This paper presents an algorithm to geometrically characterize inertial parameter identifiability for an articulated robot. The geometric approach tests identifiability across the infinite space of configurations using only a finite set of conditions and without approximation. It can be applied to general open-chain kinematic trees ranging from industrial manipulators to legged robots, and it is the first solution for this broad set of systems that is provably correct. The high-level operation of the algorithm is based on a key observation: Undetectable changes in inertial parameters can be represented as sequences of inertial transfers across the joints. Drawing on the exponential parameterization of rigid-body kinematics, undetectable inertial transfers are analyzed in terms of observability from linear systems theory. This analysis can be applied recursively, and lends an overall complexity of $O(N)$ to characterize parameter identifiability for a system of $N$ bodies. Matlab source code for the new algorithm is provided.

翻译：本文提出一种算法，用于对关节型机器人的惯性参数可辨识性进行几何刻画。该几何方法仅通过有限条件集即可在无穷位形空间上测试可辨识性，且无需近似处理。该方法可应用于从工业机械臂到足式机器人的通用开链运动树，且是首个对如此广泛系统可证明正确性的解决方案。算法的高层运作基于关键发现：惯性参数中的不可检测变化可表示为跨关节惯性传递的序列。借助刚体运动学的指数参数化，从线性系统理论的可观测性角度分析不可检测的惯性传递。该分析可递归应用，使整体复杂度达到 $O(N)$，从而刻画包含 $N$ 个刚体系统的参数可辨识性。文末给出了新算法的Matlab源代码。