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Convolutional Neural Network, Maze, Classification, Navigation, Robot
Traditionally, the maze solving robots employ ultrasonic sensors to detect the maze walls around the robot. The robot is able to transverse along the maze omnidirectionally measured depth. 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, consisting of four convolution layers, three pooling layers, and three fully-connected layers, 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 show 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.
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.
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.
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.
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.
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.
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.
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.
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.
Almabdy, Soad, and Lamiaa Elrefaei. "Deep Convolutional Neural Network-Based Approaches for Face Recognition," Applied Sciences, vol. 9, no. 20, pp. 1-21, 2019.
Permana, D. Ajie. “Pendeteksi Wajah Bermasker Menggunakan Metode Faster R-CNN,” Dissertation Universitas Komputer Indonesia, 2021.
Li, Yang, et al. "Face Recognition Based on Recurrent Regression Neural Network." Neurocomputing, vol. 297, pp. 50-58, 2018.
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.
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.
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.
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.
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
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.
A. A. Süzen, B. Duman, and B. Şen, “Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN”, International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, 2020, doi: 10.1109/HORA49412.2020.9152915
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.
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.
S. Salman and X. Liu, “Overfitting Mechanism and Avoidance In Deep Neural Networks,” arXiv preprint 2019, arXiv: 1901.06566.
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.
X. Ying, “An Overview of Overfitting and its Solutions,” Journal of Physics: Conference Series, vol. 1168, no. 2, 2022.
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
R. Wassem and W. Zenghui, “Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review,” Neural Computation, vol. 29, no. 9, 2017.
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
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.