Autonomous navigation is the foundation of agricultural robots. This paper focuses on developing an advanced autonomous navigation system for a rover operating within row-based crops. A position-agnostic system is proposed to address the challenging situation when standard localization methods, like GPS, fail due to unfavorable weather or obstructed signals. This breakthrough is especially vital in densely vegetated regions, including areas covered by thick tree canopies or pergola vineyards. This work proposed a novel system that leverages a single RGB-D camera and a Non-linear Model Predictive Control strategy to navigate through entire rows, adapting to various crop spacing. The presented solution demonstrates versatility in handling diverse crop densities, environmental factors, and multiple navigation tasks to support agricultural activities at an extremely cost-effective implementation. Experimental validation in simulated and real vineyards underscores the system’s robustness and competitiveness in both standard row traversal and target objects approach.

Non-linear Model Predictive Control for Multi-task GPS-free Autonomous Navigation in Vineyards / Sperti, Matteo; Ambrosio, Marco; Martini, Mauro; Navone, Alessandro; Ostuni, Andrea; Chiaberge, Marcello. - (2024), pp. 96-101. (Intervento presentato al convegno 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) tenutosi a Bari (Italia) nel 28 August 2024 - 01 September 2024) [10.1109/CASE59546.2024.10711737].

Non-linear Model Predictive Control for Multi-task GPS-free Autonomous Navigation in Vineyards

Ambrosio, Marco;Martini, Mauro;Navone, Alessandro;Ostuni, Andrea;Chiaberge, Marcello
2024

Abstract

Autonomous navigation is the foundation of agricultural robots. This paper focuses on developing an advanced autonomous navigation system for a rover operating within row-based crops. A position-agnostic system is proposed to address the challenging situation when standard localization methods, like GPS, fail due to unfavorable weather or obstructed signals. This breakthrough is especially vital in densely vegetated regions, including areas covered by thick tree canopies or pergola vineyards. This work proposed a novel system that leverages a single RGB-D camera and a Non-linear Model Predictive Control strategy to navigate through entire rows, adapting to various crop spacing. The presented solution demonstrates versatility in handling diverse crop densities, environmental factors, and multiple navigation tasks to support agricultural activities at an extremely cost-effective implementation. Experimental validation in simulated and real vineyards underscores the system’s robustness and competitiveness in both standard row traversal and target objects approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2994394