Towards Safe Autonomous Surgical Tasks with Control Barrier Functions
Cristina Iacono, Paolino De Risi, Rocco Moccia, Bruno Siciliano, Fanny Ficuciello
AI summary
Problem
Surgical robots must simultaneously satisfy dynamic safety constraints and complex task objectives, but existing methods rely on pre-programmed priorities or suffer from computational and singularity issues. This paper addresses the lack of a real-time mechanism to autonomously prioritize conflicting tasks based on the evolving surgical environment.
Approach
The authors encode safety and tracking objectives as Control Barrier Functions and model geometric constraints using Dual Quaternion algebra for computational efficiency. A state-dependent, Lipschitz-continuous prioritization matrix automatically switches task priorities based on proximity to safety boundaries, resolved through a quadratic programming controller.
Key results
- Autonomous dynamic task prioritization via state-dependent Lipschitz-continuous switching
- Unified geometric constraint modeling using Dual Quaternion algebra
- Successful validation on dVRK simulator and real robot across multiple surgical sub-tasks
- Guaranteed controller continuity and constraint feasibility during priority switches
Why it matters
Enables safer, more adaptable autonomous surgical robots by dynamically managing conflicting safety and task constraints in real-time, advancing clinical robotic surgery.
Abstract
Safety is of utmost importance in surgical robots, as they operate in a complex and dynamic environment that directly impacts the patient’s health based on the surgical procedure’s success. One of the main difficulties in the control of surgical manipulators is in efficiently encoding dynamic nonlinear safety constraints into trajectory planning and robot control strategies. Control Barrier Functions (CBFs) represent a valuable control method for safety-critical environments such as the surgical one since its rigorous formulation aims at ensuring safety in controlled dynamic systems. This work represents a step forward in autonomous surgical task execution since it defines Lipschitz- continuous critical and autonomously prioritized dynamic constraints enforced through a CBF framework for the safe execution of surgical robotic tasks. The proposed framework, moreover, leverages Dual Quaternion (DQ) algebra for a unified and computationally efficient representation of geometric tasks and constraints, allowing for the straightforward definition of complex, time-varying surgical constraints. The safety framework is tested in simulation on the da Vinci Research Kit (dVRK) CoppeliaSim simulator and with the real dVRK robot in several surgical sub-tasks.