This paper describes a human-inspired method (HIM) and a fully integrated navigation strategy for a wheeled mobile robot in an outdoor farm setting. The proposed strategy is composed of four main actions: sensor data analysis, obstacle detection, obstacle avoidance, and goal seeking. Using these actions, the navigation approach is capable of autonomous row-detection, row-following, and path planning motion in outdoor settings. In order to drive the robot in off-road terrain, it must detect holes or ground depressions (negative obstacles) that are inherent parts of these environments, in real-time at a safe distance from the robot. Key originalities of the proposed approach are its capability to accurately detect both positive (over ground) and negative obstacles, and accurately identify the end of the rows of bushes (e.g., in a farm) and enter the next row. Experimental evaluations were carried out using a differential wheeled mobile robot in different settings. The robot, used for experiments, utilizes a tilting unit, which carries a laser range finder (LRF) to detect objects, and a real-time kinematics differential global positioning system (RTK-DGPS) unit for localization. Experiments demonstrate that the proposed technique is capable of successfully detecting and following rows (path following) as well as robust navigation of the robot for point-to-point motion control.

References

References
1.
Bonin
,
F.
,
Ortiz
,
A.
, and
Oliver
,
G.
,
2008
, “
Visual Navigation for Mobile Robots: A Survey
,”
J. Intell. Rob. Syst. Theory Appl.
,
53
(
3
), pp.
263
296
.
2.
Kobayashi
,
Y.
,
Kurita
,
E.
, and
Gouko
,
M.
,
2013
, “
Integration of Multiple Sensor Spaces With Limited Sensing Range and Redundancy
,”
Int. J. Rob. Autom.
,
28
(
1
), pp.
31
41
.
3.
Nguyen
,
D.
,
Kuhnert
,
L.
, and
Kuhnert
,
K.
,
2013
, “
General Vegetation Detection Using an Integrated Vision System
,”
Int. J. Rob. Autom.
,
28
(
2
), pp.
170
179
.
4.
Su
,
L.
,
Luo
,
C.
, and
Zhu
,
F.
,
2009
, “
Obtaining Obstacle Information by an Omnidirectional Stereo Vision System
,”
Int. J. Rob. Autom.
,
24
(
3
), pp.
222
227
.
5.
Belforte
,
G. R.
,
Deboli
,
P.
, and
Piccarolo
,
P.
,
2006
, “
Robot Design and Testing for Greenhouse Applications
,”
Biosyst. Eng.
,
95
(
3
), pp.
309
321
.
6.
Torii
,
T.
,
2000
, “
Research in Autonomous Agriculture Vehicles in Japan
,”
Comput. Electron. Agric.
,
25
(
1–2
), pp.
133
153
.
7.
Astrand
,
B.
, and
Baerveldt
,
A.
,
2005
, “
A Vision Based Row-Following System for Agricultural Field Machinery
,”
Mechatronics
,
15
(
2
), pp.
251
269
.
8.
Hamner
,
B.
,
Singh
,
S.
, and
Bergerman
,
M.
,
2011
, “
Improving Orchard Efficiency With Autonomous Utility Vehicles
,”
American Society of Agricultural and Biological Engineers Annual International Meeting
(
ASABE
), Pittsburgh, PA, June 20–23, Paper No. 1009415, Vol.
6
, pp.
4670
4685
.
9.
Sim
,
R.
,
Elinas
,
P.
,
Griffin
,
M.
,
Shyr
,
A.
, and
Little
,
J. J.
,
2006
, “
Design and Analysis of a Framework for Realtime Vision-Based SLAM Using Rao-Blackwellised Particle Filters
,”
3rd Canadian Conference on Computer and Robot Vision
, Quebec, Canada, June 7–9, pp. 1–21.
10.
Sim
,
R.
, and
Little
,
J. J.
,
2006
, “
Autonomous Vision-Based Exploration and Mapping Using Hybrid Maps and Rao-Blackwellised Particle Filters
,”
IEEE International Conference on Intelligent Robots and Systems
(
IROS
), Beijing, Oct. 9–15, pp. 2082–2089.
11.
Manduchi
,
R.
,
Castano
,
A.
,
Talukder
,
A.
, and
Matthies
,
L.
,
2005
, “
Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation
,”
Autonom. Rob.
,
18
(
1
), pp.
81
102
.
12.
Boris
,
S.
,
Lin
,
E.
,
Bagnell
,
J.
,
Cole
,
J.
,
Vandapel
,
N.
, and
Stentz
,
A.
,
2006
, “
Improving Robot Navigation Through Self-Supervised Online Learning
,”
J. Field Rob.
,
23
(
11–12
), pp.
1059
1075
.
13.
Wellington
,
C.
,
Courville
,
A.
, and
Stentz
,
A.
,
2006
, “
A Generative Model of Terrain for Autonomous Navigation in Vegetation
,”
Int. J. Rob. Res.
,
25
(
12
), pp.
1287
1304
.
14.
Hamner
,
B.
,
Singh
,
S.
,
Roth
,
S.
, and
Takahashi
,
T.
,
2008
, “
An Efficient System for Combined Route Traversal and Collision Avoidance
,”
Auton. Rob.
,
24
(
4
), pp.
365
385
.
15.
Peynot
,
T.
,
Underwood
,
J.
, and
Scheding
,
S.
