Abstract
In a virtual environment, a robot can serve people by bringing things to them. However, when a robot moves within a house, it collides with a dynamic obstacle. These collisions make it difficult for a robot to complete its mission. We therefore apply reinforcement learning to the robot to make it more intelligent. Consequently, the robot can automatically move to avoid the dynamic obstacle in order to successfully complete its mission.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Zhang, Z., Zhao, Z.: A Multiple Mobile Robots Path planning Algorithm Based on A-star and Dijkstra Algorithm. International Journal of Smart Home (2014)
Zou, X., Ge, B., Sun, P.: Improved Genetic Algorithm for Dynamic Path Planning. International Journal of Information and Computer Science (2012)
Achour, N., Chaalal, M.: Mobile Robots Path Planning using Genetic Algorithms. In: ICAS 2011: The Seventh International Conference on Autonomic and Autonomous Systems (2011)
Sung, Y., Cho, S., Um, K., Jeong, Y., Fong, S., Cho, K.: Human-Robot Interaction Learning using Demonstration-based Learning and Q-learning in a Pervasive Sensing Environment. International Journal of Distributed Sensor Networks (2014)
Sung, Y., Ahn, E., Cho, K.: Q-learning Reward Propagation Method for Reducing the Transmission Power of Sensor Nodes in Wireless Sensor Networks. Wireless Personal Communications (2013)
Even-Dar, E., Mansour, Y.: Learning Rates for Q-learning. Journal of Machine Learning Research 5 (2003)
Smart, W.D., Kaelbling, L.P.: Practical reinforcement learning in continuous spaces. In: Proceedings of ICML 2000, pp. 903–910 (2000)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research 4 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chu, P., Vu, H., Yeo, D., Lee, B., Um, K., Cho, K. (2015). Robot Reinforcement Learning for Automatically Avoiding a Dynamic Obstacle in a Virtual Environment. In: Park, J., Chao, HC., Arabnia, H., Yen, N. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47487-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-662-47487-7_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47486-0
Online ISBN: 978-3-662-47487-7
eBook Packages: EngineeringEngineering (R0)