A hierarchical framework for collision avoidance in robot-assisted minimally invasive surgery

TL;DR

This paper introduces a hierarchical framework using Hierarchical Quadratic Programming to enhance collision avoidance in robot-assisted minimally invasive surgery, ensuring safety and efficiency in complex, dynamic environments.

Contribution

It presents a novel hierarchical approach that integrates multiple safety and performance tasks for collision avoidance in MIS robots, improving upon existing methods.

Findings

Robust collision avoidance in simulated scenarios

Effective handling of static and dynamic obstacles

Seamless task priority transitions

Abstract

Minimally invasive surgery (MIS) procedures benefit significantly from robotic systems due to their improved precision and dexterity. However, ensuring safety in these dynamic and cluttered environments is an ongoing challenge. This paper proposes a novel hierarchical framework for collision avoidance in MIS. This framework integrates multiple tasks, including maintaining the Remote Center of Motion (RCM) constraint, tracking desired tool poses, avoiding collisions, optimizing manipulability, and adhering to joint limits. The proposed approach utilizes Hierarchical Quadratic Programming (HQP) to seamlessly manage these constraints while enabling smooth transitions between task priorities for collision avoidance. Experimental validation through simulated scenarios demonstrates the framework's robustness and effectiveness in handling diverse scenarios involving static and dynamic obstacles, as well as inter-tool collisions.