IKSPARK: An Inverse Kinematics Solver using Semidefinite Relaxation and Rank Minimization
<|reference_start|>IKSPARK: An Inverse Kinematics Solver using Semidefinite Relaxation and Rank Minimization: Inverse kinematics (IK) is a fundamental problem frequently occurred in robot control and motion planning. However, the problem is nonconvex because the kinematic map between the configuration and task spaces is generally nonlinear, which makes it challenging for fast and accurate solutions. The problem can be more complicated with the existence of different physical constraints imposed by the robot structure. In this paper, we develop an inverse kinematics solver named IKSPARK (Inverse Kinematics using Semidefinite Programming And RanK minimization) that can find solutions for robots with various structures, including open/closed kinematic chains, spherical, revolute, and/or prismatic joints. The solver works in the space of rotation matrices of the link reference frames and involves solving only convex semidefinite problems (SDPs). Specifically, the IK problem is formulated as an SDP with an additional rank-1 constraint on symmetric matrices with constant traces. The solver first solves this SDP disregarding the rank constraint to get a start point and then finds the rank-1 solution iteratively via a rank minimization algorithm with proven local convergence. Compared to other work that performs SDP relaxation for IK problems, our formulation is simpler, and uses variables with smaller sizes. We validate our approach via simulations on different robots, comparing against a standard IK method.<|reference_end|>
@article{wu2024ikspark:,
    title={IKSPARK: An Inverse Kinematics Solver using Semidefinite Relaxation and
  Rank Minimization},
    author={Liangting Wu, Roberto Tron},
    journal={arXiv preprint arXiv:2403.12235},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12235},
    primaryClass={cs.RO cs.SY eess.SY}
}