skip to main content

An Embedded Computer Vision using Convolutional Neural Network for Maze Classification and Robot Navigation

Satya Wacana Christian University, Indonesia

Received: 12 May 2022; Published: 24 Sep 2024.
Open Access Copyright (c) 2024 Gunawan Dewantoro, Dinar Rahmat Hadiyanto, Andreas Ardian Febrianto
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Traditionally, the maze solving robots employ ultrasonic sensors to detect the maze walls around the robot. However, this approach only perceives the presence of the objects without recognizing the type of these objects. Therefore, computer vision has become more popular for classification purpose in robot applications. In this study, a maze solving robot is equipped with a camera to recognize the types of obstacles in a maze. The types of obstacles are classified as: intersection, dead end, T junction, finish zone, start zone, straight path, T–junction, left turn, and right turn. Convolutional neural network is employed to train the robot using a total of 24,000 images to recognize the obstacles. Jetson Nano development kit is used to implement the trained model and navigate the robot. The results shows an average training accuracy of 82% with a training time of 30 minutes 15 seconds. As for the testing, the lowest accuracy is 90% for the T-junction with the computational time being 500 milliseconds for each frame. Therefore, the convolutional neural network is adequate to serve as classifier and navigate a maze solving robot.
Keywords: convolutional neural network; maze; navigation; classification; robot

Article Metrics:

