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AGRO-Nav introduces an automated framework for navigating orchards using a static graph-based global planning approach that leverages tree-row lines from SLAM point clouds to create a topological graph. This method significantly reduces navigation error to approximately 0.08 m, outperforming traditional A* and Theta* algorithms, which exhibit errors of 0.31 m and 0.43 m, respectively. The framework not only enhances planning speed by four to five times but also maintains stability in environments with reduced tree density, making it highly effective for orchard navigation.
AGRO-Nav achieves unprecedented navigation precision in orchards, cutting error rates to just 0.08 m while planning four to five times faster than conventional methods.
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.
