手术室机器人自动送物，提升效率与无菌安全
ORB: Operating Room Bot, Automating Operating Room Logistics through Mobile Manipulation
- 用行为树整合感知与运动规划，实现模块化控制
- 实测物品取送成功率80%，补货成功率达96%
- 适合医院手术室物流自动化场景使用
在手术室中高效运送物品可能关乎生死。尽管配送机器人已在医院楼层间运送大批物资取得成功，但实现手术室层级的物流自动化仍面临感知、效率和无菌性维护等挑战。本文提出手术室机器人（ORB），一个用于自动化手术室物流任务的机器人框架。ORB采用鲁棒的分层行为树（BT）架构，集成物体识别、场景理解与基于GPU加速的运动规划功能。主要贡献包括：(1) 通过行为树实现稳健的移动操作模块化软件架构；(2) 提出融合YOLOv7、Segment Anything Model 2（SAM2）和Grounded DINO的实时物体识别新管线；(3) 将cuRobo并行轨迹优化框架适配至实时、无碰撞的移动操作；(4) 实验验证显示，物品取送任务成功率达80%，补货操作成功率高达96%。这些成果使ORB成为可靠且可扩展的自主手术室物流系统。
原文摘要 · Abstract (English)
Efficiently delivering items to an ongoing surgery in a hospital operating room can be a matter of life or death. In modern hospital settings, delivery robots have successfully transported bulk items between rooms and floors. However, automating item-level operating room logistics presents unique challenges in perception, efficiency, and maintaining sterility. We propose the Operating Room Bot (ORB), a robot framework to automate logistics tasks in hospital operating rooms (OR). ORB leverages a robust, hierarchical behavior tree (BT) architecture to integrate diverse functionalities of object recognition, scene interpretation, and GPU-accelerated motion planning. The contributions of this paper include: (1) a modular software architecture facilitating robust mobile manipulation through behavior trees; (2) a novel real-time object recognition pipeline integrating YOLOv7, Segment Anything Model 2 (SAM2), and Grounded DINO; (3) the adaptation of the cuRobo parallelized trajectory optimization framework to real-time, collision-free mobile manipulation; and (4) empirical validation demonstrating an 80% success rate in OR supply retrieval and a 96% success rate in restocking operations. These contributions establish ORB as a reliable and adaptable system for autonomous OR logistics.
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