Abstract
In this paper, we introduce an approach for friction recognition and state inference under environment where field friction changes. We use a robot soccer-based challenge environment, AI Soccer, where two teams of two-wheeled agents compete to win a game of soccer. We modify the AI Soccer simulator to make the friction coefficient between the agent wheels and the field intermittently changes to add more complexity on the challenge. The friction change causes the AI Soccer’s agents to run in a slippery environment time to time that the agents need to adapt their controls in order to play the game properly. As the agents are not equipped with sensors usable for friction detection, we develop a friction recognition classifier with multilayer perceptron based on agents’ coordinates and their wheel speed signals. However, the classification of the environmental friction would not be sufficient state information for the agents to perform well under the dynamic environment as behaviors of the ball or opponent agents would be affected by the friction as well. Detailed context information based on friction can help AI Soccer players make better decisions. Therefore, we in addition build a clustering module upon the trained multilayer perceptron classifier. clustering resonance network is adopted as the clustering module for the network’s incremental and unsupervised learning capability. Through experiments we demonstrate our system can detect friction state of the AI Soccer field and infer further based on the learned features.
C. Hong, G.-M. Lee and J.-W. Choi—These authors contributed equally to this work.
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References
Du, Y., Liu, C., Song, Y., Li, Y., Shen, Y.: Rapid estimation of road friction for anti-skid autonomous driving. IEEE Trans. Intell. Transp. Syst. 21(6), 2461–2470 (2019)
Rajamani, R., Phanomchoeng, G., Piyabongkarn, D., Lew, J.Y.: Algorithms for real-time estimation of individual wheel tire-road friction coefficients. IEEE/ASME Trans. Mechatron. 17(6), 1183–1195 (2011)
Alvarez, L., Yi, J., Horowitz, R., Olmos, L.: Dynamic friction model-based tire-road friction estimation and emergency braking control. J. Dyn. Syst. Measure. Control 127(1), 22–32 (2005)
Hahn, J.-O., Rajamani, R., Alexander, L.: GPS-based real-time identification of tire-road friction coefficient. IEEE Trans. Control Syst. Technol. 10(3), 331–343 (2002)
Hong, C., Jeong, I., Vecchietti, L.F., Har, D., Kim, J.-H.: AI world cup: robot soccer-based competitions. IEEE Trans. Games 13(4), 330–341 (2021)
FIRA RoboworldCup official website. http://www.firaworldcup.org/. Accessed 31 Aug 2021
RoboCup Federation official website. https://www.robocup.org/. Accessed 31 Aug 2021
Michel, O.: Cyberbotics Ltd. Webots™: professional mobile robot simulation. Int. J. Adv. Robot. Syst. 1(1), 5 (2004)
Choi, J.-W., Park, G.-M., Kim, J.-H.: SR-EM: episodic memory aware of semantic relations based on hierarchical clustering resonance network. IEEE Trans. Cybern. 1–13 (2021)
Schütze, H., Manning, C.D., Raghavan, P.: Introduction to Information Retrieval. Cambridge University Press, Cambridge, vol. 39 (2008)
Tan, A.-H., Carpenter, G.A., Grossberg, S.: Intelligence through interaction: towards a unified theory for learning. In: Liu, D., Fei, S., Hou, Z.-G., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4491, pp. 1094–1103. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72383-7_128
Acknowledgements
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00440, Development of artificial intelligence technology that continuously improves itself as the situation changes in the real world).
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Hong, C., Lee, GM., Choi, JW., Kim, JH. (2022). Field Friction Recognition and State Inference in AI Soccer. In: Kim, J., et al. Robot Intelligence Technology and Applications 6. RiTA 2021. Lecture Notes in Networks and Systems, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-030-97672-9_37
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DOI: https://doi.org/10.1007/978-3-030-97672-9_37
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