Surgical robots optimized for specific body parts reach more spots safely
Anatomy-Aware Dexterity-Driven Design Optimization of Surgical Continuum Robots
RoboticsArtificial Intelligence
Summary
Robots used for surgery must move carefully inside the body’s unique shapes to reach and treat tricky spots. The authors created a way to design these robots by measuring how well they can reach certain areas from different directions without bumping into anything. They developed a fast method to test these designs and then used a smart process to find the best one. This new design approach worked much better for reaching cancerous growths in the colon than older methods that only looked at simple coverage.
What this means in practice
- •For medical device engineers: Create surgical robots tailored to patient-specific anatomy to improve accessibility and dexterity in minimally invasive procedures.
- •For robot motion planners: Use the presented efficient motion planner to evaluate robot designs for collision-free end-effector reachability in complex environments.
Authors
Tony Qin, Peter Connor, Khoa Dang, Carter Hatch, Caleb Rucker, Robert J. Webster, Ron Alterovitz
Abstract
Performing complex medical procedures with continuum robots requires careful selection of their geometric design parameters. The robot should have high dexterity in the specific anatomical environment of its procedure. This work presents a design optimization method that considers both dexterity and anatomy. We introduce the Reachable Volumetric Dexterous Solid Angle (RVDSA) metric as our objective, which measures the ability of a robot's end effector to reach the points in a goal volume from different directions via collision-free paths from a start configuration. We present a computationally efficient motion planner to compute this objective function for a given robotic design, and we use an asymptotically optimal simulated annealing optimizer to compute an optimized design. We applied our new method to optimize the design of a bimanual dexterous sheaths robot for performing procedures on cancerous polyps in colon anatomies, achieving a 78% higher RVDSA on average than optimizing for 3D voxel coverage alone.