Spatial Based Deep Learning Autonomous Wheel Robot Using CNN
The development of technology is growing rapidly; one of the most popular among the scientist is robotics technology. Recently, the robot was created to resemble the function of the human brain. Robots can make decisions without being helped by humans, known as AI (Artificial Intelligent). Now, this technology is being developed so that it can be used in wheeled vehicles, where these vehicles can run without any obstacles. Furthermore, of research, Nvidia introduced an autonomous vehicle named Nvidia Dave-2, which became popular. It showed an accuracy rate of 90%. The CNN (Convolutional Neural Network) method is used in the track recognition process with input in the form of a trajectory that has been taken from several angles. The data is trained using Jupiter's notebook, and then the training results can be used to automate the movement of the robot on the track where the data has been retrieved. The results obtained are then used by the robot to determine the path it will take. Many images that are taken as data, precise the results will be, but the time to train the image data will also be longer. From the data that has been obtained, the highest train loss on the first epoch is 1.829455, and the highest test loss on the third epoch is 30.90127. This indicates better steering control, which means better stability.
 S. . Chang et al., "Resonant scattering of energetic electrons in the plasmasphere by monotonic whistler-mode waves artificially generated by ionospheric modification," Annales Geophysicae, vol. 32, pp. 507–518, 2014.
 M. G. Bechtel, E. McEllhiney, M. Kim, and H. Yun, "DeepPicar: A low-cost deep neural network-based autonomous car," Proc. - 2018 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications(RTCSA 2018), pp. 11–21, 2019, doi: 10.1109/RTCSA.2018.00011.
 C. L. Zhang and J. Wu, "Improving CNN linear layers with power mean non-linearity," Pattern Recognition, vol. 89, pp. 12–21, 2019, doi: 10.1016/j.patcog.2018.12.029.
 D. A. Prasetya and I. Mujahidin, "2.4 GHz Double Loop Antenna with Hybrid Branch-Line 90-Degree Coupler for Widespread Wireless Sensor," in 2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Aug. 2020, pp. 298–302, doi: 10.1109/EECCIS49483.2020.9263477.
 J. Sun, Y. Fu, S. Li, J. He, C. Xu, and L. Tan, "Sequential human activity recognition based on deep convolutional network and extreme learning machine using wearable sensors," Journal of Sensors, vol. 2018, no. 1, 2018, doi: 10.1155/2018/8580959.
 W. Dharmawan and H. Nambo, "End-to-End Xception Model Implementation on Carla Self Driving Car in Moderate Dense Environment," AICCC 2019: Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference, pp. 139–143, 2019, doi: 10.1145/3375959.3375969.
 Z. Q. Zhao, P. Zheng, S. T. Xu, and X. Wu, "Object Detection with Deep Learning: A Review," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212–3232, 2019, doi: 10.1109/TNNLS.2018.2876865.
 A. R. Pathak, M. Pandey, and S. Rautaray, "Application of Deep Learning for Object Detection," Procedia Computer Science, vol. 132, no. Iccids, pp. 1706–1717, 2018, doi: 10.1016/j.procs.2018.05.144.
 N. Akhtar and A. Mian, "Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey," IEEE Access, vol. 6, pp. 14410–14430, 2018, doi: 10.1109/ACCESS.2018.2807385.
 N. F. Ardiansyah, A. Rabi’, D. Minggu, and W. Dirgantara, “Computer Vision Untuk Pengenalan Obyek Pada Peluncuran Roket Kendaraan Tempur,” JASIEK (Jurnal Apl. Sains, Informasi, Elektron. dan Komputer), vol. 1, no. 1, 2019, doi: 10.26905/jasiek.v1i1.3142.
 H. Yu, D. C. Samuels, Y. yong Zhao, and Y. Guo, "Architectures and accuracy of artificial neural network for disease classification from omics data," BMC Genomics, vol. 20, no. 1, pp. 1–12, 2019, doi: 10.1186/s12864-019-5546-z.
 A. A. Elsharif, I. M. Dheir, A. Soliman, A. Mettleq, and S. S. Abu-naser, "Potato Classification Using Deep Learning," Advances in Animal Biosciences, vol. 3, no. 12, pp. 1–8, 2019.
