Issue

Vol. 8, No. 1, February 2023

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

https://doi.org/10.22219/kinetik.v8i1.1614
Achmad Imam Sudianto
Universitas Brawijaya
Muhammad Aziz Muslim
Universitas Brawijaya
Moch Rusli
Universitas Brawijaya

Corresponding Author(s) : Achmad Imam Sudianto

imamsudianto@student.ub.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 8, No. 1, February 2023

Abstract

Every mobile robot mission starts with the robot being moved to the task site. From there, the robot executes its tasks. A control system is required to move the mobile robot's actuator (which may be in the shape of wheels or legs) and comprehend the environment around the robot to perform these movements (perception). This research aims to develop a technique to control a robot’s movement while detecting obstacles and distances toward an object. The robot is equipped with LIDAR and a camera to perform these tasks. The control is divided into two major parts, low-level and high-level controller. As part of a low-level controller robot, the Model Predictive Control (MPC) method is proposed to help with the control of the wheel while the Artificial Neural Network (ANN) approach to use in this study to identify obstacles and the Convolutional Neural Network (CNN) method for detecting objects, both ANN and CNN as a control for high-level part of the robot. The results of this study can prove that CNN can help detect existing objects with a value of 45% for detecting some objects. The obtained result from the MPC method, which has been combined with an ANN as an obstacle detector, is that the smaller the horizon value, the shorter the time needed to reach the desired coordinates with the result being 45 seconds.

