Abstract
This paper presents an application of anticipatory network theory to model the behavior of a swarm of autonomous vehicles that share a common goal. In addition, each vehicle optimizes its individual performance criterion that is subordinated to the group goal. The internal swarm organization resembles a hierarchical control system where the top level is distinguished only by the hierarchy of goals, instead of a fixed assignment of powers or permissions. The arising variable hierarchy depends on the type of momentary performance of the swarm units: those performing activities leading directly to reaching the superordinated goal have the right-of-way and priority access to shared resources. Two principal problems need to be solved in this context. The first one is to recognize temporal hierarchies by swarm vehicles. This is accomplished by ensuring appropriate communication between vehicles via a local network. The second problem is to define behavior strategies that yield the best attainment of the common goal while individual indicators are nondominated. Solving both problems ensures a balance between cooperative (reaching a shared goal) and self-interested (individual goals) behavior. Finding a compromise strategy is equivalent to solving a certain anticipatory network. This model can be applied to supervising mining vehicle cooperation, where efficient communication and coordination of individual actions play central roles.
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References
Berger, C., Rumpe, B.: Autonomous driving-5 years after the urban challenge: the anticipatory vehicle as a cyber-physical system. In: Proceedings of the 10th Workshop on Automotive Software Engineering (ASE 2012), Braunschweig, September 2012, pp. 789–798 (2012)
Dai, X., Jiang, L., Zhao, Y.: Cooperative exploration based on supervisory control of multi-robot systems. Appl. Intell. 45, 18–29 (2016)
Fan, C., Hsu, C.H., Sun, Q., Yang, F.: A vertical handoff method via self-selection decision tree for internet of vehicles. IEEE Syst. J. 10(3), 1183–1193 (2016)
Hoogendoorn, S., Ossen, S., Schreuder, M.: Empirics of multianticipative car-following behavior. transportation research record. J Transp. Res. Board 1965, 112–120 (2006)
Huang, D., Hong, X., Gerla, M.: Situation-aware trust architecture for vehicular networks. Commun. Mag. IEEE 48(11), 128–135 (2010)
Huang, W., Viti, F., Tampère, C.M.J.: Repeated anticipatory network traffic control using iterative optimization accounting for model bias correction. Transp. Res. C67, 243–265 (2016)
Jiang, Z., Hsu, C.H., Zhang, D., Zou, X.: Evaluating rail transit timetable using big passengers’ data. J. Comput. Syst. Sci. 82(1, Part B), 144–155 (2016)
Kanamori, R., Takahashi, J., Ito, T.: Evaluation of anticipatory stigmergy strategies for traffic management. In: 2012 IEEE Vehicular Networking Conference, Seoul, pp. 33–39 (2012)
Rosen, R.: Anticipatory Systems - Philosophical. Mathematical and Methodological Foundations. Pergamon Press, London (1985). 2nd edn. Springer (2012)
Skulimowski, A.M.J.: Optimal control of a class of asynchronous discrete-event systems. In: Automatic Control in the Service of Mankind. Proceedings of the 11th IFAC World Congress, Tallinn (Estonia), 1990, vol.3, pp. 489–495. Pergamon Press, London (1991)
Skulimowski, A.M.J.: Freedom of choice and creativity in multicriteria decision making. In: Theeramunkong, T., Kunifuji, S., Sornlertlamvanich, V., Nattee, C. (eds.) KICSS 2010. LNCS (LNAI), vol. 6746, pp. 190–203. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24788-0_18
Skulimowski, A.M.J.: Anticipatory network models of multicriteria decision-making processes. Int. J. Syst. Sci. 45(1), 39–59 (2014). doi:10.1080/00207721.2012.670308
Skulimowski, A.M.J.: The art of anticipatory decision making. In: Kunifuji, S., Papadopoulos, G.A., Skulimowski, A.M.J., Kacprzyk, J. (eds.). AISC, vol. 416, pp. 17–35. Springer, Heidelberg (2016). doi:10.1007/978-3-319-27478-2_2
Skulimowski, A.M.J.: Selected methods, applications, and challenges of multicriteria optimization. In: Committee for Automation and Robotics of PAS, vol.19. AGH Publishers, Kraków (2016)
Wang, S., Lei, T., Zhang, L., Hsu, C.H., Yang, F.: Offloading mobile data traffic for QoS-aware service provision in vehicular cyber-physical systems. Future Gener. Comput. Syst. 61, 118–127 (2016)
Witkowski, M.: An action-selection calculus. Adapt. Behav. 15(1), 73–97 (2007)
Yang, S., Li, J., Liu, Z., Wang, S.: Managing trust for intelligence vehicles: a cluster consensus approach. In: Hsu, C.-H., Xia, F., Liu, X., Wang, S. (eds.) IOV 2015. LNCS, vol. 9502, pp. 210–220. Springer, Heidelberg (2015). doi:10.1007/978-3-319-27293-1_19
Acknowledgement
The background results on anticipatory networks have been obtained during the research project “Scenarios and Development Trends of Selected Information Society Technologies until 2025”, No. WND-POIG.01.01.01-00-021/09, financed by the ERDF within the Innovative Economy Operational Program 2006–2013.
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Skulimowski, A.M.J. (2016). Anticipatory Control of Vehicle Swarms with Virtual Supervision. In: Hsu, CH., Wang, S., Zhou, A., Shawkat, A. (eds) Internet of Vehicles – Technologies and Services. IOV 2016. Lecture Notes in Computer Science(), vol 10036. Springer, Cham. https://doi.org/10.1007/978-3-319-51969-2_6
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DOI: https://doi.org/10.1007/978-3-319-51969-2_6
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