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AGRO-Nav: Autonomous Graph-based Orchard Navigation

Ho Young Yun, Jaemin Yu, Duksu Kim

arXiv:2608.25799v1cs.RO

TL;DR

Orchard corridors are structurally regular but difficult for planners that ignore row geometry, motivating a reusable global representation. AGRO-Nav automatically builds and routes over a row graph, then connects and smooths the route; it achieves lower row-center error and faster planning than the reported baselines in real and simulated evaluations.

  • Problem

    Narrow, cluttered, and irregular orchard rows challenge planners that treat the environment as undifferentiated free space, while existing pipelines often lack an automatic reusable global topology.

    Source: §I · p. 1, A. Orchard Navigation and Mapping · p. 2

  • Method

    AGRO-Nav fits tree-row lines from a SLAM point cloud, automatically constructs an intra- and inter-row graph, routes with Dijkstra and Theta*, and smooths with a cubic B-spline.

    Source: §I · p. 1, B. Graph-Based Global Planning · p. 2, C. Topological Graph Construction · p. 3

  • Results

    0.08 m mean row-center error versus 0.31 m for A* and 0.43 m for Theta*, with planning about four to five times faster in real-orchard trials; simulation reports the lowest error at both tested densities.

    Source: §V · p. 8

  • Takeaways & Limitations

    The resulting trajectories provide repeatable, efficient, structure-aligned global planning with straight in-row segments and controlled turns suitable for differential-drive and 4WS platforms.

    Source: D. Static Graph-based Global Planning · p. 4, §V · p. 8

    Evaluation is limited to static, pre-mapped planning and a single commercial orchard site; dynamic obstacles and broader multi-site validation remain future work.

    Source: §V · p. 8

Abstract

from arXiv · show

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.

I. INTRODUCTION

AGRO-Nav addresses orchard navigation by converting SLAM-derived tree-row geometry into a reusable global topological representation. It combines automatic graph construction with row-aligned planning and evaluates the approach against local, manual, and geometry-agnostic alternatives.

  • Geometry-agnostic A* and Theta* planners optimize distance rather than row adherence, while RANSAC midline methods rely on locally visible trunks.These alternatives can cut corners, enter tree rows, or degrade when rows are sparse.Source: §I · p. 1
  • Orchard navigation combines repetitive corridor structure with narrow clearance, dense foliage, missing trees, and uneven spacing.These conditions make the row geometry only loosely defined and challenge generic planning.Source: §I · p. 1, A. Orchard Navigation and Mapping · p. 2
  • Prior systems commonly follow rows locally, use manually built or implicit graphs, or expose no routable topology from their mapping pipeline.The closest graph-based approach still manually specifies per-tree access nodes and plans shortest paths.Source: §I · p. 1
  • AGRO-Nav automatically builds a sparse, connected orchard graph from PCA-fitted tree-row lines without manual waypoints.The graph represents intra-row and inter-row connectivity for global routing.Source: §I · p. 1, A. Orchard Navigation and Mapping · p. 2

B. Graph-Based Global Planning

AGRO-Nav uses a problem-specific row graph to constrain global search to meaningful orchard routes. Its design contrasts with learned, manually specified, or distance-only graph-planning approaches.

  • A row graph bounds search to within-row travel and controlled cross-row transitions through semantically meaningful corridors.This exploits orchard structure while reducing branching compared with general sampling-based planners.Source: B. Graph-Based Global Planning · p. 2
  • Dijkstra provides deterministic global routing, Theta* shortens any-angle junction connectors, and inflation-based costs preserve vegetation clearance.Source: B. Graph-Based Global Planning · p. 2
  • AGRO-Nav constructs its graph automatically from non-learned PCA-fitted trunk geometry, avoiding labeled data and manual per-tree access nodes.Node spacing and cross-row thresholds remain tunable.Source: B. Graph-Based Global Planning · p. 2
  • Unlike the closest related method, AGRO-Nav explicitly plans for row-center adherence rather than minimizing travel distance.The related method uses learned semantic segmentation and manually specified access nodes.Source: B. Graph-Based Global Planning · p. 2

A. System Overview

The framework separates reusable offline map construction from online query planning. It converts a SLAM point cloud into a compact row graph, then connects and smooths a graph route for each start–goal request.

  • Tree trunks are segmented, PCA fits lines representing orchard rows, and nodes are sampled along centerlines between adjacent tree lines.Edges connect nodes along rows and across headlands.Source: A. System Overview · p. 2
  • Dijkstra finds a row-aligned graph route after the start and goal are snapped to their nearest graph nodes.Source: A. System Overview · p. 2
  • Offline map construction converts a 3D SLAM point cloud into a 2D map and topological graph, while online planning serves each query.Source: A. System Overview · p. 2, Figure 1 · p. 3
  • Theta* connects off-graph start and goal poses on a static cost map, after which the combined path is smoothed with a cubic B-spline.The resulting trajectory is uniformly resampled for output.Source: A. System Overview · p. 3

B. Problem Formulation and Notation

AGRO-Nav formulates orchard planning as producing one smooth, row-centered trajectory between poses through a topological abstraction. The objective balances row adherence and planning time under static-clearance and driving-region constraints.

  • The planner takes a SLAM-generated 3D point cloud, a derived 2D occupancy/cost map, detected tree lines, and start and goal poses.An optional polygon bounds the permissible driving region.Source: B. Problem Formulation and Notation · p. 3
  • The output is a smooth, uniformly resampled trajectory generated from a graph representing intra-row corridors and cross-row headland transitions.Source: B. Problem Formulation and Notation · p. 3
  • The objective minimizes lateral deviation from inter-row centerlines and planning time while maintaining static clearance and remaining within the driving region.Source: B. Problem Formulation and Notation · p. 3
  • Edges are constrained by the driving region, line-of-sight, lateral separation below τrow, and a maximum length Lmax.The trajectory must also be trackable by differential-drive and four-wheel-steering platforms.Source: B. Problem Formulation and Notation · p. 3
  • The evaluated planner assumes a static, pre-mapped orchard and does not reactively avoid dynamic or moving obstacles at runtime.Dynamic avoidance is left to future work or downstream control.Source: B. Problem Formulation and Notation · p. 3

C. Topological Graph Construction

AGRO-Nav constructs a sparse orchard graph from paired tree-row lines, placing nodes along row center axes and connecting them longitudinally and laterally under geometric constraints.

Node generation

Edge creation

D. Static Graph-based Global Planning

AGRO-Nav plans a static route by combining graph-based row travel with any-angle entry and exit connections, then smooths the complete path into a trackable trajectory.

Entry and exit path planning

Graph-level path planning

Entry and exit path planning

Path smoothing and resampling

A. Experimental Setup

The evaluation compares AGRO-Nav with grid-based and structural baselines on repeated real-world and simulated orchard routes, using row-center adherence and planning time as key measures.

Evaluation protocol

  • Each planner runs ten times per start–goal pair under identical 2D occupancy-grid conditions, with path-quality metrics averaged across evaluated pairs.Source: A. Experimental Setup · pp. 4–5

Metrics

  • Accuracy is mean lateral deviation from sampled orchard row centers, computed as each reference point’s minimum Euclidean distance to the generated path.Source: A. Experimental Setup · p. 5
  • The row-center reference acts as pseudoground-truth because ground-truth trajectories are unavailable, measuring row adherence rather than path optimality.Source: A. Experimental Setup · p. 5
  • The row-center metric structurally favors AGRO-Nav over shortest-path baselines, so reported errors require interpretation with this bias in mind.Source: A. Experimental Setup · p. 5

Simulation setup

  • Isaac Sim evaluation uses six start–goal pairs and 66 sampled points across six representative rows in a controlled orchard model.Source: A. Experimental Setup · p. 5

Real-world setup

  • Real-world evaluation uses three start–goal pairs from ROS2 datasets collected with a 4WS orchard robot in a commercial orchard in Daegu, South Korea.Source: A. Experimental Setup · p. 5

B. Simulation-based Validation

AGRO-Nav is evaluated in Isaac Sim at full and 70% tree density against A*, Theta*, and a reproduced RANSAC structural baseline. It maintains accurate, consistent row-centered paths while planning faster than the alternatives.

