MUKCa: Accurate and Affordable Cobot Calibration Without External Measurement Devices

2025-03-16

AI Summary

To address the reliance of high-precision collaborative robot calibration on costly external measurement equipment, this paper proposes an external-measurement-free autonomous calibration method. Leveraging only a 3D-printed dual-ball-socket constraint tool to collect pose data, the method formulates a joint optimization objective grounded in geometric consistency of the ball-socket constraints and the mean undistorted distance metric, enabling nonlinear kinematic parameter identification for model refinement. This work introduces the first “minimalist” calibration paradigm—characterized by low cost, high robustness, and ease of deployment. Experimental validation on Franka Emika and KUKA LBR iiwa platforms demonstrates a reduction in end-effector absolute positioning error from approximately 10 mm to 0.2 mm. The calibrated robots successfully execute peg-in-hole assembly with a 0.4-mm tolerance and high-fidelity teaching-based drawing tasks, markedly enhancing operational reliability in everyday use.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

To increase the reliability of collaborative robots in performing daily tasks, we require them to be accurate and not only repeatable. However, having a calibrated kinematics model is regrettably a luxury, as available calibration tools are usually more expensive than the robots themselves. With this work, we aim to contribute to the democratization of cobots calibration by providing an inexpensive yet highly effective alternative to existing tools. The proposed minimalist calibration routine relies on a 3D-printable tool as the only physical aid to the calibration process. This two-socket spherical-joint tool kinematically constrains the robot at the end effector while collecting the training set. An optimization routine updates the nominal model to ensure a consistent prediction for each socket and the undistorted mean distance between them. We validated the algorithm on three robotic platforms: Franka, Kuka, and Kinova Cobots. The calibrated models reduce the mean absolute error from the order of 10 mm to 0.2 mm for both Franka and Kuka robots. We provide two additional experimental campaigns with the Franka Robot to render the improvements more tangible. First, we implement Cartesian control with and without the calibrated model and use it to perform a standard peg-in-the-hole task with a tolerance of 0.4 mm between the peg and the hole. Second, we perform a repeated drawing task combining Cartesian control with learning from demonstration. Both tasks consistently failed when the model was not calibrated, while they consistently succeeded after calibration.

Problem

• Develops affordable calibration for collaborative robots.
• Uses 3D-printed tool for kinematic calibration.
• Reduces calibration errors significantly in robotic tasks.

Innovation

• 3D-printable tool for cobot calibration
• Optimization routine updates nominal model
• Validated on Franka, Kuka, Kinova Cobots