DEVELOPMENT OF ARTIFICIAL NEURAL NETWORK STRUCTURES FOR PREDICTING NAVIGATION TASKS OF A MOBILE ROBOT
DOI:
https://doi.org/10.29121/ijetmr.v7.i3.2020.553Keywords:
Artificial Neural Network, Robot Navigation, SCITOS G5Abstract
Determining trajectories in mobile robot navigation tasks is a difficult process to apply with conventional methods. Therefore, intelligent techniques produce highly effective results in trajectory optimization and orientation prediction. In this study, two different ANN (Artificial Neural Network) structures have been developed for the navigation prediction of the SCITOS G5 mobile robot. For this aim, RBF (Radial Basis Function) and MLP (Multi-Layer Perceptron) structures were used. Information obtained from 24 sensors of the robot was used as network inputs and network output determines robot direction. Accordingly, structures that have 24 inputs and one output were created. The best performance network structures obtained were compared among them in simulation environment. Accordingly, RBF has been observed to produce more accurate results than MLP.
Downloads
References
Sigalas, M. Baltzakis, H., & Trahanias, P. Temporal gesture recognition for human-robot interaction. Month, 2010.
P. C. Giulianotti. Robotics in general surgery. Robotics in General Surgery, 138(July 2003), 2014, 1–511. DOI: https://doi.org/10.1001/archsurg.138.7.777
Gul, F., Rahiman, W., & Nazli Alhady, S. S. A comprehensive study for robot navigation techniques. Cogent Engineering, 6(1), 2019, 1–25. DOI: https://doi.org/10.1080/23311916.2019.1632046
M. Knudson and K. Tumer. Adaptive Navigation for Autonomous Robots, Robotics and Autonomous Systems, 59, 2011, 410–420. DOI: https://doi.org/10.1016/j.robot.2011.02.004
Eski, I., & Yildirim, Ş. Design of neural network control system for controlling trajectory of autonomous underwater vehicles. International Journal of Advanced Robotic Systems, 11(1), 2014. DOI: https://doi.org/10.5772/56740
Fabiani, P., Fuertes, V., Piquereau, A., Mampey, R., & Teichteil-Königsbuch, F. Autonomous flight and navigation of VTOL UAVs: from autonomy demonstrations to out-of-sight flights. Aerospace Science and Technology, 11(2–3), 2007, 183–193. DOI: https://doi.org/10.1016/j.ast.2006.05.005
N. Najmaei and M. R. Kermani, "Applications of Artificial Intelligence in Safe Human–Robot Interactions," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 41, no. 2, 2011, 448-459. DOI: https://doi.org/10.1109/TSMCB.2010.2058103
Mendes Júnior, J. J. A., Pires, M. B., Vieira, M. E. M., Okida, S., & Stevan Jr, S. L. Neural Network to Failure Classification in Robotic Systems. Brazilian Journal of Instrumentation and Control, 4(1), 1, 2016. DOI: https://doi.org/10.3895/bjic.v4n1.4663
Kruse, T., Pandey, A. K., Alami, R., & Kirsch, A. Human-aware robot navigation: A survey. Robotics and Autonomous Systems, 61(12), 2013, 1726–1743. DOI: https://doi.org/10.1016/j.robot.2013.05.007
Panigrahi, P. K., Ghosh, S., & Parhi, D. R. Intelligent Leaning and Control of Autonomous Mobile Robot using MLP and RBF based Neural Network in Clustered Environment. 5(6), 2014, 313–316.
