Inverse Kinematics as Low-Rank Euclidean Distance Matrix Completion
Abstract
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.
Keywords
Cite
@article{arxiv.2011.04850,
title = {Inverse Kinematics as Low-Rank Euclidean Distance Matrix Completion},
author = {Filip Marić and Matthew Giamou and Ivan Petrović and Jonathan Kelly},
journal= {arXiv preprint arXiv:2011.04850},
year = {2022}
}
Comments
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'20) Workshop on Bringing Geometric Methods to Robot Learning, Optimization and Control, Las Vegas, USA, Oct. 29, 2020