Fully-autonomous vehicles, both aerial and ground, could provide great benefits in the Agriculture 4.0 framework when operating within cooperative architectures, thanks to their ability to tackle difficult tasks, particularly within complex irregular and unstructured scenarios such as vineyards on sloped terrains. A decentralised multi-phase approach has been proposed as an alternative to more common cooperative schemes. When perennial crops are considered, it is advantageous to build a simplified geometrical (and georeferenced) crops model, which can be identified by using 3D point clouds acquired during a-priori explorative missions by unmanned aerial vehicles. This model can be used to plan the tasks to be performed within the crops by the in-field aerial and ground drones. In this companion paper, the proposed strategy is applied to a specific case study involving a vineyard on a sloped terrain, located in the Barolo region in Piedmont, Italy. Ad-hoc technologies and guidance, navigation and control algorithms were designed and implemented. The main objectives were to improve the autonomous driving capabilities of the drones involved and to automate the process of retrieving low-complexity maps from the data collected with preliminary remote sensing missions to make them available for the autonomous navigation by a quadrotor and an unmanned 4-wheel steering ground vehicle within the vine rows. Preliminary results highlight the benefits achievable by exploiting the tailored technologies selected and applied to improve each of the analysed mission phases.

Cooperation of unmanned systems for agricultural applications: A case study in a vineyard

Comba L.
;
Biglia A.;Gay P.
Last
2022-01-01

Abstract

Fully-autonomous vehicles, both aerial and ground, could provide great benefits in the Agriculture 4.0 framework when operating within cooperative architectures, thanks to their ability to tackle difficult tasks, particularly within complex irregular and unstructured scenarios such as vineyards on sloped terrains. A decentralised multi-phase approach has been proposed as an alternative to more common cooperative schemes. When perennial crops are considered, it is advantageous to build a simplified geometrical (and georeferenced) crops model, which can be identified by using 3D point clouds acquired during a-priori explorative missions by unmanned aerial vehicles. This model can be used to plan the tasks to be performed within the crops by the in-field aerial and ground drones. In this companion paper, the proposed strategy is applied to a specific case study involving a vineyard on a sloped terrain, located in the Barolo region in Piedmont, Italy. Ad-hoc technologies and guidance, navigation and control algorithms were designed and implemented. The main objectives were to improve the autonomous driving capabilities of the drones involved and to automate the process of retrieving low-complexity maps from the data collected with preliminary remote sensing missions to make them available for the autonomous navigation by a quadrotor and an unmanned 4-wheel steering ground vehicle within the vine rows. Preliminary results highlight the benefits achievable by exploiting the tailored technologies selected and applied to improve each of the analysed mission phases.
2022
Inglese
Esperti anonimi
223
81
102
22
https://www.sciencedirect.com/science/article/pii/S1537511021003111
Control logic; Path and mission planning; Precision farming; Unmanned aerial vehicles; Unmanned ground vehicles; Unmanned vehicles cooperation
no
1 – prodotto con file in versione Open Access (allegherò il file al passo 6 - Carica)
262
5
Mammarella M.; Comba L.; Biglia A.; Dabbene F.; Gay P.
info:eu-repo/semantics/article
open
03-CONTRIBUTO IN RIVISTA::03A-Articolo su Rivista
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S1537511021003111-main.pdf

Accesso aperto

Tipo di file: PDF EDITORIALE
Dimensione 7.65 MB
Formato Adobe PDF
7.65 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1844647
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 16
social impact