Shinzato, P. Y., Fernandes, L. C., Osorio, F. S., & Wolf, D. F. Path recognition for outdoor navigation using artificial neural networks: Case study. Proceedings of the IEEE International Conference on Industrial Technology, https://doi.org/10.1109/ICIT.2010.5472489, 2010, 1457–1462. DOI: https://doi.org/10.1109/ICIT.2010.5472489
Shinzato, P. Y., & Wolf, D. F. A road following approach using artificial neural networks combinations. Journal of Intelligent and Robotic Systems: Theory and Applications, 62(3–4), https://doi.org/10.1007/s10846-010-9463-2, 2011, 527–546. DOI: https://doi.org/10.1007/s10846-010-9463-2
Dezfoulian, S. H., Wu, D., & Ahmad, I. S. A generalized neural network approach to mobile robot navigation and obstacle avoidance. Advances in Intelligent Systems and Computing, 193 AISC (VOL. 1), 2013. DOI: https://doi.org/10.1007/978-3-642-33926-4_3
Wang, X., Hou, Z. G., Lv, F., Tan, M., & Wang, Y. Mobile robots’ modular navigation controller using spiking neural networks. Neurocomputing, 134, DOI: https://doi.org/10.1016/j.neucom.2013.07.055
https://doi.org/10.1016/j.neucom.2013.07.055, 2014, 230–238. DOI: https://doi.org/10.1016/j.neucom.2013.07.055
Malleswaran, M., Angel Deborah, S., Manjula, S., & Vaidehi, V. Integration of INS and GPS using radial basis function neural networks for vehicular navigation. 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, (December), DOI: https://doi.org/10.1109/ICARCV.2010.5707295
https://doi.org/10.1109/ICARCV.2010.5707295, 2010, 2427–2430. DOI: https://doi.org/10.1109/ICARCV.2010.5707295
Budianto, A., Pangabidin, R., Syai’In, M., Adhitya, R. Y., Subiyanto, L., Khumaidi, A., Soelistijono, R. T. Analysis of artificial intelligence application using back propagation neural network and fuzzy logic controller on wall-following autonomous mobile robot. 2017 International Symposium on Electronics and Smart Devices, ISESD 2017, 2018-January (1), . DOI: https://doi.org/10.1109/ISESD.2017.8253306
https://doi.org/10.1109/ISESD.2017.8253306, 2017, 62–66. DOI: https://doi.org/10.1109/ISESD.2017.8253306
Dash, T., Nayak, T., & Swain, R. R. Controlling wall following robot navigation based on gravitational search and feed forward neural network. ACM International Conference Proceeding Series, 26-27-February-2015, https://doi.org/10.1145/2708463.2709070, 2015, 196–200. DOI: https://doi.org/10.1145/2708463.2709070
Ozbay Karakus, M., & Er, O. Learning of Robot Navigation Tasks by Probabilistic Neural Network, https://doi.org/10.5121/csit.2013.3803, 2013, 23–34. DOI: https://doi.org/10.5121/csit.2013.3803
Larasati, N., Dewi, T., & Oktarina, Y. Object Following Design for a Mobile Robot using Neural Network. Computer Engineering and Applications Journal, 6(1), DOI: https://doi.org/10.18495/comengapp.v6i1.189
https://doi.org/10.18495/comengapp.v6i1.189, 2017, 5–14. DOI: https://doi.org/10.18495/comengapp.v6i1.189
Dash, T., Soumya, R. S., Nayak, T., & Mishra, G. (2015). Neural network approach to control wall-following robot navigation, ICACCCT, 2014. DOI: https://doi.org/10.1109/ICACCCT.2014.7019262
Faisal, M., Hedjar, R., Al Sulaiman, M., & Al-Mutib, K. Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment. International Journal of Advanced Robotic Systems, 2013. DOI: https://doi.org/10.5772/54427
Singh, N. H., & Thongam, K. Mobile Robot Navigation Using MLP-BP Approaches in Dynamic Environments. Arabian Journal for Science and Engineering, 43(12), DOI: https://doi.org/10.1007/s13369-018-3267-2
https://doi.org/10.1007/s13369-018-3267-2, 2018, 8013–8028. DOI: https://doi.org/10.1007/s13369-018-3267-2
Mucientes, M., Moreno, D. L., Bugarín, A., & Barro, S. Design of a fuzzy controller in mobile robotics using genetic algorithms. Applied Soft Computing Journal, 2007, 540–546. DOI: https://doi.org/10.1016/j.asoc.2005.05.007
S. F. Desouky and H. M. Schwartz, "Genetic based fuzzy logic controller for a wall-following mobile robot," 2009 American Control Conference, St. Louis, 2009, 3555-3560. DOI: https://doi.org/10.1109/ACC.2009.5159805
A. Frank, A. Asuncion, “UCI Machine Learning Repository,” 2010.
Bishop, C.M., Neural Networks for Pattern Recognition. Oxford University Press Inc. New York, NY, ISBN: 0198538642, 1995.
Kashaninejad M. ve Dehghani A.A., Kashiri M., Modeling of wheat soaking using two artificial neural networks (MLP and RBF), Journal of Food Engineering, 2009, 602–607. DOI: https://doi.org/10.1016/j.jfoodeng.2008.10.012
Celikoglu H.B. ve Cigizoglu H.K., “Modelling public transport trips by radial basis function neural networks,” Mathematical and Computer Modelling, 2007, 480–489. DOI: https://doi.org/10.1016/j.mcm.2006.07.002
Yu, B., He, X., Training radial basis function networks with differential evolution. In: Proceedings of IEEE International Conference on Granular Computing, Atlanta, USA, 2006.
Hwang Y.S. ve Bang S.Y., “An Efficient Method to Construct a Radial Basis Function Neural Network Classifier,” 1996. DOI: https://doi.org/10.1016/S0893-6080(97)00002-6
Downloads
Published
How to Cite
Issue
Section
License
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere.
- That its release has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with International Journal of Engineering Technologies and Management Research agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or edit it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
For More info, please visit CopyRight Section
