为低成本机械臂构建可复现且物理可行的动态参数识别框架。
A Reproducible and Physically Feasible Dynamic Parameter Identification Framework for a Low-Cost Robot Arm
- 通过简化对称结构减少参数至39个，设计特定运动轨迹提升可识别性。
- 经OLS、SDP修复与闭环误差优化后，模型精度显著提升且保持物理合理性。
- 适合低预算机器人平台研发者，尤其关注模型可复现与物理可行性。
本文提出一种针对由模块化智能驱动器驱动的低成本机械臂CRANE-X7的可复现且物理可行的动态参数识别框架。为提升实际可识别性，基于近似连杆对称性移除惯性积，将刚体模型参数从65个减少至39个。识别运动基于单关节与相邻关节的结构化基元，在实际关节范围限制下手动设计。所提流程包含预处理、基于逆动力学回归的普通最小二乘法（OLS）、条件半定规划（SDP）可行性恢复，以及闭环输入误差（CLIE）精炼。40条结构化轨迹的候选解在共同主成分分析（PCA）空间中分析，选取统计中心代表模型。因统计中心未必物理合理，最终模型通过全姿态惯性矩阵正定性审计筛选，必要时采用局部后CLIE SDP修正。实验表明，参数云从OLS到SDP再到CLIE逐步集中，最终模型在保留验证轨迹高预测精度的同时满足物理可行性。结果证明该框架为低成本机器人平台提供了统计一致且物理合理的动态建模路径。
原文摘要 · Abstract (English)
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 principal component analysis (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.
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