Abstract

An accurate modeling, simulation, and estimation of the wheel-terrain interaction and its effects on a robot movement plays a key role in control and navigation tasks, specially in constantly changing environments. We study the calibration of wheel slip models using Particle Markov Chain Monte Carlo methods to approximate the posterior distributions of their parameters. In contrast to classic identification approaches, considering the parameters as random variables allows to obtain a probability measure of the parameter estimations and subsequently propagate their uncertainty to wheel slip-related variables. Extensive simulation and experimental results showed that the proposed methodology can effectively get reliable posterior approximations from noisy sensor measurements in changing terrains. Validation tests also include the applicability assessment of the proposed methodology by comparing it with the integrated prediction error minimization methodology. Field results presented up to 66% of improvement in the robot motion prediction with the proposed calibration approach.

References

1.
Auat Cheein
,
F. A.
, and
Carelli
,
R.
,
2013
, “
Agricultural Robotics: Unmanned Robotic Service Units in Agricultural Tasks
,”
IEEE Ind. Electron. Mag.
,
7
(
3
), pp.
48
58
.
2.
Wang
,
Y.
,
Nguyen
,
B. M.
, and
Kotchapansompote
,
P.
,
2012
, “
Vision-Based Vehicle Body Slip Angle Estimation With Multi-Rate Kalman Filter Considering Time Delay
,”
IEEE International Symposium on Industrial Electronics
,
Hangzhou, China
, pp.
1506
1511
.
3.
Urmson
,
C.
,
Anhalt
,
J.
,
Bagnell
,
D.
,
Baker
,
C.
,
Bittner
,
R.
,
Clark
,
M. N.
,
Dolan
,
J.
,
Duggins
,
D.
,
Galatali
,
T.
,
Geyer
,
C.
,
Gittleman
,
M.
,
Harbaugh
,
S.
,
Hebert
,
M.
,
Howard
,
T. M.
,
Kolski
,
S.
,
Kelly
,
A.
,
Likhachev
,
M.
,
McNaughton
,
M.
,
Miller
,
N.
,
Peterson
,
K.
,
Pilnick
,
B.
,
Rajkumar
,
R.
,
Rybski
,
P.
,
Salesky
,
B.
,
Seo
,
Y. W.
,
Singh
,
S.
,
Snider
,
J.
,
Stentz
,
A.
,
Whittaker
,
W.
,
Wolkowicki
,
Z.
,
Ziglar
,
J.
,
Bae
,
H.
,
Brown
,
T.
,
Demitrish
,
D.
,
Litkouhi
,
B.
,
Nickolaou
,
J.
,
Sadekar
,
V.
,
Zhang
,
W.
,
Struble
,
J.
,
Taylor
,
M.
,
Darms
,
M.
, and
Ferguson
,
D.
,
2008
, “
Autonomous Driving in Urban Environments: Boss and the Urban Challenge
,”
J. Field Robotics
,
25
(
8
), pp.
425
466
.
4.
Wong
,
J.
,
2001
,
Theory of Ground Vehicles
, Vol.
53
,
John Wiley and Sons
.
5.
Liu
,
Z.
,
Guo
,
J.
,
Ding
,
L.
,
Gao
,
H.
,
Guo
,
T.
, and
Deng
,
Z.
,
2019
, “
Online Estimation of Terrain Parameters and Resistance Force Based on Equivalent Sinkage for Planetary Rovers in Longitudinal Skid
,”
Mech. Syst. Signal. Process.
,
119
(
15
), pp.
39
54
.
6.
Hoang
,
N. B.
, and
Kang
,
H. J.
,
2016
, “
Neural Network-based Adaptive Tracking Control of Mobile Robots in the Presence of Wheel Slip and External Disturbance Force
,”
Neurocomputing
,
188
, pp.
12
22
.
7.
Guizilini
,
V.
, and
Ramos
,
F.
,
2019
, “
Variational Hilbert Regression for Terrain Modeling and Trajectory Optimization
,”
Int. J. Robot. Res.
,
38
(
12–13
), pp.
1375
1387
.
8.
Taheri
,
S.
,
Sandu
,
C.
