Can I read Convex Iteration for Distance-Geometric Inverse Kinematics on EtoBox?

Convex Iteration for Distance-Geometric Inverse Kinematics by Giamou, Matthew; Marić, Filip; Rosen, David M.; Peretroukhin, Valentin; Roy, Nicholas; Petrović, Ivan; Kelly, Jonathan is a scholarly article available to read on EtoBox.

What is Convex Iteration for Distance-Geometric Inverse Kinematics about?

Inverse kinematics (IK) is the problem of finding robot joint configurations that satisfy constraints on the position or pose of one or more end-effectors. For robots with redundant degrees of freedom, there is often an infinite, nonconvex set of solutions. The IK problem is further complicated when collision avoidance constraints are imposed by obstacles in the workspace. In general, closed-form expressions yielding feasible configurations do not exist, motivating the use of numerical solution methods. However, these approaches rely on local optimization of nonconvex problems, often requiring an accurate initialization or numerous re-initializations to converge to a valid solution. In this work, we first formulate inverse kinematics with complex workspace constraints as a convex feasibility problem whose low-rank feasible points provide exact IK solutions. We then present \texttt{CIDGIK} (Convex Iteration for Distance-Geometric Inverse Kinematics), an algorithm that solves this feasibility problem with a sequence of semidefinite programs whose objectives are designed to encourage low-rank minimizers. Our problem formulation elegantly unifies the configuration space and workspace c

Author

Giamou, Matthew; Marić, Filip; Rosen, David M.; Peretroukhin, Valentin; Roy, Nicholas; Petrović, Ivan; Kelly, Jonathan

Published

2021

Language

EN