Problem
Research questions and friction points this paper is trying to address.
Orchard Navigation
Graph-based Planning
Autonomous Driving
AGRO-Nav: Autonomous Graph-based Orchard Navigation
🤖 AI Summary
AGRO-Nav通过构建基于SLAM点云的拓扑图并使用Dijkstra搜索和Theta*算法，解决了果园中自动导航偏离行中心及碰撞风险的问题。
📝 Abstract
Orchards form semi-structured environments in which parallel tree rows create natural driving corridors, yet narrow inter-row clearance and dense foliage lead geometry-agnostic grid planners to drift off the row center and risk trunk or canopy contact. We present AGRO-Nav, an automated framework for static graph-based global planning in orchards. From tree-row lines fitted to trunk clusters in a SLAM point cloud, it builds, without any manual waypoints, a sparse topological graph of intra- and inter-row connectivity; a global route is then found by Dijkstra search on this graph, connected to the start and goal by any-angle Theta* segments, and smoothed with a cubic B-spline. In real-orchard trials, AGRO-Nav follows the row center with a mean error of about 0.08 m, far below the A* (0.31 m) and Theta* (0.43 m) shortest-path baselines, while planning roughly four to five times faster. In Isaac Sim, it attains the lowest error among A*, Theta*, and a reproduced RANSAC midline baseline and remains stable as tree density drops to 70%, where the RANSAC baseline degrades. The resulting trajectories---straight row-centered segments joined by controlled turns---suit differential-drive and four-wheel-steering platforms.
Innovation
Methods, ideas, or system contributions that make the work stand out.
graph-based planning
SLAM point cloud
Dijkstra search
cubic B-spline
autonomous navigation
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