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Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 1/2016

01.02.2016 | Regular Paper

Automatic profile generation for UAV operators using a simulation-based training environment

verfasst von: Víctor Rodríguez-Fernández, Héctor D. Menéndez, David Camacho

Erschienen in: Progress in Artificial Intelligence | Ausgabe 1/2016

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Abstract

Unmanned aerial vehicles (UAVs) are becoming a hot topic in the last few years for several research areas, such as aeronautics or computer science. Big companies such as Airbus or Amazon aim to incorporate this technology to their current systems to improve the quality of their services while reducing the human costs. However, current UAV technology requires a strong human supervisory control and this supposes an important potential risk. Therefore, it is critical to keep track of the pilot behavior to be able to determine whether he is ready or not to operate with this technology. To deal with this problem, we have developed a methodology based on different performance metrics to automatically evaluate planning and monitoring skills of new users trained in a multi-UAV simulation environment. This methodology, based on unsupervised learning and fuzzy logic, can automatically generate a profile of future operators and use it to assess their skills.
Vorheriger Artikel Erratum to: GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity
Nächster Artikel Fuzzy shape grammars

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Literatur
1.
Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Inf. Retr. 12(4), 461–486 (2009)CrossRef
2.
Beard, R.W., McLain, T.W.: Multiple UAV cooperative search under collision avoidance and limited range communication constraints. In: Proceedings of the 42nd IEEE Conference on Decision and Control, vol. 1, pp. 25–30. IEEE, Mani, Hawaii, USA (2003)
3.
Begis, G.: Adaptive gaming behavior based on player profiling. In: US Patent US6106395 A (2000)
4.
Boussemart, Y., Cummings, M.L., Fargeas, J.L., Roy, N.: Supervised vs. unsupervised learning for operator state modeling in unmanned vehicle settings. J. Aerosp. Comput. Inf. Commun. 8(3), 71–85 (2011)CrossRef
5.
Brock, G., Pihur, V., Datta, S., Datta, S.: clValid, an R package for cluster validation. J. Stat. Softw. (2008)
6.
Dixon, S.R., Wickens, C.D., Chang, D.: Unmanned aerial vehicle flight control: false alarms versus misses. In: 48th Annual Meeting of Human Factors and Ergonomic Society (2004)
7.
Drury, J.L., Scholtz, J., Yanco, H.A.: Awareness in human–robot interactions. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 912–918. IEEE, Washington, DC, USA (2003)
8.
Eddy, S.R.: Hidden Markov models. Curr. Opin. Struct. Biol. 6(3), 361–365 (1996)CrossRef
9.
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)
10.
Goodrich, M.A., Schultz, A.C.: Human–robot interaction: a survey. Found. Trends Hum. Comput. Interact. 1(3), 203–275 (2007)MATHCrossRef
11.
Günnemann, S., Färber, I., Müller, E., Assent, I., Seidl, T.: External evaluation measures for subspace clustering. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1363–1372. ACM, Glasgow, UK (2011)
12.
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a \(k\)-means clustering algorithm. Appl. Stat. 100–108 (1979)
13.
Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)CrossRef
14.
Kaufman, L., Rousseeuw, P.: Clustering by means of medoids (1987)
15.
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley (2009)
16.
Kendoul, F.: Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J. Field Robotics 29(2), 315–378 (2012)CrossRef
17.
Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic, vol. 4. Prentice Hall (1995)
18.
Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley (2014)
19.
Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 911–916. IEEE, Sydney, Australia (2010)
20.
Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1650–1654 (2002)
21.
McKinley, R.A., McIntire, L.K., Funke, M.A.: Operator selection for unmanned aerial systems: comparing video game players and pilots. Aviat. Space Environ. Med. 82(6), 635–642 (2011)CrossRef
22.
Pavlas, D., Burke, C.S., Fiore, S.M., Salas, E., Jensen, R., Fu, D.: Enhancing unmanned aerial system training: a taxonomy of knowledge, skills, attitudes, and methods. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 53, pp. 1903–1907. SAGE Publications, San Antonio, Texas, USA (2009)
23.
Rodríguez-Fernández, V., Menéndez, H.D., Camacho, D.: Analyzing planning and monitoring skills of users in a multi-UAV simulation environment. In: 16th Conference of the Spanish Association for Articial Intelligence. Advances in Artificial Intelligence, CAEPIA 2015, Albacete, 9–12 November 2015 Proceedings, pp. 255–264 (2015)
24.
Rodriguez-Fernandez, V., Menendez, H.D., Camacho, D.: Design and development of a lightweight multi-UAV simulator. In: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), pp. 255–260. IEEE, Gydnia, Poland (2015)
25.
Rodriguez-Fernandez, V., Ramirez-Atencia, C., Camacho, D.: A multi-UAV mission planning videogame-based framework for player analysis. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1490–1497. IEEE, New York (2015)
26.
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)MATHCrossRef
27.
Stulberg, A.N.: Managing the unmanned revolution in the US air force. Orbis 51(2), 251–265 (2007)CrossRef
28.
Xu, R., Wunsch, D., et al.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRef
29.
Yager, R.R., Filev, D.P.: Essentials of Fuzzy Modeling and Control. New York (1994)
30.
Zadeh, L.A., Klir, G.J., Yuan, B.: Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers, vol. 6. World Scientific (1996)
31.
Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 30(4), 451–462 (2000)CrossRef
Metadaten
Titel
Automatic profile generation for UAV operators using a simulation-based training environment
verfasst von
Víctor Rodríguez-Fernández
Héctor D. Menéndez
David Camacho
Publikationsdatum
01.02.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Progress in Artificial Intelligence / Ausgabe 1/2016
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-015-0072-y

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