Abstract
In order to achieve a safe and traffic free transportation from place to place it is mandatory that a vehicle should communicate with other vehicles autonomously to control their speed and movement. Fuzzy logic control architecture which is placed in the vehicle has been designed to take appropriate decision based on the various parameters inside the vehicle. Number of input and output parameters and rules of operation can be varied easily in the fuzzy logic toolbox in MATLAB. The output will be sent to the micro-controller using RS232 serial communication. Now the micro-controller will take the appropriate action to execute the command received. So in order to communicate with other vehicle we have used GPS receiver which will transmit the current position of the vehicle to nearby vehicle ranging within 30 or 100 m using Zigbee communication in a common transmission frequency. If there is any vehicles present in this range it will accept and communicate back by sending the corresponding GPS location of that vehicle and the path of its course. If it is found to be moving on the same direction or path a communication with the vehicle will be established and data will be transmitted among them to achieve the safe and smooth traffic travel to increase the vehicle efficiency. The same rules will apply if there is more than one vehicle is present in the transmitting range. All these transmitting data will be privacy protected. We have successfully controlled prototype model and found that architecture is working autonomously up to the expectation and provides efficient travel for the vehicle.
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
Shair, S., Chandler, J.H., Gonzalez-Villela, V.J., et al.: The use of aerial images and GPS for mobile robot waypoint navigation. IEEE ASME Trans. Mechatron. 13(6), 692–699 (2008)
Vengerov, D., Bambos, N., Berenji, H.: A fuzzy reinforcement learning approach to power control in wireless transmitters. IEEE Trans. Syst. Man Cybern. B Cybern. 35(4), 768–778 (2005)
Sutton, R., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–287 (1996)
Watkins, C., Dayan, P.: Q-learning. Mach. Learn. 8(3/4), 279–292 (1992)
Dong, D., Chen, C., Chen, Z.: Quantum reinforcement learning. In: Proceedings of First International Conference on Natural Computation, Lecture Notes in Computer Science, vol. 3611, pp. 686–689 (2005)
Dong, D., Chen, C., Li, H., Tarn, T.J.: Quantum reinforcement learning. IEEE Trans. Syst. Man Cybern. B Cybern. 38(5), 1207–1220 (2008)
Chen, C., Dong, D., Chen, Z.: Quantum computation for action selection using reinforcement learning. Int. J. Quantum Inf. 4(6), 1071–1083 (2006)
Dong, D., Chen, C.: Quantum-inspired reinforcement learning for decision-making of Markovian state transition. Presented at the 2010 International Conference on Intelligent Systems and Knowledge Engineering, Hangzhou, China, 15–16 Nov 2010
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Mohan, A., Jayabalan, S., Mohan, A. (2017). Autonomous Quantum Reinforcement Learning for Robot Navigation. In: Deiva Sundari, P., Dash, S., Das, S., Panigrahi, B. (eds) Proceedings of 2nd International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-10-1645-5_29
Download citation
DOI: https://doi.org/10.1007/978-981-10-1645-5_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1644-8
Online ISBN: 978-981-10-1645-5
eBook Packages: EngineeringEngineering (R0)