APPLICATIONS OF NEUROCOMPUTING IN AUTONOMOUS SYSTEMS AND ROBOTICS
Keywords:
Neurocomputing, Autonomous Robotics, Deep Learning, Reinforcement Learning, Neuromorphic Computing, Spiking Neural Network, Autonomous Systems, Real-Time Processing, Autonomous Vehicles, Robotic Control SystemAbstract
This paper explores the critical role of neurocomputing in advancing autonomous systems and robotics, focusing on its applications in perception, control, and real-time processing. Through the integration of deep learning models, reinforcement learning, and neuromorphic hardware, neurocomputing enhances the ability of autonomous robots to navigate complex environments, process sensory data, and make informed decisions in real-time. Case studies on autonomous vehicle control systems and other robotic applications highlight the effectiveness of these techniques in real-world scenarios. The paper also discusses the benefits of neuromorphic computing for robotics, the implementation of spiking neural networks, and the challenges associated with deploying neurocomputing technologies in practical settings. Despite these challenges, the advancements in neurocomputing continue to push the boundaries of what autonomous systems can achieve, offering new possibilities for innovation across various industries.
References
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
Schuman, C. D., Potok, T. E., Patton, R. M., Birdwell, J. D., Dean, M. E., Rose, G. S., & Plank, J. S. (2017). A Survey of Neuromorphic Computing and Neural Networks in Hardware. arXiv preprint arXiv:1705.06963.
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354-359.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778).
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.
Pezzulo, G., Rigoli, F., & Chersi, F. (2013). The mixed instrumental controller: Using value of information to combine habitual choice and mental simulation. Frontiers in Psychology, 4, 92.
Benjamin, B. V., Gao, P., McQuinn, E., Choudhary, S., Chandrasekaran, A. R., Bussat, J. M., ... & Boahen, K. (2014). Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations. Proceedings of the IEEE, 102(5), 699-716.
Zador, A. M. (2019). A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications, 10(1), 1-7
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Copyright (c) 2024 S. B. Vinay (Author)
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