Abstract
This paper presents the results of the experiments of a multi-agent control architecture for the efficient control of a multi-wheeled mobile platform. Multi-agent system incorporates multiple Q-learning agents, which permits them to effectively control every wheel relative to other wheels. The learning process was divided into two steps: module positioning – where the agents learn to minimize the error of orientation and cooperative movement – where the agents learn to adjust the desired velocity in order to conform to the desired position in formation. From this decomposition every module agent will have two control policies for forward and angular velocity, respectively. The experiments were carried out with a real robot. Our results indicate the successful application of the proposed control architecture for the real production robot.
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References
Andreas, J.C.: Energy-Efficient Electric Motors, 2nd edn. Marcel Dekker, New York (1992)
de Almeida, A.T., Bertoldi, P., Leonhard, W.: Energy efficiency improvements in electric motors and drives. Springer, Berlin (1997)
Stetter, R., Ziemniak, P., Paczynski, A.: Development, Realization and Control of a Mobile Robot. In: Obdržálek, D., Gottscheber, A. (eds.) EUROBOT 2010. CCIS, vol. 156, pp. 130–140. Springer, Heidelberg (2011)
Dziomin, U., Kabysh, A., Golovko, V., Stetter, R.: A multi-agent reinforcement learning approach for the efficient control of mobile robot. In: IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems, Berlin, vol. 2, pp. 867–873 (2013)
Mei, Y., Lu, Y.-H., Hu, Y.C., Lee, C.G.: Energy-efficient motion planning for mobile robots. In: 2004 IEEE International Conference on Robotics and Automation: Proceedings, ICRA 2004, vol. 5, pp. 4344–4349 (2004)
Ogunniyi, S., Tsoeu, M.S.: Q-learning based energy efficient path planning using weights. In: 24th Symposium of the Pattern Recognition Association of South Africa, pp. 76–82 (2013)
Mei, Y., Lu, Y.-H., Lee, C.G., Hu, Y.C.: Energy-efficient mobile robot exploration. In: IEEE International Conference, Robotics and Automation, pp. 505–511. IEEE Press (2006)
Ceccarelli, N., Di Marco, M., Garulli, A., Giannitrapani, A.: Collective circular motion of multi-vehicle systems with sensory limitations. In: 44th IEEE Conference, Decision and Control, 2005 and 2005 European Control Conference, pp. 740–745 (2005)
Ceccarelli, N., Di Marco, M., Garulli, A., Giannitrapani, A.: Collective circular motion of multi-vehicle systems. Automatica 44(12), 3025–3035 (2008)
Benedettelli, D., Ceccarelli, N., Garulli, A., Giannitrapani, A.: Experimental validation of collective circular motion for nonholonomic multi-vehicle systems. Robotics and Autonomous Systems 58(8), 1028–1036 (2010)
Ren, W., Sorensen, N.: Distributed coordination architecture for multi-robot formation control. Robotics and Autonomous Systems 56(4), 324–333 (2008)
Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. MIT Press (1998)
Kabysh, A., Golovko, V.: General model for organizing interactions in multi-agent systems. International Journal of Computing 11(3), 224–233 (2012)
Kabysh, A., Golovko, V., Lipnickas, A.: Influence Learning for Multi-Agent Systems Based on Reinforcement Learning. International Journal of Computing 11(1), 39–44 (2012)
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Dziomin, U., Kabysh, A., Golovko, V., Stetter, R. (2014). A Multi-agent Efficient Control System for a Production Mobile Robot. In: Golovko, V., Imada, A. (eds) Neural Networks and Artificial Intelligence. ICNNAI 2014. Communications in Computer and Information Science, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-319-08201-1_16
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DOI: https://doi.org/10.1007/978-3-319-08201-1_16
Publisher Name: Springer, Cham
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