Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations
Autonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomo...
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doaj-89d048dce73440b49df8644116f38d252021-04-02T20:20:36ZengMDPI AGAgronomy2073-43952021-02-011128728710.3390/agronomy11020287Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard OperationsMatteo Corno0Sara Furioli1Paolo Cesana2Sergio M. Savaresi3Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyDipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalySDF Group, 24047 Treviglio, ItalyDipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyAutonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomous tractors in orchards and vineyards is becoming commercially profitable. These scenarios offer more challenges as the vehicle needs to position itself with respect to a more cluttered environment. This paper presents an adaptive localization system for (semi-) autonomous navigation of agricultural tractors in vineyards that is based on ultrasonic automotive sensors. The system estimates the distance from the left vineyard row and the incidence angle. The paper shows that a single tuning of the localization algorithm does not provide robust performance in all vegetation scenarios. We solve this issue by implementing an Extended Kalman Filter (EKF) and by introducing an adaptive data selection stage that automatically adapts to the vegetation conditions and discards invalid measurements. An extensive experimental campaign validates the main features of the localization algorithm. In particular, we show that the Root Mean Square Error (RMSE) of the distance is 16 cm, while the angular RMSE is 2.6 degrees.https://www.mdpi.com/2073-4395/11/2/287crop row guidancelocalizationsituation awareness |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Matteo Corno Sara Furioli Paolo Cesana Sergio M. Savaresi |
spellingShingle |
Matteo Corno Sara Furioli Paolo Cesana Sergio M. Savaresi Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations Agronomy crop row guidance localization situation awareness |
author_facet |
Matteo Corno Sara Furioli Paolo Cesana Sergio M. Savaresi |
author_sort |
Matteo Corno |
title |
Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations |
title_short |
Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations |
title_full |
Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations |
title_fullStr |
Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations |
title_full_unstemmed |
Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations |
title_sort |
adaptive ultrasound-based tractor localization for semi-autonomous vineyard operations |
publisher |
MDPI AG |
series |
Agronomy |
issn |
2073-4395 |
publishDate |
2021-02-01 |
description |
Autonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomous tractors in orchards and vineyards is becoming commercially profitable. These scenarios offer more challenges as the vehicle needs to position itself with respect to a more cluttered environment. This paper presents an adaptive localization system for (semi-) autonomous navigation of agricultural tractors in vineyards that is based on ultrasonic automotive sensors. The system estimates the distance from the left vineyard row and the incidence angle. The paper shows that a single tuning of the localization algorithm does not provide robust performance in all vegetation scenarios. We solve this issue by implementing an Extended Kalman Filter (EKF) and by introducing an adaptive data selection stage that automatically adapts to the vegetation conditions and discards invalid measurements. An extensive experimental campaign validates the main features of the localization algorithm. In particular, we show that the Root Mean Square Error (RMSE) of the distance is 16 cm, while the angular RMSE is 2.6 degrees. |
topic |
crop row guidance localization situation awareness |
url |
https://www.mdpi.com/2073-4395/11/2/287 |
work_keys_str_mv |
AT matteocorno adaptiveultrasoundbasedtractorlocalizationforsemiautonomousvineyardoperations AT sarafurioli adaptiveultrasoundbasedtractorlocalizationforsemiautonomousvineyardoperations AT paolocesana adaptiveultrasoundbasedtractorlocalizationforsemiautonomousvineyardoperations AT sergiomsavaresi adaptiveultrasoundbasedtractorlocalizationforsemiautonomousvineyardoperations |
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