Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-28T16:07:45.207Z Has data issue: false hasContentIssue false

Adaptive aerial ecosystem framework to support tactical conflict resolution

Published online by Cambridge University Press:  25 September 2018

M. Radanovic*
Affiliation:
Department of Telecommunications and Systems EngineeringSchool of EngineeringAutonomous University of BarcelonaSabadellSpain
M.A. Piera
Affiliation:
Department of Telecommunications and Systems EngineeringSchool of EngineeringAutonomous University of BarcelonaSabadellSpain
T. Koca
Affiliation:
Department of Telecommunications and Systems EngineeringSchool of EngineeringAutonomous University of BarcelonaSabadellSpain

Abstract

To support a seamless transition between safety net layers in air traffic management, this article examines an extra capacity in the generation of the resolution trajectories, conditioned by future high dense, complex surrounding air traffic scenarios. The aerial ecosystem framework consists of a set of aircraft services inside a digitalised airspace volume, in which amended trajectories could induce a set of safety events such as an induced collision. Those aircraft services strive to the formation of a cost-efficient airborne separation management by exploring the preferred resolutions and actively interacting with each other. This study focuses on the dynamic analysis of a decreasing rate in the number of available resolutions, as well as the ecosystem deadlock event from the identified spatiotemporal interdependencies among the ecosystem aircraft at the separation management level. A deadlock event is characterised by a time instant at which an induced collision could emerge as an effect of an ecosystem aircraft trajectory amendment. Through simulations of two generated ecosystems, extracted from a real traffic scenario, the paper illustrates the relevant properties inside the structure of the ecosystem interdependencies, demonstrates and discusses an available time capacity for the resolution process of the aerial ecosystem.

Type
Research Article
Copyright
© Royal Aeronautical Society 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Liu, W. and Hwang, I. Probabilistic trajectory prediction and conflict detection for air traffic control, J Guidance Control and Dynamics, 2011, 34, (6), pp 17791789, https://doi.org/10.2514/1.53645.Google Scholar
2. Cook, A., Belkoura, S. and Zanin, M. ATM performance measurement in Europe, the US and China, Chinese J Aeronautics, 2017, 30, (2), pp 479–490, https://doi.org/10.1016/j.cja.2017.01.001.Google Scholar
3. Gluchshenko, O. and Foerster, P. Performance based approach to investigate resilience and robustness of an ATM System, Tenth USA/Europe Air Traffic Management R&D Seminar, p 7, 2013.Google Scholar
4. SESAR. SESAR-NextGen state of harmonisation, Integrated Communications, Navigation and Surveillance Conference, ICNS, May 2014, https://doi.org/10.1109/ICNSurv.2014.6820056.Google Scholar
5. Enea, G. and Porretta, M. A comparison of 4D-trajectory operations envisioned for Nextgen and SESAR, some preliminary findings, 28th Congress of the International Council of the Aeronautical Sciences, pp 1–14, 2012.Google Scholar
6. Prandini, M., Piroddi, L., Puechmorel, S. and Brázdilová, S. L. Toward air traffic complexity assessment in new generation air traffic management systems, IEEE Transactions on Intelligent Transportation Systems, 2011, 12, (3), pp 809–818, https://doi.org/10.1109/TITS.2011.2113175.Google Scholar
7. Tang, J., Piera, M. A. and Nosedal, J. Analysis of induced traffic alert and collision avoidance system collisions in unsegregated airspace using a Colored Petri Net model, Simulation, 2015, 91, (3), pp 233–248, https://doi.org/10.1177/0037549715570357.Google Scholar
8. Kochenderfer, M. J. and Chryssanthacopoulos, J. P. A decision-theoretic approach to developing robust collision avoidance logic, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2010, pp 1837–1842, https://doi.org/10.1109/ITSC.2010.5625063.Google Scholar
9. Sheperd, R., Cassell, R., Thapa, R. and Lee, D. A reduced aircraft separation risk assessment model, AIAA Guidance, Navigation and Control Conference, 1997, pp 1–16, https://doi.org/10.2514/6.1997-3735.Google Scholar
10. Bennett, S. The 1st July 2002 Mid-Air Collision over Überlingen, Germany: a holistic analysis. Risk Management, 2004, 6, (1), pp 31–49, https://doi.org/10.2307/3867933.Google Scholar
11. Murugan, S. TCAS functioning and enhancements, Int J Computer Application, 2010, 1, (8), pp. 46–50, https://doi.org/10.5120/184–320.Google Scholar
12. Radanovic, M., Piera, M. A., Koca, T. and Nieto, F. J. S. Self-reorganized supporting tools for conflict resolution in high-density airspace volumes, Twelfth USA/Europe Air Traffic Management Research and Development Seminar, June 2017.Google Scholar
13. Premm, M. and Kirn, S. Multiagent System Technologies, September 2015, Vol. 9433, Springer, Cham, https://doi.org/10.1007/978-3-319-27343-3_6.Google Scholar
14. Ramasamy, S., Sabatini, R., Gardi, A. and Kistan, T. