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This research advances continuum robot design by integrating anatomical constraints directly into the optimization process, significantly improving dexterity metrics for specific surgical tasks like colonoscopy. By developing a novel motion planner and using simulated annealing to maximize the Reachable Volumetric Dexterous Solid Angle, the study offers a computationally efficient framework for tailoring robotic tools to complex biological environments. The work represents a meaningful step forward in applying advanced optimization algorithms to specialized robotics challenges within the broader AI-driven scientific community.
Anatomy-Aware Dexterity-Driven Design Optimization of Surgical Continuum Robots
arXiv:2609.30745v1 Announce Type: new 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2609.30745 [cs.RO]
(or arXiv:2609.30745v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.30745 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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