ASAP-MO：面向关键任务操作的高级态势感知与感知
ASAP-MO:Advanced Situational Awareness and Perception for Mission-critical Operations
AI总结：
本文通过模拟核检查场景，集成两辆UGV进行映射、定位与远程通信，验证了异构机器人车队协调任务的可行性，并指出关键操作挑战及改进方向。
摘要：
部署机器人任务可能具有挑战性，原因在于控制具有多个自由度的机器人的复杂性、融合多种感官输入以及管理通信延迟和干扰。在核检查中，机器人在评估人类存在受限的环境中至关重要，这需要精确的远程操作和协调。远程操作需要大量培训，因为操作员必须处理多个输出，同时确保与关键资产的安全交互。当在多个环境中操作异构机器人车队时，这些挑战会被放大，因为每个机器人可能具有不同的控制接口、感官系统和操作约束。在这种环境中进行高效协调仍然是一个未解决的问题。本文介绍了一份关于我们如何集成机器人车队能力（包括映射、定位和远程通信）以实现联合任务的现场报告。我们模拟了一个暴露区域的核检查场景，使用灯光代表辐射源。我们部署了两台无人地面车辆（UGV），负责映射室内和室外环境，同时从单个基站进行远程控制。尽管具有不同的操作目标，机器人产生了统一的地图输出，证明了协调多机器人任务的可行性。我们的结果突出了关键的操作挑战，并为提高远程机器人部署中的适应性和态势感知提供了见解。
英文摘要：
Deploying robotic missions can be challenging due to the complexity of controlling robots with multiple degrees of freedom, fusing diverse sensory inputs, and managing communication delays and interferences. In nuclear inspection, robots can be crucial in assessing environments where human presence is limited, requiring precise teleoperation and coordination. Teleoperation requires extensive training, as operators must process multiple outputs while ensuring safe interaction with critical assets. These challenges are amplified when operating a fleet of heterogeneous robots across multiple environments, as each robot may have distinct control interfaces, sensory systems, and operational constraints. Efficient coordination in such settings remains an open problem. This paper presents a field report on how we integrated robot fleet capabilities - including mapping, localization, and telecommunication - toward a joint mission. We simulated a nuclear inspection scenario for exposed areas, using lights to represent a radiation source. We deployed two Unmanned Ground Vehicles (UGVs) tasked with mapping indoor and outdoor environments while remotely controlled from a single base station. Despite having distinct operational goals, the robots produced a unified map output, demonstrating the feasibility of coordinated multi-robot missions. Our results highlight key operational challenges and provide insights into improving adaptability and situational awareness in remote robotic deployments.
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。
