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

TL;DR

This paper introduces a reproducible framework for identifying physically feasible dynamic parameters of a low-cost robot arm, combining structured trajectories, statistical analysis, and physical validation to ensure accurate and practical models.

Contribution

It proposes a novel pipeline that integrates statistical and physical validation steps for dynamic parameter identification in low-cost robotic systems.

Findings

The final model maintains high predictive accuracy on validation motions.

Statistical centrality improves parameter consistency across solutions.

Physical feasibility is ensured through inertia matrix audits and SDP corrections.

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.