四足机器人自主探测火山气体，93%以上任务成功完成
Large-Scale Autonomous Gas Monitoring for Volcanic Environments: A Legged Robot on Mount Etna
- 用四足机器人ANYmal搭配质谱仪，实现复杂地形自主巡检
- 三次任务中气体源检测成功率93%-100%，实测出SO2和CO2
- 适合地质监测、灾害预警领域，为火山智能感知提供新方案
火山气体排放是喷发活动的重要前兆。然而，获取近地表精确测量仍具高危性和后勤挑战，亟需自动化解决方案。受限于崎岖火山地形，轮式系统难以可靠执行原位气体检测，降低了其作为传感平台的实用性。本文提出一种基于四足机器人ANYmal的自主火山气体分析系统，搭载四极杆质谱仪。模块化自主架构集成任务规划接口、全局路径规划、定位框架与地形感知局部导航。我们在埃特纳山开展三次自主任务，覆盖不同地形，气体源检测成功率高达93%-100%。另有一次遥控任务中，机器人成功测量自然喷气孔，检测到二氧化硫（SO2）与二氧化碳（CO2）。从气体分析与自主性角度总结经验教训，强调自适应感知策略、全局与局部规划更紧密融合，以及硬件设计优化的必要性。
原文摘要 · Abstract (English)
Volcanic gas emissions are key precursors of eruptive activity. Yet, obtaining accurate near-surface measurements remains hazardous and logistically challenging, motivating the need for autonomous solutions. Limited mobility in rough volcanic terrain has prevented wheeled systems from performing reliable in situ gas measurements, reducing their usefulness as sensing platforms. We present a legged robotic system for autonomous volcanic gas analysis, utilizing the quadruped ANYmal, equipped with a quadrupole mass spectrometer system. Our modular autonomy stack integrates a mission planning interface, global planner, localization framework, and terrain-aware local navigation. We evaluated the system on Mount Etna across three autonomous missions in varied terrain, achieving successful gas-source detections with autonomy rates of 93-100%. In addition, we conducted a teleoperated mission in which the robot measured natural fumaroles, detecting sulfur dioxide and carbon dioxide. We discuss lessons learned from the gas-analysis and autonomy perspectives, emphasizing the need for adaptive sensing strategies, tighter integration of global and local planning, and improved hardware design.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。