Most popular articles
Everything About Peaches. Clemson University Cooperative Extension Service Everything About Peaches Website: whether you are a professional or backyard peach...
Mission Statement. For the sake of mankind and the world as a whole a further increase of the sustainability...
Newsletter 9: July 2013 - Temperate Fruits in the Tropics and Subtropics. Download your copy of the Working Group Temperate...
USA Walnut varieties. The Walnut Germplasm Collection of the University of California, Davis (USA). A description of the Collection and a History...
China Walnut varieties.

Articles

HEADLAND TURNING MANEUVER OF AN AUTONOMOUS VEHICLE NAVIGATING A CITRUS GROVE USING MACHINE VISION AND SWEEPING LASER RADAR

Article number
824_38
Pages
321 – 328
Language
English
Abstract
This article discusses the development of row to row turning maneuver of an autonomous vehicle at the headlands of citrus groves.
A turning maneuver is executed in a series of five steps which are 1) determining the approach of the end of the current row, 2) establishing the end of the row, 3) turning into the headland, 4) turning into the next row, and 5) getting to a position to navigate the next row.
A John Deere eGator was used as the autonomous vehicle.
The sensors used for executing the maneuver are video camera, laser radar (ladar), inertial measurement unit (IMU), and wheel encoder.
The video camera and the ladar aid in detecting and navigating the headland.
The sweeping ladar aids in accurately determining the end of the row.
The yaw angle measured by the IMU provides the vehicle-turning angle, and the wheel encoder is used for speed control and dead reckoning to confirm end of row.
The headland turning maneuver of the vehicle was tested in a citrus grove.
Performance measures of the vehicle, while executing the maneuver, are reported.
Headland turning ability combined with row navigation ability enables an autonomous vehicle to completely navigate a citrus grove.

Publication
Authors
V. Subramanian, T.F. Burks
Keywords
guidance, sensor fusion, image processing, camera, encoder, control, steering
Full text
Online Articles (45)
Q.U. Zaman | D.C. Percival | R.J. Gordon | A.W. Schumann
E.M. Perry | F.J. Pierce | J.R. Davenport | J. Smithyman
A. Torre-Neto | C.M.P. Vaz | D.M. Milori | L.A.C. Jorge | L.M. Rabello | L.H.C. Mattoso | R.Y. Inamasu | J.S. Schepers | R.B. Bassanesi
N. Tremblay | C. Bélec | S. Jenni | E. Fortier | R. Mellgren
Y. Kavdir | R. Ilay | H. Turhan | L. Genc | I. Kavdir | A. Sümer
T. Fukatsu | M. Hirafuji | Y. Saito | T. Suzuki
B.S. Blackmore | S. Fountas | T.A. Gemtos | H.W. Griepentrog
J.P. Molin | G.D.C. Faulin | W.M. Stanislavski
S.K. Upadhyaya | V. Udompetaikul | M.S. Shafii | G.T. Browne
M.R.S. Konopatzki | E.G. Souza | L.H.P. Nóbrega | M.A. Uribe-Opazo | G. Suszek | S. Rodrigues | E.F. de Oliveira
D.M. Bulanon | T.F. Burks | V. Alchanatis
U. Rosa | L. Ferguson | C. Gliever | K. Glozer | C. Crisosto | B. Krueger | R. Diaz-Silva | D. Pursell | J. Galbraith | S. Castro-Garcia | D. Smith | J. Burns
T. Baugher | J. Schupp | K. Lesser | R.M. Harsh | C. Seavert | K. Lewis | T. Auvil
S.M. Garrán | L. Vera | M.J. Beribe | M.J. Tito | O. Faure | S. Massueli | R. Mika
I. Kavdir | M.B. Buyukcan | H. Kocabiyik | R. Lu | M. Seker