A Convex Optimization Framework for Constrained Concurrent Motion Control of a Hybrid Redundant Surgical System
This work addresses motion control challenges for a specific surgical robotic system, representing an incremental improvement with domain-specific application.
The paper tackled constrained motion control for a redundant surgical system used in minimally invasive pelvic osteolysis treatment by developing a convex optimization framework with a novel ADMM-based controller, achieving global linear convergence and demonstrating robustness in simulations.
We present a constrained motion control framework for a redundant surgical system designed for minimally invasive treatment of pelvic osteolysis. The framework comprises a kinematics model of a six Degrees-of-Freedom (DoF) robotic arm integrated with a one DoF continuum manipulator as well as a novel convex optimization redundancy resolution controller. To resolve the redundancy resolution problem, formulated as a constrained l2-regularized quadratic minimization, we study and evaluate the potential use of an optimally tuned alternating direction method of multipliers (ADMM) algorithm. To this end, we prove global convergence of the algorithm at linear rate and propose expressions for the involved parameters resulting in a fast convergence. Simulations on the robotic system verified our analytical derivations and showed the capability and robustness of the ADMM algorithm in constrained motion control of our redundant surgical system.