AI 中文总结
本研究提出基于能力图的人形机器人基座与工具安装联合优化框架，在三种手术中评估可达性，证明其优于基线方法，并揭示近端应用潜力与局限。
AI 中文摘要
人形机器人技术的快速发展激发了人们将人形机器人应用于医疗和临床任务的日益浓厚的兴趣。然而，当代人形机器人在多大程度上能满足机器人辅助腹腔镜手术的运动学需求，目前仍不清楚。在本工作中，我们通过对工作空间和机器人设置配置的定量分析，探讨了最优机器人定位的问题。我们提出了一种基于能力图的机器人设置框架，该框架优化人形机器人的基座放置和工具安装方向，以在考虑工具尖端运动学和远程运动中心（RCM）约束的同时，最大化双臂人形机器人的可达性。我们评估了三种具有不同身体尺寸和运动学冗余度的人形机器人平台，针对三种代表性普外科手术（胆囊切除术、腹股沟疝修补术和袖状胃切除术）的工作空间可达性进行了评估。所提出的基座放置与工具安装的联合优化始终优于仅优化基座的方法和启发式基线。对于以相对较小且重叠最少的工作空间为特征的胆囊切除术和腹股沟疝修补术，人形机器人的可达性接近90%。对于袖状胃切除术中较大且重叠的多臂端口工作空间，人形机器人的覆盖率则显著较低。这些结果量化了人形机器人在近期内用于特定腹腔镜手术的前景，并阐明了为更广泛部署必须解决的关键局限性。
英文摘要
Rapid advances in humanoid robotics have motivated growing interest in the application of humanoids for healthcare and clinical tasks. However, it remains unclear how close contemporary humanoids are to meeting the kinematic demands of robot-assisted laparoscopic surgery. In this work, we address the question of optimal robot positioning through a quantitative analysis of workspace and robot setup configurations. We present a capability-map-based robot setup framework that optimizes humanoid base placement and tool mounting orientation to maximize bimanual humanoid reachability while accounting for tool-tip kinematics and remote-center-of-motion (RCM) constraints. We evaluate three humanoid platforms spanning different body dimensions and kinematic redundancy on workspace reachability for three representative general surgery procedures: cholecystectomy, inguinal hernia repair, and sleeve gastrectomy. The proposed joint optimization of base placement and tool mounting consistently outperforms base-only optimization and heuristic baselines. For cholecystectomy and inguinal hernia repair, which are characterized by relatively small and minimally overlapping workspaces, humanoid reachability approached 90%. For the larger, overlapping multi-port arm workspace of sleeve gastrectomy, humanoids yield substantially lower coverage. These results quantify the near-term promise of humanoids for selected laparoscopic procedures and clarify key limitations that must be addressed for broader deployment.