  1. M. Jogin, Mohana, M. S. Madhulika, G. D. Divya, R. K. Meghana and S. Apoorva, "Feature Extraction Using Convolution Neural Networks (CNN) and Deep Learning," in 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, 2018, pp. 2319-2323, doi: 10.1109/RTEICT42901.2018.9012507
  2. H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions On Medical Imaging, vol. 35, No.5, pp. 1-15, May 2016
  3. Z. Wang, H. Li, X. Zhang, “Construction Waste Recycling Robot For Nails And Screws: Computer Vision Technology And Neural Network Approach”, Automation in Construction, vol. 97, pp. 220-228, Hongkong, 2019, ISSN 0926-5805, https://doi.org/10.1016/j.autcon.2018.11.009
  4. D. A. Alghmgham, G. Latif, J. Alghazo, and L. Alzubaidi, “Autonomous Traffic Sign (ATSR) Detection and Recognition Using Deep CNN,” in Procedia Computer Science, vol. 163, pp. 266-274, 2019. ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.12.108
  5. B. Ko, H. J. Choi, C. Hong, J. H. Kim, O. C. Kwon, and C. D. Yoo, "Neural Network-Based Autonomous Navigation For A Homecare Mobile Robot,” in IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 403–406, Jeju, 2017, doi: 10.1109/BIGCOMP.2017.7881744
  6. Kocić, Jelena, N. Jovičić, and V. Drndarević. "An End-To-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms." Sensors, vol. 19, no. 9, 2019
  7. D. L. Z. Astuti and Samsuryadi “Kajian Pengenalan Ekspresi Wajah Menggunakan Metode PCA Dan CNN,” in Prosiding Annual Research, vol. 4, no. 1, pp. 293-297, 2018
  8. A. Chavda, J. Dsouza, S. Badgujar and A. Damani, "Multi-Stage CNN Architecture for Face Mask Detection," in 6th International Conference for Convergence in Technology (I2CT), Maharashtra, pp. 1-8, 2021. doi: 10.1109/I2CT51068.2021.9418207
  9. A. Ulhaq, J. Born, A. Khan, D. P. S. Gomes, S. Chakraborty and M. Paul, "COVID-19 Control by Computer Vision Approaches: A Survey," IEEE Access, vol. 8, pp. 179437-179456, 2020, doi: 10.1109/ACCESS.2020.3027685
  10. Almabdy, Soad, and Lamiaa Elrefaei. "Deep Convolutional Neural Network-Based Approaches for Face Recognition," Applied Sciences, vol. 9, no. 20, pp. 1-21, 2019
  11. Permana, D. Ajie. “Pendeteksi Wajah Bermasker Menggunakan Metode Faster R-CNN,” Dissertation Universitas Komputer Indonesia, 2021
  12. Li, Yang, et al. "Face Recognition Based on Recurrent Regression Neural Network." Neurocomputing, vol. 297, pp. 50-58, 2018
  13. A. Zarkasi, H. Ubaya, C. D. Amanda, and R. Firsandaya, “Implementation of RAM Based Neural Networks On Maze Mapping Algorithms for Wall Follower Robot,” Journal of Physics: Conference Series, vol. 1196, no. 1, pp. 1-6, 2019, doi: 10.1088/1742-6596/1196/1/012043
  14. A. Rodriguez-Tirado, D. Magallan-Ramirez, J. D. Martinez-Aguilar, C. Francisco Moreno-Garcia, D. Balderas and E. Lopez-Caudana, "A Pipeline Framework for Robot Maze Navigation Using Computer Vision, Path Planning and Communication Protocols," 2020 13th International Conference on Developments in eSystems Engineering (DeSE), pp. 152-157, 2020. doi: 10.1109/DeSE51703.2020.9450731
  15. Rostami, S. M. Hosseini, et al. "Obstacle Avoidance of Mobile Robots Using Modified Artificial Potential Field Algorithm," EURASIP Journal on Wireless Communications and Networking, vol. 70, pp. 1-19, 2019
  16. O. Khatib, "Real-Time Obstacle Avoidance for Manipulators and Mobile Robots," Proceedings. 1985 IEEE International Conference on Robotics and Automation, pp. 500-505, 1985, doi: 10.1109/ROBOT.1985.1087247
  17. S. Suryanarayana, V. Akhila, “Autonomous Maze Solving Robot Using Arduino”, International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 12, no. 3, pp. 595-603, 2021, doi: 10.3421/IJARET.12.3.2021.054
  18. A. Sabril and N. M. Abdal, “Perbandingan Waktu Tempuh Mobile Robot Dalam Arena Labirin Dengan Algoritma Tangan Kiri Dan Algoritma Tangan Kanan,” Jurnal Media Elektrik, vol. 17, no. 3, 2020. p-ISSN: 1907-1728, e-ISSN: 2721-9100
  19. Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998, doi: 10.1109/5.726791
  20. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., “Imagenet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015
  21. S. Salman and X. Liu, “Overfitting Mechanism and Avoidance In Deep Neural Networks,” arXiv preprint 2019, arXiv: 1901.06566
  22. Q. Xu, M. Zhang, Z. Gu, “Overfitting Remedy by Sparsifying Regularization on Fully-Connected Layers of CNNs,” Neurocomputing, vol. 328, pp. 69-74, 2019, doi: https://doi.org/10.1016/j.neucom.2018.03.080
  23. X. Ying, “An Overview of Overfitting and its Solutions,” Journal of Physics: Conference Series, vol. 1168, no. 2, 2022
  24. Z. Guoping, “On the confusion matrix in credit scoring and its analytical properties,” Communications in Statistics - Theory and Methods, vol 49, no. 9, 2020. https://doi.org/10.1080/03610926.2019.1568485
  25. R. Wassem and W. Zenghui, “Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review,” Neural Computation, vol. 29, no. 9, 2017
  26. S. Ahmad, S. U. Ansari, U. Haider, K. Javed, J. U. Rahman, and S. Anwar, “Confusion matrix-based modularity induction into pretrained CNN,” Multimedia Tools and Applications, vol. 81, pp. 23311 – 23337, 2022. https://doi.org/10.1007/s11042-022-12331-2
  27. S. Konduri, E. O. C. Torres, P. R. Pagilla, “Dynamics and Control of a Differential Drive Robot With Wheel Slip: Application to Coordination of Multiple Robots,” Journal of Dynamic Systems, Measurement, and Control, vol. 139, no. 1, 2017

Last update:

No citation recorded.

Last update: 2024-11-03 06:03:03

No citation recorded.