论文标题
反向运动学作为低级欧几里得距离矩阵完成
Inverse Kinematics as Low-Rank Euclidean Distance Matrix Completion
论文作者
论文摘要
大多数逆运动学（IK）算法在由关节角度定义的配置空间中搜索解决方案。但是，许多机器人的运动学也可以用刚性附着点之间的距离来描述，这些距离集体形成了欧几里得距离矩阵。运动学的替代几何描述揭示了IK与低级矩阵完成问题之间的优雅等效性。我们使用这种连接来针对具有对称关节角度约束的各种铰接式机器人实现一种新型的Riemannian优化解决方案。
The majority of inverse kinematics (IK) algorithms search for solutions in a configuration space defined by joint angles. However, the kinematics of many robots can also be described in terms of distances between rigidly-attached points, which collectively form a Euclidean distance matrix. This alternative geometric description of the kinematics reveals an elegant equivalence between IK and the problem of low-rank matrix completion. We use this connection to implement a novel Riemannian optimization-based solution to IK for various articulated robots with symmetric joint angle constraints.
