[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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Fig. 4. Results of the second experiment As can be seen the quality of the sensing, which is related to the reliability has a direct influence on the effectiveness of the task performed. The last part of simulation research was to assess influence of exchanging information on the agent effectiveness improvement. In this experiment two agents were performing the task simultaneously. The agents were in permanent communication. The first agent was modeled as the one of high reliability. The reliability of the second one was set by the parameter to values 0.5 and 0. We can see that thanks to the communication and information fusion effectiveness of performing the task was im- proved (compare fig.4 and fig.5). Information Fusion in Multi-agent System Based on Reliability Criterion 215 We can observe that with the decrease of (what corresponds to increasing the random component) the reliability assessment decrease what means the eval- effectiveness of the pick up collection task performance. The first agent was performing 15 detect-collec cycles. The number of objects collected was counted. The procedure was repeated 5 times. The average number of objects collected in 5 simulation runs was the measure of the method effectiveness. The procedure described above was done for three different values of parameter . uation method works correctly. Next part of experiment was arranged to evaluate an reliability influence on the The results are gathered in the tabular (fig.4). Fig. 5. Results of the third experiment That proves that proper data fusion in MAS can improve the quality of the system operation.
216 M. Mellado and K. Skrzypczyk 6 Conclusion This paper addressed the problem of information fusion in MAS. The method of combining data provided by intercommunicated agents is discussed. The approach presented is based on reliability of particular agents. The level of reliability is evaluated by each agent while performing the task by confronting its data collected with the real state of the process. This approach is very simple to implement what is its great advantage. The functioning of the data fusion algorithm is illustrated using an example of pick up and collection task. Nevertheless the method is flexible and can be applied to any MAS in which agents are able to assess its reliability using different sensory systems, the vision systems for instance. Some relevant simulation examples are presented to prove the effectiveness of the approach presented. References [1] Cheng, X., Shen, J., Liu, H., Gu, G.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part III. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007) [2] Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logic controller in game theoretic framework. Robotics and Autonomous Systems 57(1) (2009) [3] Jones, C., Mataric, M.: Adaptive Division of Labor in Large-Scale Minimalist Multi-Robot Systems. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, pp. 1969–1974 (2003) [4] Kaminka, G.A., Erusalimchik, D., Kraus, S.: Adaptive Multi-Robot Coordination: A Game-Theoretic Perspective. In: Proc. of IEEE International Conference on Robotics and Automation, Anchorage Convention District, Anchorage, Alaska, USA (2002) [5] Kok, J.R., Spaan, M.T.J., Vlassis, N.: Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems 50(2-3), 99–114 (2005) [6] Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intelligent Systems 17(3), 42–47 (2002) [7] Kraus, S., Winkfeld, J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence 75, 297–345 (1995) [8] Kraus, S.: Negotiation and cooperation in multiagent environments. Artificial Intelligence 94(1-2), 79–98 (1997) [9] Mataric, M., Sukhatme, G., Ostergaard, E.: Multi-Robot Task Allocation in Uncertain Environments. Autonomous Robots 14, 255–263 (2003) [10] Schneider-Fontan, M., Mataric, M.J.: Territorial Multi-Robot Task Division. IEEE Transactionson Robotics and Automation 14(5), 815–822 (1998) [11] Winkfeld, K.J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence (75), 297–345 (1995)
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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