Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations

Matteo Corno,Sergio M Savaresi,Sara Furioli,Paolo Cesana

doi:10.3390/agronomy11020287

Abstract

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.

Highlights

Farmers have been using some forms of automatic driving technologies for years.Vehicle automatization has an appealing potential in agriculture, mainly due to the relative simple regulations, seasonally long working hours, and the repetitive nature of many agricultural tasks
We focus on the localization algorithm for an Advance Driver Assistance System (ADAS) of level 3
The estimation algorithm is based on an Extended Kalman Filter and a data selection step

Summary

Introduction

Farmers have been using some forms of automatic driving technologies for years.Vehicle automatization has an appealing potential in agriculture, mainly due to the relative simple regulations, seasonally long working hours, and the repetitive nature of many agricultural tasks. Agricultural vehicle automation can reduce costs, by operating longer hours without the need of employing personnel, and, most importantly, is an enabling technology for precision farming, a farm management approach that uses real time information on the state of the crops, and responds—as automatically as possible—to varying crops conditions [2,3]. The most successful autonomous driving systems in agriculture have been those used in open fields. These tasks require the vehicle to track a pre-computed path and are, easy to automate. The scenario becomes more challenging if one considers, for example, orchards and vineyards. These high value cultivars are often planted very densely, in non-flat terrains and grow irregularly. A map based navigation of orchards and vineyards is not the preferred option as the maps would need to be constantly updated

Methods

Discussion

Conclusion