Problem
Research questions and friction points this paper is trying to address.
online motion planning
polygonal robots
unknown environments
collision-free navigation
resolution-complete
dRVG: Quadtree-Guided, Resolution-Complete Online Motion Planning for Polygonal Robots in Unknown Environments
🤖 AI Summary
This study addresses the inefficiency of uniform grids in online motion planning for polygonal robots operating in unknown environments by proposing the dynamic Rotating Visibility Graph (dRVG) algorithm. This method integrates quadtree-guided perception with a local roadmap merging mechanism to effectively prevent redundant exploration while preserving full-orientation configurations, with resolution completeness established under specific assumptions. Furthermore, dRVG combines a center-scanning strategy with exact geometric computation to enable real-time joint translational-rotational path search. Experimental evaluations across 140 test cases on 20 complex maps demonstrate that the proposed algorithm successfully solves all instances, achieving a median planning time of merely 1.18 seconds. The end-to-end effectiveness of the approach is further validated through deployment on a physical robot platform.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Online Motion Planning
Visibility Graph
Quadtree
Resolution-Complete
Polygonal Robot
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