基于树行图的自主果园导航，定位更准且规划更快。
AGRO-Nav: Autonomous Graph-based Orchard Navigation
- 从点云构建树行拓扑图，自动规划全局路径。
- 真实果园中误差仅0.08米，比A*快4-5倍。
- 适合差速与四轮转向机器人，对树密变化鲁棒。
果园是半结构化环境，平行树行形成自然行车通道，但狭窄的行间间隙和密集枝叶导致几何无关的网格规划器偏离行中心，有碰撞树干或树冠的风险。本文提出AGRO-Nav，一种用于果园的静态图基全局规划自动化框架。该方法从SLAM点云中拟合出树丛簇的树行线，无需人工航点，构建内/外行连通的稀疏拓扑图；通过该图进行Dijkstra搜索，起点与终点用任意角度Theta*段连接，并用三次B样条平滑。真实果园测试中，其路径沿行中心平均误差约0.08米，远低于A*（0.31米）和Theta*（0.43米），同时规划速度提升4至5倍。在Isaac Sim仿真中，其误差低于A*、Theta*及复现的RANSAC中线基线，在树密度降至70%时仍保持稳定，而RANSAC基线性能下降。生成轨迹由直线行中心段与可控转弯段组成，适用于差速驱动与四轮转向平台。
原文摘要 · Abstract (English)
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.
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