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

Jacinto Colan, Ana Davila, Khusniddin Fozilov, Yasuhisa Hasegawa

TL;DR

The paper tackles safe collision avoidance in robot-assisted minimally invasive surgery by formulating a hierarchical, HQP-based controller that coherently integrates RCM constraints, precise tracking, collision avoidance, manipulability maximization, and joint-limit adherence. It uses a stack of prioritized QPs with null-space projections and a smooth transition mechanism to shift emphasis from tracking to collision avoidance as obstacles are detected, incorporating both hard and soft constraints. The approach is validated in simulation across static/dynamic obstacles and inter-tool collisions, showing accurate end-effector tracking, robust RCM maintenance, and improved manipulability without compromising safety. The results indicate practical potential for real-time RAMIS safety, with future work focusing on real-world experiments and sensory/adaptive enhancements to further improve performance in clinical environments.

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.

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

TL;DR

The paper tackles safe collision avoidance in robot-assisted minimally invasive surgery by formulating a hierarchical, HQP-based controller that coherently integrates RCM constraints, precise tracking, collision avoidance, manipulability maximization, and joint-limit adherence. It uses a stack of prioritized QPs with null-space projections and a smooth transition mechanism to shift emphasis from tracking to collision avoidance as obstacles are detected, incorporating both hard and soft constraints. The approach is validated in simulation across static/dynamic obstacles and inter-tool collisions, showing accurate end-effector tracking, robust RCM maintenance, and improved manipulability without compromising safety. The results indicate practical potential for real-time RAMIS safety, with future work focusing on real-world experiments and sensory/adaptive enhancements to further improve performance in clinical environments.

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.
Paper Structure (17 sections, 19 equations, 10 figures, 3 tables)

This paper contains 17 sections, 19 equations, 10 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. Proposed approach
  3. Hierarchical framework for surgical robot motion planning
  4. HQP controller
  5. Hierarchy of Tasks
  6. Remote center of Motion
  7. Tracking control
  8. Collision avoidance
  9. Manipulability maximization
  10. Joint limits
  11. Smooth task priority transition
  12. Experimental validation
  13. Case I: No collision avoidance
  14. Case II: Collision avoidance with static object
  15. Case III: Collision avoidance with dynamic object
  16. ...and 2 more sections

Figures (10)

  • Figure 1: Multiple surgical tools are commonly used in a constrained workspace.
  • Figure 2: Hierarchy of tasks for the proposed framework
  • Figure 3: Characterization of the collision avoidance task.
  • Figure 4: A. Simulation environment. Two surgical manipulators, introduce surgical robotic tools through trocar points. B. The kinematic chain of each surgical manipulator, comprising a 7-DOF robtic arm and a 3-DOF robotic surgical tool (RST) colan23openrst. C. Simplified collision model for each RST. The shaft and jaw links are represented as capsules.
  • Figure 5: Visualization of the experimental cases. A. A 6D circular path tracking task without obstacles. B. Tracking task with a static object. C. Fixed position with a dynamic object.
  • ...and 5 more figures