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The paper presents an approach for controlling a line- following robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following robot on a running track with different configurations is then evaluated. The results show that the line-following robot responded more efficiently with an artificial neural network control algorithm than with a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following robot to adapt to environmental conditions and overcome obstacles on the track more effectively.
Słowa kluczowe
Rocznik
Tom
Strony
35--42
Opis fizyczny
Bibliogr. 31 poz., rys.
Twórcy
autor
- Department of Electronics, Instituto Tecnológico Superior Rumiñahui, Sangolquí, 171103, Ecuador
autor
- Department of Electronics, Instituto Tecnológico Superior Rumiñahui, Sangolquí, 171103, Ecuador
autor
- Department of Electronics, Instituto Tecnológico Superior Rumiñahui, Sangolquí, 171103, Ecuador
autor
- Department of Electronics, Instituto Tecnológico Superior Rumiñahui, Sangolquí, 171103, Ecuador
Bibliografia
- [1] O. Gumus, M. Topaloglu, and D. Ozcelik, “The Use of Computer Controlled Line Follower Robots in Public Transport,” Procedia Comput. Sci., vol. 102, no. August, 2016, pp. 202-208, doi:10.1016/j.procs.2016.09.390.
- [2] A. Latif, H. A. Widodo, R. Rahim, and K. Kunal, “Implementation of Line Follower Robot Based Microcontroller atmega32a,” J. robot. Control, vol. 1, no. 3, 2020, pp. 70-74, doi: 10.18196/jrc.1316.
- [3] M. Antony, M. Parameswaran, N. Mathew, V.S. Sajithkumar, J. Joseph, and C.M. Jacob, “Design And Implementation Of Automatic Guided Vehicle For Hospital Application,” Proc. 5th Int. Conf. Commun. Electron. Syst. ICCES 2020, no. Icces, 2020, pp. 1031-1036, doi: 10.1109/ICCES48766.2020.09137867.
- [4] O.F. Gómez and U. E. Gómez, “Kinematic Simulation Of A Line Follower Robot For The CreationOf The Programming Videogame Rusty Roads In The Unity Framework,” Inf. Tecnol., vol. 28, no. 5, 2017, pp. 55-64, doi: 10.4067/s0718-07642017000500008.
- [5] A. Aharari and Y. Ueda, “Low Pass Filter Applied to Color Sensor of Line Follower Robot,” Procedia Computer Science, vol. 154, 2018, pp. 693-698, doi: 10.1016/j.procs.2019.06.108.
- [6] V.G.R. Caitite, D.M.G. Dos Santos, I.C. Gregorio, W.B. Da Silva, and V.F. Mendes, “Diffusion Of Robotics Through Line Follower Robots,” Proc. -15th Lat. Am. robot. Symp. 6th Brazilian robot. Symp. 9th Work. robot. Educ. LARS/SBR/WRE2018, 2018, pp. 604-609, doi: 10.1109/LARS/SBR/WRE.2018.00109.
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- [8] S. Kokare, “Using ZigBee,” 2018 Fourth Int. Conf. Comput. Commun. Control Autom., 2018, pp. 1-5.
- [9] J. Chaudhari, A. Desai, and S. Gavarskar, “Line following Robot Using Arduino For Hospitals,” 2019 2nd Int. Conf. Intell. Commun. Comput. Tech. ICCT 2019, 2019, pp. 330-332, doi: 10.1109/ICCT46177.2019.8969022.
- [10] W.K. Born and C.J. Lowrance, “Application of Convolutional Neural Network Image Classification for a Path-Following Robot,” 2018 IEEE MIT Undergrad. Res. Technol. Conf. URTC 2018, 2018, pp. 11-14, doi: 10.1109/URTC45901.2018.9244781.
- [11] C.F. Hsu, C.T. Su, W.F. Kao, and B.K. Lee, “Vision-Based Line-Following Control of a Two-Wheel Self-Balancing Robot,” Proc. Int. Conf. Mach. Learn. Cybern., vol. 1, 2018, pp. 319-324, doi: 10.1109/ICMLC.2018.8526952.
- [12] J. Sarwade, S. Shetty, A. Bhavsar, M. Mergu, and A. Talekar, “Line Following Robot Using Image Processing,” Proc. 3rd Int. Conf. Comput. Methodol. Commun. ICCMC 2019, no. Iccmc, 2019, pp. 1174-1179, doi: 10.1109/ICCMC.2019.8819826.
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- [14] A. Moulay, F. Laoufi, T. Benslimane, and O. Abdelkhalek, “FPGA-Based Car-Like Robot Path Follower with Obstacle Avoidance,” Proc. 2020 Int. Conf. Math. Inf. Technol. ICMIT 2020, 2020,pp. 125-131, doi: 10.1109/ICMIT47780.2020.9047008.