,
Taheri
,
S.
,
Pinto
,
E.
, and
Gorsich
,
D.
,
2015
, “
A Technical Survey on Terramechanics Models for Tire-terrain Interaction Used in Modeling and Simulation of Wheeled Vehicles
,”
J. Terramech.
,
57
, pp.
1
22
.
9.
Reina
,
G.
, and
Messina
,
A.
,
2019
, “
Vehicle Dynamics Estimation Via Augmented Extended Kalman Filtering
,”
Measurement
,
133
, pp.
383
395
.
10.
Lindsten
,
F.
,
2013
, “
Particle Filters and Markov Chains for Learning of Dynamical Systems
,”
Ph.D. thesis
,
Linköping University
.
11.
Song
,
X.
,
Gao
,
H.
,
Ding
,
L.
,
Spanos
,
P. D.
,
Deng
,
Z.
, and
Li
,
Z.
,
2016
, “
Locally Supervised Neural Networks for Approximating Terramechanics Models
,”
Mech. Syst. Signal. Process.
,
75
(
15
), pp.
27
74
.
12.
Padmanabhan
,
C.
,
Gupta
,
S.
, and
Mylswamy
,
A.
,
2018
, “
Estimation of Terramechanics Parameters of Wheel-Soil Interaction Model Using Particle Filtering
,”
J. Terramech.
,
79
, pp.
79
95
.
13.
Solin
,
A.
,
Kok
,
M.
,
Wahlstr
,
N.
, and
Sch
,
T. B.
,
2018
, “
Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes
,”
IEEE Trans. Robot.
,
34
(
4
), pp.
1112
1127
.
14.
Seegmiller
,
N. A.
,
2014
, “
Dynamic Model Formulation and Calibration for Wheeled Mobile Robots
,”
Ph.D. thesis
,
Carnegie Mellon University
.
15.
Seegmiller
,
N.
,
Rogers-marcovitz
,
F.
,
Miller
,
G.
, and
Kelly
,
A.
,
2013
, “
Vehicle Model Identification by Integrated Prediction Error Minimization
,”
Int. J. Robot. Res.
,
32
(
8
), pp.
912
931
.
16.
Angelova
,
A.
,
Matthies
,
L.
,
Helmick
,
D.
, and
Perona
,
P.
,
2007
, “
Learning and Prediction of Slip From Visual Information
,”
J. Field Robot.
,
24
(
3
), pp.
205
231
.
17.
Cunningham
,
C.
,
Ono
,
M.
,
Nesnas
,
I.
,
Yen
,
J.
, and
Whittaker
,
W. L.
,
2017
, “
Locally-Adaptive Slip Prediction for Planetary Rovers Using Gaussian Processes
,”
IEEE International Conference on Robotics and Automation (ICRA)
,
Marina Bay Sands, Singapore
, pp.
5487
5494
.
18.
Omura
,
T.
, and
Ishigami
,
G.
,
2017
, “
Wheel Slip Classification Method for Mobile Robot in Sandy Terrain Using In-Wheel Sensor
,”
J. Robot. Mechatron.
,
29
(
5
), pp.
902
910
.
19.
Ordonez
,
C.
,
Gupta
,
N.
,
Reese
,
B.
,
Seegmiller
,
N.
,
Kelly
,
A.
, and
Collins
,
E. G.
,
2017
, “
Learning of Skid-Steered Kinematic and Dynamic Models for Motion Planning
,”
Rob. Auton. Syst.
,
95
, pp.
207
221
.
20.
Bogdanski
,
K.
, and
Best
,
M. C.
,
2018
, “
Kalman and Particle Filtering Methods for Full Vehicle and Tyre Identification
,”
Veh. Syst. Dyn.
,
56
(
5
), pp.
769
790
.
21.
Pranav
,
P. K.
,
Tewari
,
V. K.
,
Pandey
,
K. P.
, and
Jha
,
K. R.
,
2012
, “
Automatic Wheel Slip Control System in Field Operations for 2WD Tractors
,”
Comput. Electron. Agricult.
,
84
, pp.
1
6
.
22.
Yi
,
J.
,
Member
,
S.
