Visual-Based Joint Compliance Calibration Using Measurement Pose Optimization

Accurate calibration of robot joint compliance poses a significant challenge with limited existing research. For camera-based calibration, concurrently identifying both joint offsets and compliance errors becomes intricate due to measurement inaccuracies. To overcome this problem, this paper proposes an innovative approach that leverages measurement pose optimization. By leveraging the local product of exponentials (POE) model, our method enables the simultaneous identification of the geometric parameters (joint offsets) and non-geometric parameters (joint compliance). The introduction of a modified visual observability index minimizes sensitivity to camera errors during joint compliance calibration. Experimental results conducted on a 6R serial robot show superior accuracy compared to existing indices, validating the effectiveness of our approach.