GUIDE: グローバルグラフ推論と拡散モデルを統合した自律ロボット探索フレームワーク
GUIDE: A Diffusion-Based Autonomous Robot Exploration Framework Using Global Graph Inference
未観測領域の予測を組み込んだ領域評価型グローバルグラフと拡散ポリシーを組み合わせ、屋内環境での探索経路を効率的に計画する手法を提案した。
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著者: Zijun Che, Yinghong Zhang, Shengyi Liang, Boyu Zhou, Jun Ma, Jinni Zhou
分類: cs.RO
原文アブストラクト
Autonomous exploration in structured and complex indoor environments remains a challenging task, as existing methods often struggle to appropriately model unobserved space and plan globally efficient paths. To address these limitations, we propose GUIDE, a novel exploration framework that synergistically combines global graph inference with diffusion-based decision-making. We introduce a region-evaluation global graph representation that integrates both observed environmental data and predictions of unexplored areas, enhanced by a region-level evaluation mechanism to prioritize reliable structural inferences while discounting uncertain predictions. Building upon this enriched representation, a diffusion policy network generates stable, foresighted action sequences with significantly reduced denoising steps. Extensive simulations and real-world deployments demonstrate that GUIDE consistently outperforms state-of-the-art methods, achieving up to 18.3% faster coverage completion and a 34.9% reduction in redundant movements.
