机器人辅助微创手术中避障的分层框架
A hierarchical framework for collision avoidance in robot-assisted minimally invasive surgery
AI总结：
针对机器人辅助微创手术动态杂乱环境下的安全避障难题，提出一种整合多任务约束的分层框架，采用HQP管理约束并实现任务优先级平滑切换，经仿真验证在多类避障场景中具备鲁棒性与有效性。
摘要：
微创手术（MIS）流程因机器人系统提升的精度与灵巧度而获益显著，但在这类动态且杂乱的环境中保障安全性仍是一项持续存在的挑战。本文提出了一种用于MIS避障的新型分层框架，该框架整合了多项任务，包括维持远程运动中心（RCM）约束、跟踪期望工具位姿、规避碰撞、优化可操作性以及遵守关节限位。所提方法利用分层二次规划（HQP）无缝管理这些约束，同时实现避障任务优先级之间的平滑切换。通过仿真场景开展的实验验证表明，该框架在处理涉及静态与动态障碍物以及工具间碰撞的各类场景时，具备鲁棒性与有效性。
英文摘要：
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.
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