面向机器人先遣救援人员的情境图：在拆除毒品实验室中的应用
Situational Graphs for Robotic First Responders: an application to dismantling drug labs
AI总结：
本文提出面向行为的情境图，通过融合感知驱动骨干网络与先验可操作知识，支持机器人在拆除毒品实验室中实现自主任务规划与多级别自主切换，并在真实场景中验证了有效性。
摘要：
在这项工作中，我们通过部署机器人进行初步检查，协助安全领域的专家更安全地拆除毒品实验室。能够对发现的环境采取行动是实现这种（半）自主检查的关键，例如开门或近距离查看可疑物品。我们的方法通过一种新颖的环境表示——面向行为的情境图来解决这一问题，我们在经典情境图的基础上，通过情境可供性模式将感知驱动的骨干网络与先验可操作知识相融合。将情境与机器人行为联系起来，既促进了自主任务规划，也帮助操作员理解情境。由于直接整合了可操作信息，在图上进行规划更简单、更快速，这对在线任务系统至关重要。此外，该表示方法允许人类操作员在机器人的不同自主级别之间无缝切换，从远程控制到行为执行再到完全自主探索。我们在荷兰警察训练设施的真实毒品实验室场景中使用移动Spot机器人测试了该方法的有效性，并利用结果对系统设计进行了迭代。
英文摘要：
In this work, we support experts in the safety domain with safer dismantling of drug labs, by deploying robots for the initial inspection. Being able to act on the discovered environment is key to enabling this (semi-)autonomous inspection, e.g. to open doors or take a closer at suspicious items. Our approach addresses this with a novel environmental representation, the Behavior-Oriented Situational Graph, where we extend on the classical situational graph by merging a perception-driven backbone with prior actionable knowledge via a situational affordance schema. Linking situations to robot behaviors facilitates both autonomous mission planning and situational understanding of the operator. Planning over the graph is easier and faster, since it directly incorporates actionable information, which is critical for online mission systems. Moreover, the representation allows the human operator to seamlessly transition between different levels of autonomy of the robot, from remote control to behavior execution to full autonomous exploration. We test the effectiveness of our approach in a real-world drug lab scenario at a Dutch police training facility using a mobile Spot robot and use the results to iterate on the system design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
