A Self-Adaptive Robot Parameter Calibration Method via Broad-Deep Fusion Networks

Chuxin Chen, Yueke Kong, Guanglong Du, Xueqian Wang, Quanlong Guan, Xiaojun Zhu, Ziwei Wang, Peter X. Liu

Abstract: Robot errors, inherent in manufacturing processes and progressively worsening through operational wear, critically impact system precision through accumulated inaccuracies. Current calibration methods face significant challenges due to measurement limitations and the time-varying nature of errors, while conventional kinematic model-based approaches prove inadequate in addressing dynamic error components and often result in deteriorating accuracy. To overcome these limitations, this work presents an innovative compensation framework combining three key elements: 1) raw parameter-based inverse kinematics calculations that eliminate dependency on joint angle measurements; 2) a novel broad-deep fusion network (BDFN) architecture integrating broad learning system (BLS) and deep belief network (DBN) capabilities; and 3) an incremental learning mechanism for adaptive correction of time-varying errors including angular backlash. The proposed method achieves efficient error compensation using minimal end-effector (EE) data, overcoming conventional measurement limitations. Experimental results verify its effectiveness, demonstrating substantially improved positioning accuracy while maintaining real-time performance capabilities. This integrated approach successfully addresses the critical trade-off between precision and practicality in robotic error compensation, offering a robust solution to persistent challenges in industrial robotic applications.

External IDs:doi:10.1109/tim.2025.3614842