Geometric Robot Dynamic Identification: A Convex Programming Approach
Geometric Robot Dynamic Identification: A Convex Programming Approach
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DOI：
10.1109/tro.2019.2926491
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发表时间：
2020-04
影响因子：
7.8
通讯作者：
Taeyoon Lee;Patrick M. Wensing;F. Park
中科院分区：
文献类型：
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作者：
Taeyoon Lee;Patrick M. Wensing;F. Park
最近的工作揭示了用于机器人质量惯性参数识别的约束最小二乘估计方法通常不可靠的性能，特别是对于受到噪声和不完整测量影响的高自由度系统。相反，微分几何识别方法已被证明更加准确和稳健。这些方法考虑了质量惯性参数驻留在弯曲黎曼空间中的事实，并允许以坐标不变的方式测量质量惯性属性的扰动。然而，现有几何方法的一个持续缺点是，相应的优化问题本质上是非凸的，具有许多局部极小值，并且求解起来计算量很大。在本文中，我们在相同的坐标不变黎曼几何框架下提出了一种凸公式，直接解决了几何方法的这些和其他缺陷。我们的凸公式可以产生全局最优解，减少计算量，收敛更快、更可靠，并且可以轻松包含额外的凸约束。我们的方法背后的主要思想是熵散度测量，它允许惯性参数识别问题的凸正则化。使用 3 自由度 MIT Cheetah 腿、7 自由度 AMBIDEX 肌腱驱动臂和 16 连杆铰接人体模型进行的大量实验表明，与现有向量空间方法相比，鲁棒性和通用性显着提高，同时确保快速、有保证地收敛到全局解决方案。
Recent work has shed light on the often unreliable performance of constrained least-squares estimation methods for robot mass-inertial parameter identification, particularly for high degree-of-freedom systems subject to noisy and incomplete measurements. Instead, differential geometric identification methods have proven to be significantly more accurate and robust. These methods account for the fact that the mass-inertial parameters reside in a curved Riemannian space, and allow perturbations in the mass-inertial properties to be measured in a coordinate-invariant manner. Yet, a continued drawback of existing geometric methods is that the corresponding optimization problems are inherently nonconvex, have numerous local minima, and are computationally highly intensive to solve. In this paper, we propose a convex formulation under the same coordinate-invariant Riemannian geometric framework that directly addresses these and other deficiencies of the geometric approach. Our convex formulation leads to a globally optimal solution, reduced computations, faster and more reliable convergence, and easy inclusion of additional convex constraints. The main idea behind our approach is an entropic divergence measure that allows for the convex regularization of the inertial parameter identification problem. Extensive experiments with the 3-DoF MIT Cheetah leg, the 7-DoF AMBIDEX tendon-driven arm, and a 16-link articulated human model show markedly improved robustness and generalizability vis-à-vis existing vector space methods while ensuring fast, guaranteed convergence to the global solution.