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Cordesses, Lionel; Cariou, Christophe; Berducat, Michel

doi:10.1023/a:1011473630247

Cited by 37 publications

(5 citation statements)

References 6 publications

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“…Autonomous Navigation in Agricultural Fields. GPS, alone and in combination with IMU and RTK corrections, is commonly used for outdoor navigation for tractors and over-canopy agricultural robots [52,2,3,70,37,18,36]. Under-canopy navigation is concerned with autonomous rowfollowing between the rows of crops.…”

Section: Related Workmentioning

confidence: 99%

Learned Visual Navigation for Under-Canopy Agricultural Robots

Sivakumar

Modi

Gasparino

et al. 2021

Robotics: Science and Systems XVII

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This paper describes a system for visually guided autonomous navigation of under-canopy farm robots. Low-cost under-canopy robots can drive between crop rows under the plant canopy and accomplish tasks that are infeasible for overthe-canopy drones or larger agricultural equipment. However, autonomously navigating them under the canopy presents a number of challenges: unreliable GPS and LiDAR, high cost of sensing, challenging farm terrain, clutter due to leaves and weeds, and large variability in appearance over the season and across crop types. We address these challenges by building a modular system that leverages machine learning for robust and generalizable perception from monocular RGB images from low-cost cameras, and model predictive control for accurate control in challenging terrain. Our system, CropFollow, is able to autonomously drive 485 meters per intervention on average, outperforming a state-of-the-art LiDAR based system (286 meters per intervention) in extensive field testing spanning over 25 km. I. INTRODUCTIONThis paper describes the design of a visually-guided navigation system for compact, low-cost, under-canopy agricultural robots for commodity row-crops (corn, soybean, sugarcane etc), such as that shown in Figure 1. Our system, called CropFollow, uses monocular RGB images from an on-board front-facing camera to steer the robot to autonomously traverse in between crop rows in harsh, visually cluttered, uneven, and variable real-world agricultural fields. Robust and reliable autonomous navigation of such under-canopy robots has the potential to enable a number of practical and scientific applications: High-throughput plant phenotyping [43,37,68,66,58,25], ultra-precise pesticide treatments, mechanical weeding [41], plant manipulation [17,61], and cover crop planting [64,62] Such applications are not possible with overcanopy larger tractors and UAVs, and are crucial for increasing agricultural sustainability [55,22].Autonomous row-following is a foundational capability for robots that need to navigate between crop rows in agricultural fields. Such robots cannot rely on RTK (Real-Time Kinematic)-GPS [21] based methods which are used for overthe-canopy autonomy (e.g. for drones, tractors, and combine Project website with data and videos: https://ansivakumar.github.io/ learned-visual-navigation/. Correspondence to {av7,girishc}@illinois.edu. * Girish Chowdhary and Saurabh Gupta contributed equally and are listed alphabetically.

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Section: Related Workmentioning

confidence: 99%

Learned Visual Navigation for Under-Canopy Agricultural Robots

Sivakumar

Modi

Gasparino

et al. 2021

Robotics: Science and Systems XVII

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“…Control law of the form (20), based on the feedback linearization approach, was earlier considered in the papers Cordesses et al (2000), Thuilot et al (2002), and others. In the present paper we extend it on the case of the control with saturation.…”

Section: Control Law Designmentioning

confidence: 99%

“…There are many applications like a road construction and agriculture where a vehicle must be automatically driven along a target trajectory with high level of accuracy. These and other tasks are performed by wheeled robots equipped with satellite and inertial navigation tools, see Cordesses et al (2000), Thuilot et al (2002), . On the other hand, automatic control does not exclude manual steering.…”

Section: Introductionmentioning

confidence: 99%

“…The problem, similar to the one considered in this paper is not new. In many papers (e.g., see Cordesses et al (2000), Thuilot et al (2002), Samson (1995) and McClamroch (1995), Guldner and Utkin (1994) and the references therein), a control law is designed which stabilizes the motion along a line segment or stabilizes the motion towards a given point in the plane; the control may be either continuous or discontinuous (Guldner and Utkin (1994). Here, we consider the control design problem which accounts for the boundedness of controls.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Attraction Domains in Wheeled Robot Control Using Absolute Stability Approach

Rapoport

Morozov

2008

IFAC Proceedings Volumes

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“…To increase machine efficiency, a speed control system was developed based on harvester process variables, such as feed rate, engine load and grain mass flow [3]. Combine harvester equipped with kinematic GPS based guidance system was constructed to eliminate the accumulative error during direction regulation [4]. Machine vision based guidance system was also developed to indentify the crop swath.…”

Section: Introductionmentioning

confidence: 99%

Design and Field Test of Virtual Instrument Based Monitor and Control System for Combine Harvester Automation

Wang

Ding

et al. 2011

AMM

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In this study an online monitor and control system was developed and tested based on virtual instrument environment for combine harvester automation. A data acquisition system and control elements were integrated into a self-propelled combine harvester. The following online information was recorded: performance parameters (threshing drum torque, engine speed, operation speed, etc), machine settings (reel speed, knife speed, drum speed, etc) and guidance information. The collected data were integrated into windows-based software for real-time processing. Image sensing information was used for swath detection and obtained results could be transmitted to the actuator for auto steering. The guidance error could be controlled in the range of 0.15 m during road surface test with centerline. The ground speed was adjusted by using the threshing power consumption data to improve work performance during field test. Experiments results indicated that it was possible to use virtual instrument environment based system to monitor operation data and conduct other real-time applications for field operation.

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Untitled

Cited by 37 publications

References 6 publications

Learned Visual Navigation for Under-Canopy Agricultural Robots

Learned Visual Navigation for Under-Canopy Agricultural Robots

Estimation of Attraction Domains in Wheeled Robot Control Using Absolute Stability Approach

Design and Field Test of Virtual Instrument Based Monitor and Control System for Combine Harvester Automation

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