AGRO-Nav：基于自主图的果园导航
AGRO-Nav: Autonomous Graph-based Orchard Navigation
- School of Computer Engineering, Korea University of Technology and Education (KOREATECH)（韩国技术教育大学计算机工程学院（KOREATECH））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对果园半结构化环境中网格规划器易偏离行中心的问题，提出 AGRO-Nav 框架，通过 SLAM 点云构建拓扑图并结合 Dijkstra 搜索等算法规划路径，在真实和仿真环境中均表现出更高精度与规划效率。
AI中文摘要：
果园属于半结构化环境，平行的果树行形成了天然的驾驶通道，但狭窄的行间间隙和茂密的 foliage 导致与几何无关的网格规划器会偏离行中心，存在碰撞树干或树冠的风险。本文提出 AGRO-Nav，一种用于果园静态基于图的全局规划自动化框架。该方法从 SLAM 点云中拟合树干簇得到的果树行线，无需任何手动航路点即可构建行内和行间连通性的稀疏拓扑图；随后通过该图上的 Dijkstra 搜索找到全局路径，通过任意角度 Theta* 段连接起点和终点，并使用三次 B 样条进行平滑处理。在真实果园试验中，AGRO-Nav 沿行中心行驶的平均误差约为 0.08 m，远低于 A*（0.31 m）和 Theta*（0.43 m）最短路径基线，规划速度约快 4 至 5 倍。在 Isaac Sim 中，AGRO-Nav 在 A*、Theta* 和复现的 RANSAC 中线基线中误差最低，且在树木密度降至 70% 时仍保持稳定，而 RANSAC 基线则出现性能下降。生成的轨迹为受控转弯连接的笔直行中心段，适用于差速驱动和四轮转向平台。
英文摘要：
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.
