Single Spike Neural Network Model for Superficial Environment Classification for Mobile Robot Navigation

Main Article Content

Zhraa Issam Ibrahim
Nadia Adnan Shiltagh Al-Jamali

Abstract

In this work, a single Semi-Recurrent Spike Neural Network (SRSNN) supervised learning method based on time coding is proposed to classify visual terrain encountered by the mobile robot. To this end, the features are extracted using the Local Binary Pattern (LBP) method. Then, the SRSNN is trained to classify multi-class of terrains.  This training is used to classify six classes of terrain: hydrop, gravel, asphalt, grass, mud, and sand. The proposed training algorithm is based on adaptive synaptic weights that reach the threshold value. The feature extracted method and SRSNN form the proposed Intelligent structure. This structure effectively evaluates the accuracy, precision, recall, and F1 score. The simulation results achieve good performance in minimizing the mean square error in the training phase and maximizing the overall accuracy to 87.22%, especially in dangerous terrain (i.e. hydrop). The effectiveness of the proposed model is proved by the efficiency of the training algorithm which can learn fast with accurate results.

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Articles

How to Cite

“Single Spike Neural Network Model for Superficial Environment Classification for Mobile Robot Navigation” (2024) Journal of Engineering, 30(04), pp. 118–133. doi:10.31026/j.eng.2024.04.08.

References

Al-Araji, A.S., Ahmed, A.K., and Dagher, K.E., 2019. A cognition path planning with a nonlinear controller design for wheeled mobile robot based on an intelligent algorithm. Journal of Engineering, 25(1), pp. 64-83. Doi:10.31026/j.eng.2019.01.06

Al-Araji, A.S., and Ibraheem, B.A., 2019. A comparative study of various intelligent optimization algorithms based on path planning and neural controller for mobile robot. Journal of Engineering, 25(8), pp. 80-99. Doi:10.31026/j.eng.2019.08.06

Atiyah, H.A., and Hassan, M.Y., 2023. Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm. Journal Européen des Systèmes Automatisés, 56(1), P. 1. Doi:10.18280/jesa.560101

Jawad, M.M., and Hadi, E.A., 2019. A Comparative study of various intelligent algorithms based path planning for Mobile Robots. Journal of Engineering, 25(6), pp. 83-100. Doi:10.31026/j.eng.2019.06.07

DuPont, E.M., Moore, C.A., and Roberts, R.G., 2008. Terrain classification for mobile robots traveling at various speeds: An eigenspace manifold approach. IEEE International Conference on Robotics and Automation (pp. 3284-3289). Doi:10.1109/ROBOT.2008.4543711

Yu, Z., Sadati, S.H., Wegiriya, H., Childs, P., and Nanayakkara, T., 2021. A method to use nonlinear dynamics in a whisker sensor for terrain identification by mobile robots. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8437-8443. IEEE. Doi:10.1109/IROS51168.2021.9636571

Zou, X., Hwu, T., Krichmar, J., and Neftci, E., 2020. Terrain classification with a reservoir-based network of spiking neurons. IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). Doi:10.1109/ISCAS45731.2020.9180740

Wang, W., Zhang, B., Wu, K., Chepinskiy, S.A., Zhilenkov, A.A., Chernyi, S., and Krasnov, A.Y., 2022. A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine. Transactions of the Institute of Measurement and Control, 44(4), pp. 744-753. Doi:10.1177/0142331220987917

Hanson, N., Shaham, M., Erdoğmuş, D., and Padir, T., 2022. Vast: Visual and spectral terrain classification in unstructured multi-class environments. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3956-3963. Doi:10.1109/IROS47612.2022.9982078

Kozlowski, P., and Walas, K., 2018. Deep neural networks for terrain recognition task. Baltic URSI Symposium (URSI), pp. 283-286. Doi:10.23919/URSI.2018.8406736

Zhang, H., Dai, X., Sun, F., and Yuan, J., 2016. Terrain classification in field environment based on Random Forest for the mobile robot. 35th Chinese Control Conference (CCC), pp. 6074-6079. Doi:10.1109/ChiCC.2016.7554310

Zürn, J., Burgard, W., and Valada, A., 2020. Self-supervised visual terrain classification from unsupervised acoustic feature learning. IEEE Transactions on Robotics, 37(2), pp. 466-481. Doi:10.1109/TRO.2020.3031214

Wu, H., Zhang, W., Li, B., Sun, Y., Duan, D., and Chen, P., 2019. Visual terrain classification methods for mobile robots using hybrid coding architecture. IEEE 4th International Conference on Image, Vision and Computing (ICIVC), pp. 17-22. Doi:10.1109/ICIVC47709.2019.8981092

Papadakis, P., 2013. Terrain traversability analysis methods for unmanned ground vehicles: A survey. Engineering Applications of Artificial Intelligence, 26(4), pp. 1373-1385. Doi:10.1016/j.engappai.2013.01.006

Shiltagh, N.A., and Abas, H.A., 2015. Spiking neural network in precision griculture. Journal of Engineering, 21(7), pp. 17-34. Doi:10.31026/j.eng.2015.07.02

