P-AgNav: Range View-Based Autonomous Navigation System for Cornfields

In this paper, we present an in-row and under-canopy autonomous navigation system for cornfields, called the Purdue Agricultural Navigation System or P-AgNav. Our navigation framework is primarily based on range view images from a 3D light detection and ranging (LiDAR) sensor. P-AgNav is designed for an autonomous robot to navigate in the corn rows with collision avoidance and to switch between rows without GNSS assistance or pre-defined waypoints. The system enables robots, which are intended to monitor crops or conduct physical sampling, to autonomously navigate multiple crop rows with minimal human intervention, thereby increasing crop management efficiency. The capabilities of P-AgNav have been validated through experiments in both simulation and real cornfield environments.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-02-13
DOI
https://doi.org/10.1109/lra.2025.3541335
Citations
5
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
8.75
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article

P-AgNav: Range View-Based Autonomous Navigation System for Cornfields

Aarya Deb, David J. Cappelleri, Kitae Kim
5 citations
IEEE Robotics and Automation Letters
Smart Agriculture and AI
8.75
article

P-AgNav: Range View-Based Autonomous Navigation System for Cornfields

Aarya Deb, David J. Cappelleri, Kitae Kim
article en
5 citations

Abstract

In this paper, we present an in-row and under-canopy autonomous navigation system for cornfields, called the Purdue Agricultural Navigation System or P-AgNav. Our navigation framework is primarily based on range view images from a 3D light detection and ranging (LiDAR) sensor. P-AgNav is designed for an autonomous robot to navigate in the corn rows with collision avoidance and to switch between rows without GNSS assistance or pre-defined waypoints. The system enables robots, which are intended to monitor crops or conduct physical sampling, to autonomously navigate multiple crop rows with minimal human intervention, thereby increasing crop management efficiency. The capabilities of P-AgNav have been validated through experiments in both simulation and real cornfield environments.

IEEE Robotics and Automation LettersVol. 10(4)
Purdue University West Lafayette (US)
Openalex Percentile: Top 3%
Smart Agriculture and AI
8.75
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P-AgNav: Range View-Based Autonomous Navigation System for Cornfields — Aarya Deb, David J. Cappelleri, et al. · IEEE Robotics and Automation Letters (2025) | TGRS Research Map | TGRS