Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-25T19:31:04.306Z Has data issue: false hasContentIssue false
  • English
  • Français

Virtual Reality based Mobile Robot Navigation in Greenhouse Environment

Published online by Cambridge University Press:  01 June 2017

M. Saiful Azimi ,
Z. A. Shukri  and
M. Zaharuddin
M. Saiful Azimi*
Affiliation:
Department of Control and Mechatronics, Faculty of Electrical Engineering, University of Technology Malaysia Skudai, 81310 Skudai, Johor, Malaysia
Z. A. Shukri
Affiliation:
Department of Control and Mechatronics, Faculty of Electrical Engineering, University of Technology Malaysia Skudai, 81310 Skudai, Johor, Malaysia
M. Zaharuddin
Affiliation:
Department of Control and Mechatronics, Faculty of Electrical Engineering, University of Technology Malaysia Skudai, 81310 Skudai, Johor, Malaysia
*
†E-mail: shukri@fke.utm.my
Get access

Abstract

The difficulties of transporting heavy mobile robots limit robotic experiments in agriculture. Virtual reality however, offers an alternative to conduct experiments in agriculture. This paper presents an application of virtual reality in a robot navigational experiment using SolidWorks and simulated into MATLAB. Trajectories were initiated using Probabilistic Roadmap and compared based on travel time, distance and tracking error, and the efficiency was calculated. The simulation results showed that the proposed method was able to conduct the navigational experiment inside the virtual environment. U-turn trajectory was chosen as the best trajectory for crop inspection with 82.7% efficiency.

Keywords

greenhousemobile robotnavigationvirtual reality.
Type
Agri-engineering
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 854 - 859
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bence, K, Geza, S, Ferenc, T, Mauricio, B and Peter, K 2016. A novel potential field method for path planning of mobile robots by adapting animal motion attributes. Robotics and Automation Systems 82, 24–34.Google Scholar
Bochtis, DD and Sorensen, CG 2009. The vehicle routing problem in field logistics part I. Biosystems Engineering 104, 447–457.Google Scholar
Chuan, TK, Shin, CN, Foo, D, Chao, WY and Wenhao, Y 2007. Modelling robot movement in a virtual environment. In: 10th IFAC, IFIP, IFORS, IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, Elsevier, pp. 177-182.Google Scholar
Kavraki, EL, Svetska, P, Latombe, J-C and Overmars, HM 1996. Probabilistic roadmap for path planning in high dimensional configuration spaces. IEEE Transactions on Robotic and Automation 12, 566–579.Google Scholar
Dias, T, Miraldo, P, Goncalves, N and U.Lima, P 2015. Augmented reality on robot navigation using non-central catadioptric cameras. In: 2015 IEEE/RSJ International Conference on Intelligent Robot and Systems (IROS), IEEE, pp. 4999-5004.Google Scholar
Eddarouich, S, Hammouch, A, Meriem, T, Touahni, R and Sbihi, A 2014. Unsupervised neural-morphological color image segmentation using mahalanobis criteria of resemblance. In: Multimedia Computing and Systems (ICMCS) International Conference, IEEE, pp. 314-320.Google Scholar
Felipe Martins, N, Wanderly Caleste, C, Carelli, M, Sarcinelli-Filho, M and Teodiano Bastos-Filho, F 2008. An adaptive dynamic controller for autonomous mobile robot trajectory tracking. Control Engineering Practice 16, 1354–1363.Google Scholar
Le, QH, Park, K-T and Yang, S-Y 2013. Study on development of easy operating system for field robot in virtual reality environment. In: 2013 3rd International Conference on Control, Automation and Systems (ICCAS 2013), ICROS, pp. 1152-1155.CrossRefGoogle Scholar
Li, WJ, Song, ZH, Zhu, ZX and Mao, ER 2016. Analysis and simulation of a 6R robot in virtual reality. In: 5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016, Elsevier, pp. 426-430.Google Scholar
Lutovac, MM, Dimic, Z, Mitrovic, S and Stepanovic, A 2015. Reconfigurable multi-robot virtual environment. In: 23rd Telecommunication forum TELFOR 2015, IEEE, pp. 954-957.Google Scholar
Menad, M, Derouiche, Z, Ahmed Foitih, Z and Nouibat, W 2013. Kinematic modelling of a humanoid robot with 18 DOF in a virtual environment. In: 2013 3rd International Conference on Innovative Computing Technology (INTECH), IEEE, pp. 424-429.Google Scholar
Mostefa, M, Kaddour El Boudadi, L, Loukil, A, Mohamed, K and Amine, D 2015. Design of mobile robot teleoperation system based on virtual reality. In: 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), IEEE, pp. 1-6.Google Scholar
Nakaoka, S 2012. Choreonoid: Extensible virtual robot environment built on an integrated GUI framework. In: 2012 IEEE/SICE International Symposium on System Integration (SII), IEEE, pp. 79-85.Google Scholar
Tian, S, Cui, X and Gong, Y 2014. A general vector-based algorithm to generate weighted voronoi diagrams based on ArcGIS engine. International Journal of Computer and Information Technology 3, 737–742.Google Scholar