Miao, Y., Tang, H., and Pan, G., 2018. A supervised multi-spike learning algorithm for spiking neural networks. International Joint Conference on Neural Networks (IJCNN), pp. 1-7. Doi:10.1109/IJCNN.2018.8489175

Wu, D., Lin, X., and Du, P., 2019. An adaptive structure learning algorithm for multi-layer spiking neural networks. 15th International Conference on Computational Intelligence and Security (CIS), pp. 98-102. Doi:10.1109/CIS.2019.00029

Lee, C., Srinivasan, G., Panda, P., and Roy, K., 2018. Deep spiking convolutional neural network trained with unsupervised spike-timing-dependent plasticity. IEEE Transactions on Cognitive and Developmental Systems, 11(3), pp. 384-394. Doi:10.1109/TCDS.2018.2833071

Chen, Y., Rastogi, C., and Norris, W.R., 2021. A CNN based vision-proprioception fusion method for robust ugv terrain classification. IEEE Robotics and Automation Letters, 6(4), pp. 7965-7972. Doi:10.1109/LRA.2021.3101866

Vulpi, F., Milella, A., Marani, R., and Reina, G., 2021. Recurrent and convolutional neural networks for deep terrain classification by autonomous robots. Journal of Terramechanics, 96, pp. 119-131. Doi:10.1016/j.jterra.2020.12.002

Schilling, F., Chen, X., Folkesson, J., and Jensfelt, P., 2017. Geometric and visual terrain classification for autonomous mobile navigation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2678-2684. Doi:10.1109/IROS.2017.8206092

Karis, M.S., Razif, N.R.A., Ali, N.M., Rosli, M.A., Aras, M.S.M., and Ghazaly, M.M., 2016. Local Binary Pattern (LBP) with application to variant object detection: A survey and method. IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 221-226. Doi:10.1109/CSPA.2016.7515835

Singh, C., Walia, E., and Kaur, K.P., 2018. Color texture description with novel local binary patterns for effective image retrieval. Pattern Recognition, 76, pp. 50-68. Doi:10.1016/j.patcog.2017.10.021

Humeau-Heurtier, A., 2019. Texture feature extraction methods: A survey. IEEE access, 7, pp. 8975-9000. Doi:10.1109/ACCESS.2018.2890743

Kim, J., Kim, H., Huh, S., Lee, J., and Choi, K., 2018. Deep neural networks with weighted spikes. Neurocomputing, 311, pp. 373-386. Doi:10.1016/j.neucom.2018.05.087

Hu, Y., Tang, H., and Pan, G., 2021. Spiking deep residual networks. IEEE Transactions on Neural Networks and Learning Systems. Doi:10.1109/TNNLS.2021.3119238

Indiveri, G., Linares-Barranco, B., Hamilton, T.J., Schaik, A.V., Etienne-Cummings, R., Delbruck, T., Liu, S.C., Dudek, P., Häfliger, P., Renaud, S., and Schemmel, J., 2011. Neuromorphic silicon neuron circuits. Frontiers in neuroscience, 5, p.73. Doi:10.3389/fnins.2011.00073

Lin, X., Zhang, M., and Wang, X., 2021. Supervised learning algorithm for multilayer spiking neural networks with long-term memory spike response model. Computational Intelligence and Neuroscience, 2021. Doi:10.1155/2021/8592824

Al-Yassari, M.M.R., and Al-Jamali, N.A.S., 2023. Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment. Journal of Engineering, 29(6), pp. 87-97. Doi:10.31026/j.eng.2023.06.07

Yellakuor, B.E., Moses, A.A., Zhen, Q., Olaosebikan, O.E., and Qin, Z., 2020. A multi-spiking neural network learning model for data classification. IEEE Access, 8, pp. 72360-72371. Doi:10.1109/ACCESS.2020.2985257

Oniz, Y., Kaynak, O., and Abiyev, R., 2013. Spiking neural networks for the control of a servo system. IEEE International Conference on Mechatronics (ICM), pp. 94-98. Doi:10.1109/ICMECH.2013.6518517

Thiruvarudchelvan, V., Crane, J.W., and Bossomaier, T., 2013. Analysis of SpikeProp convergence with alternative spike response functions. IEEE Symposium on Foundations of Computational Intelligence (FOCI), pp. 98-105. Doi:10.1109/FOCI.2013.6602461

Nasser, F.K., and Behadili, S.F., 2022. Breast Cancer detection using decision tree and K-Nearest neighbour classifiers. Iraqi Journal of Science, pp. 4987-5003. Doi:10.24996/ijs.2022.63.11.34

Abdulrezzak, S., and Sabir, F., 2023. An empirical investigation on Snort NIDS versus supervised machine learning classifiers. Journal of Engineering, 29(2), pp. 164-178. Doi:10.31026/j.eng.2023.02.11

Soud, N.S., and Al-Jamali, N.A.S., 2023. Intelligent congestion control of 5G traffic in SDN using dual-spike neural network. Journal of Engineering, 29(1), pp. 110-127. Doi:10.31026/j.eng.2023.01.07

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