,
Wang
,
H.
,
Zhang
,
J.
, and
Member
,
S.
,
2009
, “
Kinematic Modeling and Analysis of Skid-Steered Mobile Robots With Applications to Low-Cost Inertial-Measurement-Unit-Based Motion Estimation
,”
IEEE Trans. Robot.
,
25
(
5
), pp.
1087
1097
.
23.
Helmick
,
D. M.
,
Roumeliotis
,
S. I.
, and
Cheng
,
Y.
,
2006
, “
Slip-Compensated Path Following for Planetary Exploration Rovers
,”
Adv. Robot.
,
20
(
11
), pp.
1257
1280
.
24.
Gonzalez
,
R.
,
Fiacchini
,
M.
, and
Iagnemma
,
K.
,
2018
, “
Slippage Prediction for Off-road Mobile Robots Via Machine Learning Regression and Proprioceptive Sensing
,”
Robot. Auton. Syst.
,
105
, pp.
85
93
.
25.
Ishigami
,
G.
,
Kewlani
,
G.
, and
Iagnemma
,
K.
,
2009
, “
Predictable Mobility
,”
IEEE Robot. Auton. Mag.
,
16
(
4
), pp.
61
70
.
26.
Andrieu
,
C.
,
De Freitas
,
N.
,
Doucet
,
A.
, and
Jordan
,
M.
,
2003
, “
An Introduction to MCMC for Machine Learning
,”
Mach. Learn.
,
50
(
1
), pp.
5
43
.
27.
Lindsten
,
F.
, and
Schön
,
T.
,
2013
, “
Backward Simulation Methods for Monte Carlo Statistical Inference
,”
Found. Trends Mach. Learn.
,
6
(
1
), pp.
1
143
.
28.
Kroese
,
D. P.
,
Taimre
,
T.
, and
Botev
,
Z.
,
2011
,
Handbook of Monte Carlo Methods
, 1st ed.,
John Wiley and Sons
,
New York
.
29.
Andrieu
,
C.
,
Doucet
,
A.
, and
Holenstein
,
R.
,
2010
, “
Particle Markov Chain Monte Carlo Methods
,”
J. R. Statist. Soc.
,
72
(
3
), pp.
269
342
.
30.
Brooks
,
S.
,
Gelman
,
A.
,
Galin
,
J.
, and
Xiao-Li
,
M.
,
2011
,
Handbook of Markov Chain Monte Carlo
,
Chapman and Hall
.
31.
Lindsten
,
F.
,
Jordan
,
M.
, and
Schön
,
T.
,
2014
, “
Particle Gibbs with Ancestor Sampling
,”
J. Mach. Learn. Res.
,
15
(
1
), pp.
2145
2184
.
32.
Lindsten
,
F.
,
Schön
,
T. B.
, and
Jordan
,
M. I.
,
2013
, “
Bayesian Semiparametric Wiener System Identification
,”
Automatica
,
49
(
7
), pp.
2053
2063
.
33.
Yang
,
R.
,
Drive
,
R.
, and
Berger
,
J. O.
,
1998
, “
A Catalog of Noninformative Priors
,”
Technical Report
,
Duke University
.
34.
Seegmiller
,
N.
, and
Kelly
,
A.
,
2016
, “
High-Fidelity Yet Fast Dynamic Models of Wheeled Mobile Robots
,”
IEEE Trans. Robot.
,
32
(
3
), pp.
614
625
.
35.
Yandun
,
F. J.
,
2019
, “
Perception of Wheeled Mobile Robots in Agriculture: Phenotyping and Mobility Assessment
,”
Ph.D. thesis
,
Universidad Técnica Federico Santa María
.
36.
Antonelli
,
G.
,
Chiaverini
,
S.
, and
Fusco
,
G.
,
2005
, “
A Calibration Method for Odometry of Mobile Robots Based on the Least-Squares Technique: Theory
,”
IEEE Trans. Robot.
,
21
(
5
), pp.
994
1004
.
37.
Wong
,
J. Y.
,
2010
,
Terramechanics and Off-Road Vehicle Engineering
, 2nd ed.,
Butterworth-Heinemann
.
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