Precision Guidance of Agricultural Tractors for Autonomous Farming

Eaton, Ray; Katupitiya, Jayantha; Siew, K. W.; Dang, Kim Son

doi:10.1109/systems.2008.4519026

Cited by 27 publications

(7 citation statements)

References 11 publications

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“…As an alternative, some approaches consider the specific phenomena encountered in a natural environment (mainly due to wheel skidding) as perturbations in the kinematic model to be rejected (see Wang & Low, 2007, for instance). Some robust control techniques are then designed to compensate for such perturbations [as investigated in Eaton, Katupitiya, Siew, and Dang (2008), Hao, Lenain, Thuilot, and Martinet (2005), or Lucet, Grand, Salle, and Bidaud (2008)]. However, these approaches tend to be conservative, even at limited speed, and oscillating behavior can be recorded when significant sliding occurs.…”

Section: Introductionmentioning

confidence: 99%

Mixed kinematic and dynamic sideslip angle observer for accurate control of fast off‐road mobile robots

Lenain¹,

Thuilot²,

Cariou³

et al. 2009

Journal of Field Robotics

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Automation in outdoor applications (farming, surveillance, military activities, etc.) requires highly accurate control of mobile robots, at high speed, although they are moving on low-grip terrain. To meet such expectations, advanced control laws accounting for natural ground specificities (mainly sliding effects) must be derived. In previous work, adaptive and predictive control algorithms, based on an extended kinematic representation, have been proposed. Satisfactory experimental results have been reported (accurate to within ±10 cm, whatever the grip conditions), but at limited velocity (below 3 m·s-1). Nevertheless, simulations reveal that control accuracy is decreased when vehicle speed is increased (up to 10 m·s-1). In particular, oscillations are observed at curvature transition. This drawback is due to delays in sideslip angle estimation, unavoidable at high speed because only an extended kinematic representation was used. In this paper, a mixed backstepping kinematic and dynamic observer is designed to improve observation of these variables: the slow-varying data are still estimated from a kinematic representation, which is then injected into a dynamic observer to supply reactive and reliable sliding variable (namely sideslip angle) estimation, without increasing the noise level. The algorithm is evaluated via advanced simulations (coupling Adams and MatLab software) investigating high-speed capabilities. Actual experiments at lower speed (experimental platform maximum velocity) demonstrate the benefits of the proposed approach

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Section: Introductionmentioning

confidence: 99%

Mixed kinematic and dynamic sideslip angle observer for accurate control of fast off‐road mobile robots

Lenain¹,

Thuilot²,

Cariou³

et al. 2009

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…A preliminary solution can consist of using robust principles [such as proposed in Eaton, Katupitiya, Siew, & Dang (2008) or Hao, Lenain, Thuilot, & Martinet (2005)], viewing sliding as bounded external perturbations to be compensated. Unfortunately, such principles are conservative when grip conditions are quite low, potentially leading to an oscillating behavior.…”

Section: Kinematic Model Extended With Sliding Parametersmentioning

confidence: 99%

Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations

Cariou¹,

Lenain²,

Thuilot³

et al. 2009

Journal of Field Robotics

107

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As world population growth requires an increasing level of farm production at the same time that environmental preservation is a priority, the development of new agricultural tools and methods is required. In this framework, the development of robotic devices can provide an attractive solution, particularly in the field of autonomous vehicles. Accurate automatic guidance of mobile robots in farming constitutes a challenging problem for researchers, mainly due to the low grip conditions usually found in such a context. From assisted-steering systems to agricultural robotics, numerous control algorithms have been studied to achieve high-precision path tracking and have reached an accuracy within ±10 cm, whatever the ground configuration and the path to be followed. However, most existing approaches consider classical two-wheel-steering vehicles. Unfortunately, by using such a steering system, only the lateral deviation with respect to the path to be followed can be satisfactorily controlled. Indeed, the heading of the vehicle remains dependent on the grip conditions, and crabwise motions, for example, are systematically observed on a slippery slope, leading to inaccurate field operations. To tackle this drawback, a four-wheel-steering (4WS) mobile robot is considered, enabling servo of both Cariou et al.: Automatic Guidance of a Four-Wheel-Steering Mobile Robot • 505lateral and angular deviations with respect to a desired trajectory. The path tracking control is designed using an extended kinematic representation, allowing account to be taken online of wheel skidding, while a backstepping approach permits management of the 4WS structure. The result is an approach taking advantage of both rear and front steering actuations to fully compensate for sliding effects during path tracking. Moreover, a predictive algorithm is developed in order to address delays induced by steering actuators, compensating for transient overshoots in curves. Experimental results demonstrate that despite sliding phenomena, the mobile robot is able to automatically and accurately achieve a desired path, with lateral and angular errors, respectively, within ±10 cm and ±2 deg, whatever its shape and whatever the terrain conditions. This constitutes a promising result in efforts to define efficient tools with which to tackle tomorrow's agriculture challenge. C 2009 Wiley Periodicals, Inc.

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“…Today's state of technics and technology allows to create not only automatic, but also autonomous machines. The trend is shifting from automation to the robotization of industries, which, within the agriculture, occurs by using high-precision farming [10,11] and unmanned cycles of growing crops [12].…”

Section: Introductionmentioning

confidence: 99%

Approaches to the robotization of agricultural mobile machines

2018

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Abstract. Agriculture is the extremely important and developing economic movement in all times. Automation of agricultural machines occurs by different ways. One way is through the creation of specialized technical solutions for the required technological processes, another way is the construction of automatic agricultural machines, including mobile ones. The state of modern technology allows to create autonomous machines. The agriculture robotization trends are the high precision and unmanned farming. The article considers the issues of robotization of agricultural machinery. Stages of robotization of agricultural mobile machines were analyzed. The factors affecting the autonomous movement of mobile agrorobots were shown.

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Precision Guidance of Agricultural Tractors for Autonomous Farming

Cited by 27 publications

References 11 publications

Mixed kinematic and dynamic sideslip angle observer for accurate control of fast off‐road mobile robots

Mixed kinematic and dynamic sideslip angle observer for accurate control of fast off‐road mobile robots

Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations

Approaches to the robotization of agricultural mobile machines

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