Dynamic Active Constraints for Surgical Robots using Vector Field Inequalities

TL;DR

This paper introduces a dynamic active-constraints method based on vector field inequalities that enables real-time collision avoidance for multiple robots and moving objects in constrained surgical environments, enhancing safety and autonomy.

Contribution

The work extends vector-field-inequalities to support dynamic, multi-robot, and moving object scenarios in surgical settings, with real-time collision prevention demonstrated through experiments and simulations.

Findings

The method achieves optimal trajectory error in simulations.

Robots autonomously prevent collisions in real-time.

Framework works under teleoperation with tissue interactions.

Abstract

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but robotic aid is still underrepresented in procedures with constrained workspaces, such as deep brain neurosurgery and endonasal surgery. In these procedures, surgeons have restricted vision to areas near the surgical tooltips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings. In this work, our vector-field-inequalities method is extended to provide dynamic active-constraints to any number of robots and moving objects sharing the same workspace. The method is evaluated with experiments and simulations in which robot tools have to avoid collisions autonomously and in real-time, in a constrained endonasal surgical environment. Simulations show that with our method the combined trajectory error of two robotic systems is optimal. Experiments using a real robotic system show that the method can autonomously prevent collisions between the moving robots themselves and between the robots and the environment. Moreover, the framework is also successfully verified under teleoperation with tool-tissue interactions.