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- [16] M.A. Kader, M.Z. Islam, J. Al Rafi, M.R. Islam,and F.S. Hossain, “Line Following Autonomous Office Assistant Robot with PID Algorithm,” 2018 Int. Conf. Innov. Sci. Eng. Technol. ICISET 2018, no. October, 2018, pp. 109-114, doi:10.1109/ICISET.2018.8745606.
- [17] D. Nikolov, G. Zafirov, I. Stefanov, K. Nikov, and S. Stefanova, “Autonomous Navigation And Speed Control For Line Following Robot,” 2018 IEEE 27th Int. Sci. Conf. Electron. 2018 - Proc., 2018, pp. 1-4, doi: 10.1109/ET.2018.8549580.
- [18] X. Wu, P. Jin, T. Zou, Z. Qi, H. Xiao, and P. Lou, “Backstepping Trajectory Tracking Based on Fuzzy Sliding Mode Control forDifferential Mobile Robots,” Journal of Intelligent Robotics, vol. 96, no. 1, 2019, pp. 109-121, doi: 10.1007/s10846-019-00980-9.
- [19] M. S. Gharajeh and H. B. Jond, “Speed Control For Leader-Follower Robot Formation Using Fuzzy System And Supervised Machine Learning,” Sensors, vol. 21, no. 10, 2021, pp. 1-14, doi:10.3390/s21103433.
- [20] S. Tayal, H.P.G. Rao, S. Bhardwaj, and H. Aggarwal, “Line Follower Robot: Design and HardwareApplication,” ICRITO 2020 - IEEE 8th Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir.), 2020, pp. 10-13, doi: 10.1109/ICRITO48877.2020.9197968.
- [21] I.P. Latini, W.E. Barioni, M. Teixeira, F. Neves-Jr.,and L.V.R. de Arruda, “Comparison betweenLine-Followers and Free Movement Robots inTasks Execution in a Simulated Environment,” in 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), 2022, pp. 145-150. doi: 10.1109/LARS/SBR/WRE56824.2022.9995776.
- [22] R. Farkh, K. Al Jaloud, S. Alhuwaimel, M. T. Quasim, and M. Ksouri, “A Deep Learning Approach For The Mobile-Robot Motion Control System,” Intelligent Automation & Soft Computing, vol. 29, no. 2, 2021, pp. 423-435, doi: 10.32604/iasc.2021.016219.
- [23] A. Roy and M.M. Noel, “Design Of A High-Speed Line Following Robot That Smoothly Follows Tight Curves,” Computer and Electrical Engineering, vol. 56, 2016, pp. 732-747, doi: 0.1016/j.compeleceng.2015.06.014.
- [24] B. G. Fernández et al., “Robotics vs. Game-Console-Based Platforms to Learn Computer Architecture,” IEEE Access, vol. 8, 2020, pp. 95153-95169, doi: 10.1109/ACCESS.2020.2994196.
- [25] M.H. Nushra, Q.A. Rahman, S.M.F. Mursalin, N.B. Asad, M.M. Asif Syeed, and M.M. Islam, “Smart Car Parking With The Assistance Of Line Following Robot,” 2019 Int. Conf. Sustain. Technol. Ind. 4.0, STI 2019, vol. 0, 2019, pp. 24-25, doi: 10.1109/STI47673.2019.9068046.
- [26] J.W. Lok, W.M.W. Muda, and A.N. Woro, “Development Of Warehouse Robot With Advanced Line Following And Background Color Sensors,” Journal of Advanced Manufacturing Technology, vol. 15, no. 2, 2021, pp. 23-34.
- [27] H. Murcia, J.D. Valenciano, and Y. Tapiero, “Development of a Line-Follower Robot for Robotic Competition Purposes,” Applied Computer Sciences in Engineering, 2018, pp. 464-474.
- [28] L. Screw and D. Loads, “Motor Torque Calculation,” Wire, no. 86, pp. 1-5.
- [29] B. Zeng, J. Zhang, L. Chen, and Y. Wang, “Self-balancing car based on ARDUINO UNO R3,” 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2018, pp. 1939-1943. doi: 10.1109/IAEAC.2018.8577775.
- [30] R. Chai, H. Niu, J. Carrasco, F. Arvin, H. Yin, and B. Lennox, “Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment,” IEEE Trans. Neural Networks Learn. Syst., 2022, doi: 10.1109/TNNLS.2022.3209154.
- [31] T. Guillod, P. Papamanolis, and J.W. Kolar, “Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design,” IEEE Open J. Power Electron., vol. 1, 2020, pp. 284-299, doi: 0.1109/OJPEL.2020.3012777.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9d721020-1303-43cd-abbd-ce2901808299