Abstract
The technology for autonomy in vehicles has a momentous advancement. Autonomous ground vehicles (AGV) for off-the-road applications will aid various sectors of the society such as mining, constructions, forest path maneuvering, and defense. This project demonstrates a working prototype of a 1/10th scale autonomous car that has been developed using a custom neural network model. The prototype uses Raspberry pi-4 as the core processor to compute real-time images collected from the camera as the key input. The results illustrate the optimized capability of path planning for the AGV using the custom convolutional neural network model with data augmentation. This paper summarizes the results derived and compares the accuracy of the steering in AGV which can be translated for off-road applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kelly, A., Stentz, A., Amidi, O., Bode, M., Bradley, D., Diaz-Calderon, A., Happold, M et al.: Toward reliable off road autonomous vehicles operating in challenging environments. Int. J. Rob. Res. 25. 5(6), 449–483 (2006)
Behringer, R., Sundar S., Brian G., Richard E., Bob A., Wayne G., Robert D., David B.: The DARPA grand challenge-development of an autonomous vehicle. In IEEE Intelligent Vehicles Symposium, 2004. IEEE, pp 226–231 (2004)
Jurdak, R., Elfes, A., Kusy, B., Tews, A., Wen, Hu., Hernandez, E., Kottege, N., Sikka, P.: Autonomous surveillance for biosecurity. Trends. Biotechnol. 33(4), 201–207 (2015)
Pomerleau, D.A.: Alvinn: An autonomous land vehicle in a neural network. In: Advances in neural information processing systems, pp 305–313 (1989)
Bojarski, M., Davide Del, T., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Lawrence, D., Jackel et al.: End to end learning for self-driving cars. arXiv preprint, arXiv:1604.07316 (2016)
Mishkin, D., Sergievskiy, N., Matas, J.: Systematic evaluation of convolution neural network advances on the imagenet. Comput. Vis. Image Underst. 161, 11–19 (2017)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315–323 (2011)
Himmelsbach, M., Mueller, A et al.: LIDAR-based 3D object perception. In: Proceedings of 1st international workshop on cognition for technical systems, vol. 1 (2008)
O'Kelly, M., Sukhil, V., Abbas, H., Harkins, J., Kao, C., Pant, Y.V., Mangharam, R et al.: F1/10: an open-source autonomous cyber-physical platform. arXiv preprint arXiv:1901.08567 (2019)
Seth, A., James, A., Mukhopadhyay, S.C.: 1/10th scale autonomous vehicle based on convolutional neural network. Int. J. Smart Sens. Intell. Syst. 13(1), 1–17 (2020)
Roscoe, W.: Donkey Car: an opensource DIY self driving platform for small scale cars. (2019)
Bechtel, M.G., McEllhiney, E., Kim, M., Yun, H.: Deeppicar: A low-cost deep neural network-based autonomous car. In: 2018 IEEE 24th International conference on embedded and real-time computing systems and applications (RTCSA). IEEE, pp 11–21 (2018)
Sheppard, E.A., Mukhopadhyay, S.C., Sen Gupta, G.: Remote controlled solid state fireworks detonation system. In: Proceedings of the fourteenth electronics New Zealand conference ENZCon 2007, Wellington, New Zealand, ISBN 978-0-473-12951-4, pp. 95–99, 12–13 Nov 2007
Chandni, C.K., Sajith Variyar, VV., Guruvayurappan, K.: Vision based closed loop pid controller design and implementation for autonomous car. In: 2017 International conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 1928–1933 (2017)
Dunbabin, M., Tews, A.: Toward robotic visual and acoustic stealth for outdoor dynamic target tracking. Proceeding of ARAA ACRA (2012)
Muller, U., Ben, J., Cosatto, E., Flepp, B., Cun, Y.L.: Off-road obstacle avoidance through end-to-end learning. In: Advances in neural information pro-cessing systems, pp 739–746 (2006)
Lake, B.J., Mukhopadhyay, S.C., Sen Gupta, G.: Electronics and magnetics for a smart battery charger for soccer robots. In: Proceedings of the fourteenth electronics New Zealand conference ENZCon 2007, Wellington, New Zealand, ISBN 978-0-473-12951-4, pp 47–52, 12–13 Nov 2007
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks mark. Commun. ACM 60(6), 84–90 (2017)
Liu, D., Zhao, J., Xi, A., Wang, C., Huang, X., Lai, K., Liu, C.: Data augmentation technology driven by image style transfer in self-driving car based on end-to-end learning. Comput. Model. Eng. Sci. (2020)
Seelye, M., Gupta, G.S., Seelye, J., Mukhopadhyay, SC.: Camera-in-hand Robotic System for Remote Monitoring of Plant Growth in a Laboratory. In: Proceedings of 2010 IEEE I2MTC conference, Austin, Texas, USA, pp 809–814, 4–6May 2010
Acknowledgements
We are grateful for the facilities and laboratories provided by Macquarie University, Sydney. We acknowledge and sincerely show our gratitude to our Supervisor Dr. Subhas Chandra Mukhopadhyay for his guidance and profound perspective on this project. We would also like to acknowledge our parents Mr. James Joseph and Mrs. Gladys James and Late Mr. Nitin Seth and Mrs. Zenobia Seth for their constant motivation and inspiration.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
James, A., Seth, A., Mukhopadhyay, S.C. (2022). Autonomous Ground Vehicle for Off-the-Road Applications Based on Neural Network. In: Mandal, J.K., Roy, J.K. (eds) Proceedings of International Conference on Computational Intelligence and Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-3368-3_27
Download citation
DOI: https://doi.org/10.1007/978-981-16-3368-3_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3367-6
Online ISBN: 978-981-16-3368-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)