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R. Vaughan, Brian Gerkey, A. Howard (2003)
On device abstractions for portable, reusable robot codeProceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), 3
L. Hugues, Nicolas Bredèche (2006)
Simbad: An Autonomous Robot Simulation Package for Education and Research
(2011)
Simulation of communication within the RHEA robotic fleet
Sung-Yong Cho, Sun Chang, You Kim, K. An (2002)
AE—Automation and Emerging Technologies: Development of a Three-Degrees-of-Freedom Robot for harvesting Lettuce using Machine Vision and Fuzzy logic ControlBiosystems Engineering, 82
Agrotechnology (2002)
A SPECIFICATION OF BEHAVIOURAL REQUIREMENTS FOR AN AUTONOMOUS TRACTOR Was
(2012)
Webots : robot simulator ”
E. Henten, D. Slot, C. Hol, L. Willigenburg (2009)
Optimal manipulator design for a cucumber harvesting robotComputers and Electronics in Agriculture, 65
(2011)
Application of mechanical and thermal weed control in maize as part of the RHEA project
P. Thornton, R. Fawcett, J. Dent, T. Perkins (1990)
Spatial weed distribution and economic thresholds for weed controlCrop Protection, 9
S. Blackmore, H. Have, S. Fountas (2002)
Specification of Behavioural Requirements for an Autonomous Tractor
K. Thorp, Kendall DeJonge, A. Kaleita, W. Batchelor, J. Paz (2008)
Methodology for the use of DSSAT models for precision agriculture decision supportComputers and Electronics in Agriculture, 64
N. Tillett, T. Hague, S. Miles (2002)
Inter-row vision guidance for mechanical weed control in sugar beetComputers and Electronics in Agriculture, 33
CROPS
Clever robots for crops
W. Lee, D. Slaughter, D. Giles (2004)
Robotic Weed Control System for TomatoesPrecision Agriculture, 1
H. Jeon, L. Tian (2009)
Direct application end effector for a precise weed control robotBiosystems Engineering, 104
G. Carson, R. Puk, Rikk Carey (1999)
Developing the VRML 97 International StandardIEEE Computer Graphics and Applications, 19
J. Blasco, N. Aleixos, J.M. Roger, G. Rabatel, E. Moltó
Robotic weed control using machine vision
N. Zhang, Maohua Wang, Ning Wang (2002)
Precision agriculture—a worldwide overviewComputers and Electronics in Agriculture, 36
D. Wyse-Pester, L. Wiles, P. Westra (2002)
Infestation and spatial dependence of weed seedling and mature weed populations in corn, 50
S.I. Cho, S.J. Chang, Y.Y. Kim, K.J. An
Development of a three‐degrees‐of‐freedom robot for harvesting lettuce using machine vision and fuzzy logic control
K. Johns, T. Taylor (2008)
Professional Microsoft Robotics Developer Studio
(2012)
“ MATLAB and Simulink for technical computing ”
M. Loghavi, B. Mackvandi (2008)
Development of a target oriented weed control systemComputers and Electronics in Agriculture, 63
M. Jurado-Expósito, F. López-Granados, J. González-Andújar, L. Garcia-Torres (2004)
Spatial and temporal analysis of Convolvulus arvensis L. populations over four growing seasonsEuropean Journal of Agronomy, 21
Esri
A complete GIS and mapping software system
J. Cardina, G. Johnson, D. Sparrow (1997)
The nature and consequence of weed spatial distributionWeed Science, 45
G. Belforte, R. Deboli, P. Piccarolo, D. Aimonino (2006)
Robot Design and Testing for Greenhouse ApplicationsBiosystems Engineering, 95
S. Cook, R. Bramley (1998)
Precision agriculture — opportunities, benefits and pitfalls of site-specific crop management in AustraliaAustralian Journal of Experimental Agriculture, 38
Y. Edan, G. Miles (1994)
Systems Engineering of Agricultural Robot DesignIEEE Trans. Syst. Man Cybern. Syst., 24
N. Kawamura, K. Namikawa (1986)
Robots in agricultureAdv. Robotics, 3
J. Blasco, N. Aleixos, J. Roger, G. Rabatel, E. Moltó (2002)
AE—Automation and Emerging Technologies: Robotic Weed Control using Machine VisionBiosystems Engineering, 83
C. Stöckle, M. Donatelli, R. Nelson (2003)
CropSyst, a cropping systems simulation modelEuropean Journal of Agronomy, 18
B. Keating, P. Carberry, G. Hammer, M. Probert, M. Robertson, D. Holzworth, N. Huth, J. Hargreaves, H. Meinke, Z. Hochman, G. McLean, K. Verburg, V. Snow, J. Dimes, M. Silburn, E. Wang, S. Brown, K. Bristow, S. Asseng, S. Chapman, R. Mccown, D. Freebairn, C. Smith (2003)
An overview of APSIM, a model designed for farming systems simulationEuropean Journal of Agronomy, 18
AirRobot GmbH & Co. KG
The universal platform for aerial surveillance, survey and documentation
J. Goudriaan, H. Laar (1994)
Modelling Potential Crop Growth Processes
O. Chocron, E. Delaleau, J. Fleureau (2007)
Flatness-Based Control of a Mechatronic Weed Killer Autonomous Robot2007 IEEE International Symposium on Industrial Electronics
T. Bakker (2009)
An autonomous robot for weed control: design, navigation and control.
Purpose – The purpose of this paper is to propose going one step further in the simulation tools related to agriculture by integrating fleets of mobile robots for the execution of precision agriculture techniques. The proposed new simulation environment allows the user to define different mobiles robots and agricultural implements. Design/methodology/approach – With this computational tool, the crop field, the fleet of robots and the different sensors and actuators that are incorporated into each robot can be configured by means of two interfaces: a configuration interface and a graphical interface, which interact with each other. Findings – The system presented in this article unifies two very different areas – robotics and agriculture – to study and evaluate the implementation of precision agriculture techniques in a 3D virtual world. The simulation environment allows the users to represent realistic characteristics from a defined location and to model different variabilities that may affect the task performance accuracy of the fleet of robots. Originality/value – This simulation environment, the first in incorporating fleets of heterogeneous mobile robots, provides realistic 3D simulations and videos, which grant a good representation and a better understanding of the robot labor in agricultural activities for researchers and engineers from different areas, who could be involved in the design and application of precision agriculture techniques. The environment is available at the internet, which is an added value for its expansion in the agriculture/robotics family.
Industrial Robot: An International Journal – Emerald Publishing
Published: Jan 4, 2013
Keywords: Agriculture; Robots; Simulation; Crops; Precision agriculture; Autonomous robots; Simulation environment; Fleet of robots; Weed management
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