解剖学的環境を考慮した手術用連続体ロボットの器用さ駆動型設計最適化
Anatomy-Aware Dexterity-Driven Design Optimization of Surgical Continuum Robots
手術用連続体ロボットの設計において、到達可能な体積と器用さを評価する新しい指標RVDSAを導入し、解剖学的環境を考慮した最適化手法を提案。大腸ポリープ手術用双腕ロボットに適用し、従来の3Dボクセル被覆率のみの最適化より78%高い器用さを実現。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Tony Qin, Peter Connor, Khoa Dang, Carter Hatch, Caleb Rucker, Robert J. Webster, Ron Alterovitz
分類: cs.RO, cs.AI
原文アブストラクト
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.