外观
论文arXiv 2608.257992 个月前
AGRO-Nav：基于图的自主果园导航
原文链接：AGRO-Nav: Autonomous Graph-based Orchard Navigation（arXiv 2608.25799 · 发布日期：2026-08-26）
AGRO-Nav: Autonomous Graph-based Orchard Navigation
AGRO-Nav用SLAM点云构建果园拓扑图并全局规划，在真实果园和Isaac Sim中验证行中心导航。
核心要点
- 果园树行形成自然走廊，但窄行距和密集枝叶使栅格规划易偏离行中心。
- 方法从SLAM点云中的树干簇拟合树行线，自动构建稀疏拓扑连通图。
- 全局路线由Dijkstra搜索得到，并用Theta*连接起终点、三次B样条平滑。
- 真实果园误差约0.08米且规划更快；Isaac Sim中误差最低并在70%树密度稳定。
验证范围与局限：验证范围为真实果园试验与Isaac Sim仿真中的果园导航场景。
英文摘要 (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.
作者：Ho Young Yun, Jaemin Yu, Duksu Kim
来源
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