Full-density results

  • AGRO-Nav achieved the lowest full-density simulation errors, with 0.14 m mean and 0.42 m maximum lateral deviation.Its 0.10 m standard deviation was also the smallest among the compared methods.Source: B. Simulation-based Validation · p. 5

Planning efficiency

  • 14.24 ms and 13.48 ms were AGRO-Nav’s planning times at full and 70% density, making it roughly four to five times faster than grid-based planners.It was also 2.4–2.6× faster than RANSAC in the two simulation conditions.Source: B. Simulation-based Validation · p. 6

70% density results

  • AGRO-Nav maintained a 0.14 m mean error at 70% tree density, essentially unchanged from full density.Maximum error and path length increased only marginally, while grid-based planners remained substantially less accurate.Source: B. Simulation-based Validation · p. 7

Method interpretation

  • AGRO-Nav’s stability follows from constructing the graph from global extracted row geometry rather than isolated tree observations.The graph remains close to the full-density structure when remaining trunks provide enough evidence.Source: B. Simulation-based Validation · p. 7

70% density results

  • Theta* penetrated a tree row at 70% density, while RANSAC mean error increased from 0.18 m to 0.24 m as density decreased.These results contrast with AGRO-Nav’s stable row-centered error under partial sparsity.Source: B. Simulation-based Validation · p. 7

Clearance behavior

  • AGRO-Nav held trunk-clearance profiles near 2.5 m across routes, whereas A* and Theta* repeatedly fell near 1 m at row transitions and off-row segments.RANSAC followed a similar band but dipped more at transitions.Source: B. Simulation-based Validation · p. 7

C. Real-world Orchard Evaluation

In the real orchard, AGRO-Nav follows row centerlines more accurately and plans faster than the baseline planners. Its slightly longer routes trade shortest-path behavior for centered segments and controlled headland turns that preserve clearance.

Accuracy

  • 0.08 m was AGRO-Nav’s real-orchard mean lateral error, confirming precise row-centerline following.The field evaluation reports this as the mean error across the real-orchard block.Source: C. Real-world Orchard Evaluation · p. 7

Planning efficiency

  • 11.52 ms versus 51.24 ms and 58.48 ms made AGRO-Nav approximately four to five times faster than the baselines.The speedup follows from searching a small precomputed topological graph rather than many occupancy-grid cells.Source: C. Real-world Orchard Evaluation · p. 7

Trajectory structure

  • 218.03 m versus 214.09 m for A* and 212.70 m for Theta* shows that AGRO-Nav accepts slightly longer trajectories than shortest-path baselines.Its route uses straight row-centered segments joined by controlled turns at row transitions.Source: C. Real-world Orchard Evaluation · p. 7

Operational safety

  • Turning only at headlands keeps AGRO-Nav’s trajectory consistently distant from trunks and canopy, providing a margin that absorbs ordinary tracking error.The stated consequence applies to both differential-drive and four-wheel-steering platforms.Source: C. Real-world Orchard Evaluation · p. 7

V. CONCLUSION

AGRO-Nav automatically constructs a connected orchard topological graph from SLAM-derived tree-row lines, then combines graph routing, any-angle search, and spline smoothing for row-aligned trajectories. Real-orchard and Isaac Sim evaluations show lower row-center error and faster planning than the compared baselines, while the current evaluation remains limited to static planning and one orchard site.

Conclusion

  • AGRO-Nav automatically builds a connected topological graph from PCA-fitted tree-row lines recovered from a SLAM-generated 3D point cloud, eliminating manual waypoint design.The graph supports static global planning through Dijkstra routing over sparse row connectivity.Source: §V · p. 7
  • Cubic B-spline smoothing and uniform resampling produce continuous trajectories with straight in-row segments and controlled turns, suited to differential-drive and 4WS platforms.Source: §V · p. 8
  • 0.08 m mean row-center error was achieved in real-orchard trials, compared with 0.31 m for A* and 0.43 m for Theta*, while planning took 11.52 ms versus 51.24 ms and 58.48 ms.Isaac Sim experiments also reported the lowest mean and maximum row-center error among compared methods at full and 70% tree density, with 0.14 m mean error at both densities.Source: §V · p. 8
  • Static global planning is evaluated in a pre-mapped environment, while dynamic-obstacle avoidance is left for future work; real-world testing used a single commercial orchard site.Future work includes a runtime local obstacle layer, physical-platform trajectory and kinematic validation, and evaluation across broader layouts, seasons, and sites.Source: §V · p. 8

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