dRVG: 四分割木ガイドによる未知環境での多角形ロボットの解像度完全オンライン動作計画
dRVG: Quadtree-Guided, Resolution-Complete Online Motion Planning for Polygonal Robots in Unknown Environments
未知の静的環境で多角形ロボットを目標へ導くオンライン動作計画法dRVGを提案し、四分割木で観測を効率化しつつ解像度完全性を保証、実験で有効性を示した。
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著者: Duo Zhang, Hechen Zhang, Junshan Huang, Jingjin Yu
分類: cs.RO
原文アブストラクト
We present the dynamic rotation-stacked visibility graph (dRVG), an online motion planner that guides polygonal robots to specified goals in initially unknown, static environ- ments. It merges local roadmaps from successive observations to plan collision-free translations and rotations without a uniform position grid. A spatial quadtree schedules sensing goals across regions to reduce repeated visits while retaining all orientation configurations for routing. Under exact sensing and geometric computation and star-shaped robot and envelope assumptions, dRVG with center scans is resolution-complete relative to full- map RVG at the same angular resolution. In experiments using footprint scans, dRVG solves all 140 cases across 20 difficult maps and seven angular resolutions within a 20 s planning budget, with a median planning time of 1.18 s at 360 orientation layers. Six microMVP demonstrations illustrate the complete online planning loop on a physical robot.
