Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[4] O.F. Gómez and U. E. Gómez, “Kinematic Simulation Of A Line Follower Robot For The Creation Of The Programming Videogame Rusty Roads In The Unity Framework,” Inf. Tecnol., vol. 28, no. 5, 2017, pp. 55–64, doi: 10.4067/s0718-07642017000500008. Search in Google Scholar

[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. Search in Google Scholar

[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/WRE 2018, 2018, pp. 604–609, doi: 10.1109/LARS/S BR/WRE.2018.00109. Search in Google Scholar

[7] H. A. Calderón, C. Mejía, and L. Cobo, “Implementación De Un Robot Móvil Seguidor De Línea Y Detector De Obstáculos Con Comunicación Bluetooth,” Review Ontare, vol. 4, no. 2, 2017, pp. 99–118, doi: 10.21158/23823399.v4.n2.20 16.1639. Search in Google Scholar

[8] S. Kokare, “Using ZigBee,” 2018 Fourth Int. Conf. Comput. Commun. Control Autom., 2018, pp. 1–5. Search in Google Scholar

[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/IC CT46177.2019.8969022. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[13] R. Javanmard, A.H. Zabbah, M. Karimi, and K. Jeddisaravi, “Line Following Autonomous Driving Robot Using Deep Learning,” 6th Iran. Conf. Signal Process. Intell. Syst. ICSPIS 2020, 2020, doi: 10.1109/ICSPIS51611.2020.9349547. Search in Google Scholar

[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. Search in Google Scholar

[15] A. Ghorbel, N. Ben Amor, and M. Jallouli, “Design Of A Flexible Reconfigurable Mobile Robot Localization System Using FPGA Technology,” SN Applied Science, vol. 2, no. 7, 2020, doi: 10.1007/s42452-020-2960-4. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[18] X. Wu, P. Jin, T. Zou, Z. Qi, H. Xiao, and P. Lou, “Backstepping Trajectory Tracking Based on Fuzzy Sliding Mode Control for Differential Mobile Robots,” Journal of Intelligent Robotics, vol. 96, no. 1, 2019, pp. 109–121, doi: 10.1007/s10846-019-00980-9. Search in Google Scholar

[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. Search in Google Scholar

[20] S. Tayal, H.P.G. Rao, S. Bhardwaj, and H. Aggarwal, “Line Follower Robot: Design and Hardware Application,” ICRITO 2020 - IEEE 8th Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir.), 2020, pp. 10–13, doi: 10.1109/ICRITO48 877.2020.9197968. Search in Google Scholar

[21] I.P. Latini, W.E. Barioni, M. Teixeira, F. Neves-Jr., and L.V.R. de Arruda, “Comparison between Line-Followers and Free Movement Robots in Tasks 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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[28] L. Screw and D. Loads, “Motor Torque Calculation,” Wire, no. 86, pp. 1–5. Search in Google Scholar

[29] B. Zeng, J. Zhang, L. Chen, and Y. Wang, “Selfbalancing 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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar