Environmental modeling of a robot which is needed for robot navigation and path planning is in the form of planar or 2D modeling. Several previous researchers have used laser sensor to model 2D obstacles because it has data accuracy for navigation. It is in a planar shape and implemented in quadrotor thus the obstacle modeling formed is in 2D. The problem in a 2D environment is that the path planning and navigation of the robot requires a considerable time because the robot stops at loca minima and attempts to find other paths in the dimension. The problem can be solved by using combined data taken from the laser sensor. The combination of the data uses several algorithms such as graph theory and vector field histogram algorithms. Therefore, this paper presents the combination of the algorithms to model a 3D environment. By using this model, the quadrotor is able to avoid loca minima.
REFERENCES
1.
J.
Morales
, J. L.
Martínez
, M. A.
Martínez
, and A.
Mandow
, EURASIP J. Adv. Signal Process
2009
, 1
–10
(2009
)2.
3.
K.
Lingemann
, N.
Andreas
, J.
Hertzberg
, and H.
Surmann
, Rob. Auton. Syst.
51
, 275
–296
(2005
).4.
D.
Gu
, S.
Bu
, G.
Xing
, B.
Ye
, and X.
Chen
, “Autonomous Relative Navigation with Laser Range Finder and Inertial Sensor for Uncooperative Spacecraft Rendezvous
,” in International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping-2011
(M2RSM
, 2011
),5.
T.
Yoshida
, K.
Irie
, E.
Koyanagi
, and M.
Tomono
, “A Sensor Platform for Outdoor Navigation Using Gyro-assisted Odometry and Roundly-swinging 3D Laser Scanner
,” in IEEE/RSJ International Conference on Intelligent Robots and Systems-2010
, (IROS
, 2010
), pp. 1414
–1420
.6.
C.
Kirsch
and C.
Rohrig
, “Position tracking using sensor fusion of a wireless network and a laser range finder
,” in 7th Workshop on Positioning, Navigation and Communication-2010
(IEEE
, 2010
), pp. 193
–199
.7.
S.
Mohottala
, S.
Ono
, M.
Kagesawa
, and K.
Ikeuchi
, “Fusion of a camera and a laser range sensor for vehicle recognition
,” in IEEE Conference on Computer Vision and Pattern Recognition-2009
, (IEEE
, 2009
), pp. 16
–23
.8.
Y.
Bok
, D. G.
Choi
, Y.
Jeong
, and I. S.
Kweon
, IEEE Int. Conf. Intell. Robot. Syst.
, 4436
–4441
(2011
).9.
D.
Gruyer
, A.
Cord
, and R.
Belaroussi
, “Target-to-track collaborative association combining a laser scanner and a camera
,” in IEEE Conf. Intell. Transp. Syst. Proceedings
, (IEEE
, 2013
) pp. 1125
–1130
.10.
R.
Solea
, G.
Veliche
, D.-C.
Cernega
, and M.-R.
Teaca
, “Indoor 3D object model obtained using data fusion from laser sensor and digital camera on a mobile robot
,” in Syst. Theory, Control Comput. ICSTCC 2013
(IEEE
, 2013
), pp. 479
–484
.11.
J.
Shin
, S.
Ji
, W.
Shon
, H.
Lee
, K.
Cho
, and S.
Park
, J Intell Robot Syst
61
, 15
–27
(2011
).12.
L. A.
Sandino
, M.
Bejar
, K.
Kondak
, and A.
Ollero
, J Intell Robot Syst
70
, 509
–525
(2013
).13.
P.
Pounds
, R.
Mahony
, and P.
Corke
, Control Eng. Pract.
18
, 691
–699
, (2010
).14.
X.
Zezhong
, Z.
Yanbin
, and C.
Huahua
, “Proc. World Congr. Intell. Control Autom.
2
, 8630
–8634
(2006
).15.
A.
Babinec
, F.
Duchoň
, M.
Dekan
, P.
Pásztó
, and M.
Kelemen
, Rob. Auton. Syst.
62
, 1098
–1115
(2014
).16.
D.
Habermann
and C.
Garcia
, “Obstacle Detection and Tracking Using Laser 2D
,” in 2010 Latin American Robotics Symposium and Intelligent Robotic Meeting (LARS)
, (IEEE
, 2010
), pp. 120
–125
.17.
P.
Kondaxakis
, S.
Kasderidis
, and P.
Trahanias
, “Tracking multiple targets from a mobile robot platform using a laser range scanner
, in 2008 IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications
, (IEEE
, 2008
), pp. 175
–184
.18.
K. C.
Tan
, K. K.
Tan
, T. H.
Lee
, S.
Zhao
, and Y. J.
Chen
, “Fuzzy Sensor Fusion And Reinforcement Learning
,” in Proceedings of the 2002 IEEE International Symposium on Intelligent Control
, (IEEE
, 2002
), pp. 182
–187
.19.
I.
Ulrich
and J.
Borenstein
, Proc. IEEE Intl. Conf. Robot. Autom.
, 3
, 2505
–2511
, (2000
).20.
T.
Behrens
, R.
Schärfig
, and T.
Winkler
, Lect. Notes Comput. Sci.
(including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics
) 5774
, 4
–15
(2009
).21.
Y.
Bresler
, “Optimum Visualization Of Vector Fields And Multi-sensor Fusion
,” in IEEE Seventh SP Workshop on Statistical Signal and Array Processing
(IEEE
, 1994
), pp. 91
–96
.22.
J. W.
Kwon
and D.
Chwa
, Robot. IEEE Trans.
28
, 1335
–1345
(2012
).23.
Iswanto
, O.
Wahyunggoro
, and A. I.
Cahyadi
, Int. J. Electr. Comput. Eng.
6
, 212
–222
(2016
).24.
25.
H.
Grigoryan
and H. a.
Harutyunyan
, J. Discret. Algorithms.
31
, 40
–47
(2015
).26.
Q.
Yang
and J.
Sun
, “An Underwater Autonomous Robot Based on Multi-sensor Data Fusion
,” in The Sixth World Congress on Intelligent Control and Automation Volume 2
(IEEE
, 2006
), pp. 9139
–9143
.
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