Implicit Active Constraints for a Compliant Surgical Manipulator

Abstract

Active constraints are high-level control algorithms providing software-generated force feedback from virtual environments. When applied to surgery, they can assist surgeons in performing complex tasks by guiding their navigation pathways along narrow, possibly convoluted, surgical trajectories. This paper presents a method to generate concave tubular constraints implicitly from pre- or intra-operative data. Patient-specific constraints may be generated efficiently with the proposed scheme and readily deployed in various surgical scenarios. Furthermore, a five degree-of-freedom active constraint framework is proposed, which accounts for the entire tool shaft rather than just the end-effector, and is applicable to both static and dynamic active constraint scenarios. Experimental results on simulated surgical tasks show that this framework can improve safety and accuracy as well as reduce the perceived workload during complex surgical tasks.