多机器人协同巡检核设施,单基地远程操控实现统一地图融合
ASAP-MO:Advanced Situational Awareness and Perception for Mission-critical Operations
- 单基地控制异构地面机器人,融合地图与定位能力
- 两台UGV在室内外环境协同作业,生成统一任务地图
- 适用于高危场景远程操作,提升应急响应中的态势感知
由于控制多自由度机器人、融合多样传感器输入以及处理通信延迟和干扰的复杂性,部署机器人任务极具挑战。在核设施巡检中,机器人可替代人类进入高辐射区域,但需精准遥操作与协调。遥操作对操作员要求高,需同时处理多路输出并确保与关键资产安全交互。当在多个环境中操作异构机器人车队时,这一挑战进一步加剧,因每台机器人具有不同的控制接口、传感系统和运行约束。本论文报告了我们如何整合机器人车队的能力——包括建图、定位和远程通信——以支持联合任务。我们模拟了暴露区域的核设施巡检场景,用灯光代表辐射源。部署了两台无人地面车辆(UGVs),分别负责室内与室外环境的测绘,均由单一基地远程控制。尽管目标不同,两台机器人最终生成了一致的统一地图,验证了多机器人协同任务的可行性。结果揭示了实际操作中的关键挑战,并为提升远程部署中的适应性与态势感知提供了洞见。
原文摘要 · Abstract (English)
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.
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