地下鉱山における異種ロボット協調のための空間認識
Towards Spatial Perception for Heterogeneous Robot Collaboration in Subterranean Mining Environments
軽量Explorerが未知の鉱山をマッピングし言語プロンプトで鉱床を検出、その地図と3DシーングラフをInspectorに渡して近接検査を計画する協調認識パイプラインを構築し、実鉱山で検証した。
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著者: Mario Alberto Valdes Saucedo, Akash Patel, Christoforos Kanellakis, George Nikolakopoulos
分類: cs.RO
原文アブストラクト
The autonomous extraction of deep mineral deposits in abandoned underground mines is fundamentally a multi-agent integration problem. No single platform simultaneously offers the mobility to traverse kilometers of degraded drifts and the sensing payload required to characterize an ore body. This article presents the onboard perception pipeline that bridges two heterogeneous agents within the PERSEPHONE autonomous mining mission. Which consist of a lightweight Explorer robot that maps an unknown mine and generates a 3D scene graph of inspection targets, by running a zero-shot, vision-language semantic segmentation stack that detects mineral deposits directly from natural-language prompts. The map and the graph are then handed to a second Inspector robot, which carries an advanced sensing payload and uses them to plan close-range inspection viewpoints. We detail the complete pipeline, with emphasis on the geometric abstraction that turns raw detections into actionable inspection targets, spanning per-view bounding-box generation, cross-view box merging, plane fitting, and polygon extraction, and we report an extensive field validation in a subterranean test facility and in an active magnesite mine, covering both iron-vein and magnesite mineralization under realistic, perceptually degraded conditions.
関連論文
- 実世界の非構造環境における異種ロボット協調のための接地型生成知能異種ロボット協調