,
2009
, “
Towards Reliable Perception for Unmanned Ground Vehicles in Challenging Conditions
,”
IEEE/RSJ International Conference on Intelligent Robots and Systems
(
IROS 2009
), St. Louis, MO, Oct. 10–15, pp.
1170
1176
.
16.
Ordonez
,
C.
,
Chuy
,
O. Y.
,
Collins
,
E. G.
, and
Xiuwen
,
L.
,
2011
, “
Laser-Based Rut Detection and Following System for Autonomous Ground Vehicles
,”
J. Field Rob.
,
28
(
2
), pp.
158
179
.
17.
Zou
,
A. M.
,
Hou
,
Z. G.
,
Fu
,
S. Y.
,
Tan
,
M.
,
2006
, “
Neural Networks for Mobile Robot Navigation: A Survey
,”
Advances in Neural Networks (Lecture Notes in Computer Science)
, Vol.
3972
,
Springer, Berlin
, pp.
1218
1226
.
18.
Kulkarni
,
A.
, and
Tesar
,
D.
,
2010
, “
Instant Center Based Kinematic Formulation for Planar Wheeled Platforms
,”
ASME J. Mech. Rob.
,
2
(
3
), p.
031015
.
19.
Udengaard
,
M.
, and
Iagnemma
,
K.
,
2009
, “
Analysis, Design, and Control of an Omnidirectional Mobile Robot in Rough Terrain
,”
ASME J. Mech. Des.
,
131
(
12
), p.
121002
.
20.
Chakraborty
,
N.
, and
Ghosal
,
A.
,
2005
, “
Dynamic Modeling and Simulation of a Wheeled Mobile Robot for Traversing Uneven Terrain Without Slip
,”
ASME J. Mech. Des.
,
127
(
5
), pp.
901
909
.
21.
Witus
,
G.
,
Karlsen
,
R.
,
Gorsich
,
D.
, and
Gerhart
,
G.
,
2001
, “
Preliminary Investigation Into the Use of Stereo Illumination to Enhance Mobile Robot Terrain Perception
,”
Proc. SPIE
,
4364
, pp.
290
301
.
22.
Matthies
,
L.
,
2003
, “
Negative Obstacle Detection by Thermal Signature
,”
IEEE/RSJ International Conference on Intelligent Robots and Systems
(
IROS 2003
), Las Vegas, NV, Oct. 27–31, Vol.
1
, pp.
906
913
.
23.
Youngshik
,
K.
, and
Minor
,
M. A.
,
2007
, “
Path Manifold-Based Kinematic Control of Wheeled Mobile Robots Considering Physical Constraints
,”
Int. J. Rob. Res.
,
26
(
9
), pp.
955
975
.
24.
Flickinger
,
D. M.
, and
Minor
,
M. A.
,
2007
, “
Remote Low Frequency State Feedback Kinematic Motion Control for Mobile Robot Trajectory Tracking
,”
IEEE International Conference on Robotics and Automation
(
ICRA
), Rome, Apr. 10–14, pp.
3502
3507
.
25.
Barawid
,
O.
,
Mizushima
,
A.
,
Ishii
,
K.
, and
Noguchi
,
N.
,
2007
, “
Development of an Autonomous Navigation System Using a Two-Dimensional Laser Scanner in an Orchard Application
,”
Biosyst. Eng.
,
96
(
2
), pp.
139
149
.
26.
Heidari
,
F.
, and
Fotouhi
,
R.
,
2013
, “
A Human-Inspired Method for Mobile Robot Navigation
,”
ASME
Paper No. DETC2013-13523.
27.
Amoozgar
,
M.
,
Sadati
,
H.
, and
Alipour
,
K.
,
2012
, “
Trajectory Tracking of Wheeled Mobile Robots Using a Kinematical Fuzzy Controller
,”
Int. J. Rob. Autom.
,
27
(
1
), pp.
49
59
.
28.
Suo
,
T.
,
Yang
,
S.
, and
Anmin
,
Z.
,
2011
, “
A Novel GA-Based Fuzzy Controller for Mobile Robots in Dynamic Environments With Moving Obstacles
,”
Int. J. Rob. Autom.
,
26
(
2
), pp.
212
228
.
29.
Payne
,
V.
, and
Isaacs
,
L.
,
2012
,
Human Motor Development: A Lifespan Approach
,
8th ed.
,
McGraw-Hill
,
New York
.
30.
Fajen
,
B. R.
, and
Warren
,
W. H.
,
2003
, “
Behavioral Dynamics of Steering, Obstacle Avoidance, and Route Selection
,”
J. Exp. Psychol.
,
29
(
2
), pp.
343
362
.
31.
Frank
,
T. D.
,
Gifford
,
T. D.
, and
Chiangga
,
S.
,
2014
, “
Minimalistic Model for Navigation of Mobile Robots Around Obstacles Based on Complex-Number Calculus and Inspired by Human Navigation Behavior
,”
Math. Comput. Simul.
,
97
, pp.
108
122
.
32.
Huang
,
W. H.
,
Fajen
,
B. R.
,
Fink
,
J. R.
, and
Warren
,
W. H.
,
2006
, “
Visual Navigation and Obstacle Avoidance Using a Steering Potential Function
,”
Rob. Auton. Syst.
,
54
(
4
), pp.
288
299
.
33.
Campus Farm Field, University of Saskatchewan, Saskatchewan, Canada.
34.
CNH Farm Field North of Saskatoon, Saskatchewan, Canada.
You do not currently have access to this content.