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.

翻译：本文提出了一种针对CRANE-X7（一种由模块化智能关节驱动的低成本机械臂）的可复现且物理可行的动态参数辨识框架。为提高实际可辨识性，根据近似连杆对称性消除惯性积，将刚体模型参数从65个缩减至39个基参数。辨识运动基于实际关节限位约束下的结构化单关节与相邻关节基元手工设计。所提流程融合了预处理、基于逆动力学回归器的普通最小二乘法（OLS）、用于可行性恢复的条件半定规划（SDP）投影，以及闭环输入误差（CLIE）精化方法。在公共主成分分析（PCA）空间中分析40组结构化轨迹的候选解，以选取统计中心性代表性模型。鉴于统计中心性本身无法保证物理可接受性，最终选定模型需通过全位姿惯性矩阵正定性审核，并在必要时通过局部化CLIE后SDP救援步骤进行修正。实验表明，参数云从OLS到SDP及CLIE阶段呈现渐进式集中，且最终接受模型在保留验证运动上具有高预测精度。这些结果展示了为低成本机器人平台构建统计一致且物理可行的动态模型的实用路径。