-robot collaboration and obstacle avoidance.

Contribution

It presents a new calibration algorithm compatible with any robot and skin with IMUs, validated through simulation and real-world experiments, improving positional accuracy significantly.

Findings

Achieved sub-centimeter positional error in simulation

Demonstrated effective real-world calibration on a 7-DOF robot

Enabled obstacle avoidance using distributed proximity data

Abstract

In this paper, we present an accelerometer-based kinematic calibration algorithm to accurately estimate the pose of multiple sensor units distributed along a robot body. Our approach is self-contained, can be used on any robot provided with a Denavit-Hartenberg kinematic model, and on any skin equipped with Inertial Measurement Units (IMUs). To validate the proposed method, we first conduct extensive experimentation in simulation and demonstrate a sub-cm positional error from ground truth data --an improvement of six times with respect to prior work; subsequently, we then perform a real-world evaluation on a seven degrees-of-freedom collaborative platform. For this purpose, we additionally introduce a novel design for a stand-alone artificial skin equipped with an IMU for use with the proposed algorithm and a proximity sensor for sensing distance to nearby objects. In conclusion, in this work, we demonstrate seamless integration between a novel hardware design, an accurate calibration method, and preliminary work on applications: the high positional accuracy effectively enables to locate distributed proximity data and allows for a distributed avoidance controller to safely avoid obstacles and people without the need of additional sensing.