A Reproducible and Physically Feasible Dynamic Parameter Identification Framework for a Low-Cost Robot Arm

2026-05-15

AI Summary

This study addresses the challenge of accurately identifying dynamic models for low-cost robotic arms, which often suffer from parameter redundancy and physical infeasibility. Focusing on the CRANE-X7 manipulator, the work proposes a reproducible, physically consistent parameter identification pipeline. It begins with a simplified rigid-body model and a structured excitation trajectory, then integrates inverse dynamics regression, ordinary least squares (OLS), semidefinite programming (SDP) feasibility projection, and closed-loop input error (CLIE) optimization. A novel two-stage filtering mechanism is introduced—combining statistical centrality-based selection with full-pose positive definiteness auditing—to ensure both statistical consistency and physical plausibility. The estimated parameters progressively converge from OLS through SDP to CLIE, achieving high prediction accuracy while rigorously adhering to physical constraints.

Abstract

This paper presents a reproducible and physically feasible dynamic parameter identification framework for CRANE-X7, a low-cost robot arm driven by modular smart actuators. To improve practical identifiability, products of inertia are removed according to approximate link symmetry, reducing the rigid-body model from 65 to 39 base parameters. Identification motions are hand-designed from structured single-joint and adjacent-joint primitives under practical joint-range limits. The proposed pipeline combines preprocessing, inverse-dynamics-regressor-based ordinary least squares (OLS), conditional semidefinite-programming (SDP) projection for feasibility recovery, and closed-loop input error (CLIE) refinement. Candidate solutions from 40 structured trajectories are analyzed in a common PCA space to select a statistically central representative model. Because statistical centrality alone does not ensure physical acceptability, the selected model is finally screened by an all-pose positive-definiteness audit of the inertia matrix and, when necessary, corrected by a localized post-CLIE SDP rescue step. Experiments show that the parameter cloud becomes progressively more concentrated from OLS to SDP and CLIE, while the final accepted model preserves high predictive accuracy on held-out validation motions. These results demonstrate a practical route to statistically coherent and physically feasible dynamic models for low-cost robot platforms.

Problem

• dynamic parameter identification
• low-cost robot arm
• physical feasibility
• reproducibility
• inertia matrix

Innovation

• dynamic parameter identification
• physically feasible modeling
• low-cost robot arm
• semidefinite programming (SDP)
• closed-loop input error (CLIE)