 A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, "CARLA: An Open Urban Driving Simulator," 1st Conference on Robot Learning (CoRL 2017), no. CoRL, pp. 1–16, 2017, [Online]. Available: http://arxiv.org/abs/1711.03938.
 W. Xiang, D. M. Lopez, P. Musau, and T. T. Johnson, "Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems," Safe, Autonomous and Intelligent Vehicles, pp. 123–144, 2019, doi: 10.1007/978-3-319-97301-2_7.
 A. A. Heidari, H. Faris, I. Aljarah, and S. Mirjalili, "An efficient hybrid multilayer perceptron neural network with grasshopper optimization," Soft Computing, vol. 23, no. 17, pp. 7941–7958, 2019, doi: 10.1007/s00500-018-3424-2.
 W. A. H. M. Ghanem, A. Jantan, S. A. A. Ghaleb, and A. B. Nasser, "An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons," IEEE Access, vol. 8, pp. 130452–130475, 2020, doi: 10.1109/access.2020.3009533.
 J. Heaton, "Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning," Genetic Programming and Evolvable Machines, vol. 19, no. 1–2, pp. 305–307, 2018, doi: 10.1007/s10710-017-9314-z.
 W. Dharmawan, "End-to-End Sequential Input with Time Distributed Model for Carla Self Driving Car in Moderate Dense Environment," 2019.
 C. Zhao, B. Ni, J. Zhang, Q. Zhao, W. Zhang, and Q. Tian, "Variational convolutional neural network pruning," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2019-June, pp. 2775–2784, 2019, doi: 10.1109/CVPR.2019.00289.
 Y. Liu, B. Fan, S. Xiang, and C. Pan, "Relation-shape convolutional neural network for point cloud analysis," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2019-June, pp. 8887–8896, 2019, doi: 10.1109/CVPR.2019.00910.
 A. Amidi, S. Amidi, D. Vlachakis, V. Megalooikonomou, N. Paragios, and E. I. Zacharaki, "EnzyNet : enzyme classification using 3D convolutional neural networks on spatial representation," Bioinformatics and Genomics, pp. 1–11, 2017.
 D. A. Prasetya, T. Yasuno, H. Suzuki, and A. Kuwahara, "Cooperative Control System of Multiple Mobile Robots Using Particle Swarm Optimization with Obstacle Avoidance for Tracking Target," Journal of Signal Processing, vol. 17, no. 5, pp. 199–206, 2013.
 A. P. Sari, H. Suzuki, T. Kitajima, T. Yasuno, and D. A. Prasetya, "Prediction Model of Wind Speed and Direction Using Deep Neural Network," JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science), vol. 3, no. 1, pp. 1–10, 2020, doi: 10.26905/jeemecs.v3i1.3946.
 A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox, "Discriminative unsupervised feature learning with convolutional neural networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. January, pp. 766–774, 2014.
 S. Wang, J. Sun, I. Mehmood, C. Pan, Y. Chen, and Y. D. Zhang, "Cerebral micro-bleeding identification based on a nine-layer convolutional neural network with stochastic pooling," Concurrency and Computation Practice and Experience, vol. 32, no. 1, pp. 1–16, 2020, doi: 10.1002/cpe.5130.
 A. F. Agarap, "Deep Learning using Rectified Linear Units (ReLU)," Neural and Evolutionary Computing, no. 1, pp. 2–8, 2018.
 L. Jing, M. Zhao, P. Li, and X. Xu, "A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox," Measurement. Journal of the International Measurement Confederation (IMEKO), vol. 111, pp. 1–10, 2017, doi: 10.1016/j.measurement.2017.07.017.
 M. Yu et al., "Gradiveq: Vector quantization for bandwidth-efficient gradient aggregation in distributed CNN training," Advances In Neural Information Processing Systems 31 (NIPS 2018), vol. 2018-Decem, no. NeurIPS, pp. 5123–5133, 2018.
 S. Chen, C. Zhang, M. Dong, J. Le, and M. Rao, “Chen_Using_Ranking-CNN_for_CVPR_2017_paper.pdf,” Cvpr, pp. 5183–5192, 2017.
 C. Science, "End-to-End Spatial Based Deep Neural Network on Self-Driving Car," 2020.
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