Keywords

LIDAR Camera Artificial Neural Network Model Predictive Control Convolutional Neural Network Mobile Robot
Sudianto, A. I., Muslim, M. A., & Rusli, M. (2023). Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 8(1), 461-472. https://doi.org/10.22219/kinetik.v8i1.1614
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Rafaila, R. C., Caruntu, C. F., & Livint, G. (2016). Centralized model predictive control of autonomous driving vehicles with Lyapunov stability. 2016 20th International Conference on System Theory, Control and Computing, ICSTCC 2016 - Joint Conference of SINTES 20, SACCS 16, SIMSIS 20 - Proceedings, 663–668. https://doi.org/10.1109/ICSTCC.2016.7790742
  2. Peng, Y., Qu, D., Zhong, Y., Xie, S., Luo, J., & Gu, J. (2015). The obstacle detection and obstacle avoidance algorithm based on 2-D lidar. 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In Conjunction with 2015 IEEE International Conference on Automation and Logistics, 1648–1653. https://doi.org/10.1109/ICInfA.2015.7279550
  3. Hutabarat, D., Rivai, M., Purwanto, D., & Hutomo, H. (2019). Lidar-based obstacle avoidance for the autonomous mobile robot. Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019, 197–202. https://doi.org/10.1109/ICTS.2019.8850952
  4. Kaleci, B., Turgut, K., & Dutagaci, H. (2022). 2DLaserNet: A deep learning architecture on 2D laser scans for semantic classification of mobile robot locations. Engineering Science and Technology, an International Journal, 28. https://doi.org/10.1016/j.jestch.2021.06.007
  5. Maddalena, E. T., da Moraes, C. G. S., Waltrich, G., & Jones, C. N. (2020). A neural network architecture to learn explicit MPC controllers from data. IFAC-PapersOnLine, 53(2), 11362–11367. https://doi.org/10.1016/j.ifacol.2020.12.546
  6. Ghorpade, D., Thakare, A. D., & Doiphode, S. (2018, September 11). Obstacle Detection and Avoidance Algorithm for Autonomous Mobile Robot using 2D LiDAR. 2017 International Conference on Computing, Communication, Control and Automation, ICCUBEA 2017. https://doi.org/10.1109/ICCUBEA.2017.8463846
  7. Shalumov, A., Halaly, R., & Tsur, E. E. (2021). LiDAR-driven spiking neural network for collision avoidance in autonomous driving. Bioinspiration and Biomimetics, 16(6). https://doi.org/10.1088/1748-3190/ac290c
  8. Tavernini, D., Metzler, M., Gruber, P., & Sorniotti, A. (2019). Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control. IEEE Transactions on Control Systems Technology, 27(4), 1438–1451. https://doi.org/10.1109/TCST.2018.2837097
  9. S. Sahoo, B. Subudhi and G. Panda, "Optimal speed control of DC motor using linear quadratic regulator and model predictive control," 2015 International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), 2015, pp. 1-5, https://doi.org/10.1109/EPETSG.2015.7510130
  10. D. Tavernini, M. Metzler, P. Gruber and A. Sorniotti, "Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control," in IEEE Transactions on Control Systems Technology, vol. 27, no. 4, pp. 1438-1451, July 2019, https://doi.org/10.1109/TCST.2018.2837097
  11. Giusti A, Cireşan D C, Masci J, Gambardella L M and Schmidhuber J “Fast image scanning with deep max-pooling convolutional neural networks”. IEEE Int. Conf. Image Process. 2013 https://doi.org/10.48550/arXiv.1302.1700
  12. Guerrero-Higueras, Á. M., Álvarez-Aparicio, C., Calvo Olivera, M. C., Rodríguez-Lera, F. J., Fernández-Llamas, C., Rico, F. M., & Matellán, V. (2019). Tracking people in a mobile robot from 2D lidar scans using full convolutional neural networks for security in cluttered environments. Frontiers in Neurorobotics, 13. https://doi.org/10.3389/fnbot.2018.00085
  13. Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. https://doi.org/10.48550/arXiv.1804.02767
  14. M. Marian, F. Stîngă, M. -T. Georgescu, H. Roibu, D. Popescu and F. Manta, "A ROS-based Control Application for a Robotic Platform Using the Gazebo 3D Simulator," 2020 21th International Carpathian Control Conference (ICCC), 2020, pp. 1-5, https://doi.org/10.1109/ICCC49264.2020.9257256
  15. S. Gobhinath, K. Anandapoorani, K. Anitha, D. D. Sri and R. DivyaDharshini, "Simultaneous Localization and Mapping [SLAM] of Robotic Operating System for Mobile Robots," 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp. 577-580, https://doi.org/10.1109/ICACCS51430.2021.9441758
  16. J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788, https://doi.org/10.1109/CVPR.2016.91
  17. Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2022). A Review of Yolo Algorithm Developments. Procedia Computer Science, 199, 1066–1073. https://doi.org/10.1016/j.procs.2022.01.135
  18. X. Zhang, L. Zhang and D. Li, "Transmission Line Abnormal Target Detection Based on Machine Learning Yolo V3," 2019 International Conference on Advanced Mechatronic Systems (ICAMechS), 2019, pp. 344-348, https://doi.org/10.1109/ICAMechS.2019.8861617
  19. Bhuiyan MR, Abdullah J, Hashim N, Al Farid F, Ahsanul Haque M, Uddin J, Mohd Isa WN, Husen MN, Abdullah N. 2022. A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network. PeerJ Computer Science 8:e895 https://doi.org/10.7717/peerj-cs.895
  20. Li, L., Jia, Z., Cheng, T., & Jia, X. (2011). Optimal model predictive control for path tracking of autonomous vehicle. Proceedings - 3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011, 2, 791–794. https://doi.org/10.1109/ICMTMA.2011.481
  21. Tavernini, D., Metzler, M., Gruber, P., & Sorniotti, A. (2019). Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control. IEEE Transactions on Control Systems Technology, 27(4), 1438–1451. https://doi.org/10.1109/TCST.2018.2837097
  22. Maddalena, E. T., da Moraes, C. G. S., Waltrich, G., & Jones, C. N. (2020). A neural network architecture to learn explicit MPC controllers from data. IFAC-PapersOnLine, 53(2), 11362–11367. https://doi.org/10.1016/j.ifacol.2020.12.546
  23. Farag, K. K. A., Shehata, H. H., & El-Batsh, H. M. (2021). Mobile robot obstacle avoidance based on neural network with a standardization technique. Journal of Robotics, 2021. https://doi.org/10.1155/2021/1129872
  24. Duan, L., Ren, Y., & Duan, F. (2022). Adaptive stochastic resonance based convolutional neural network for image classification. Chaos, Solitons & Fractals, 162, 112429. https://doi.org/10.1016/j.chaos.2022.112429
  25. M. Ahmadi, Z. Xu, X. Wang, L. Wang, M. Shao and Y. Yu, "Fast Multi Object Detection and Counting by YOLO V3," 2021 China Automation Congress (CAC), 2021, pp. 7401-7404, https://doi.org/10.1109/CAC53003.2021.9727949
  26. G. Oltean, C. Florea, R. Orghidan and V. Oltean, "Towards Real Time Vehicle Counting using YOLO-Tiny and Fast Motion Estimation," 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME), 2019, pp. 240-243, https://doi.org/10.1109/SIITME47687.2019.8990708
  27. Medina-Santiago, A., Camas-Anzueto, J. L., Vazquez-Feijoo, J. A., Hernández-De León, H. R., & Mota-Grajales, R. (2014). Neural Control System in Obstacle Avoidance in Mobile Robots Using Ultrasonic Sensors (Vol. 12). https://doi.org/10.1016/S1665-6423(14)71610-4
  28. Jannah, S. W., & Santoso, A. (2022). Nonlinear Model Predictive Control for Longitudinal and Lateral Dynamic of Autonomous Car. 2022 11th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), 145–148. https://doi.org/10.1109/EECCIS54468.2022.9902927
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 11 Download : 12

Downloads

Download data is not yet available.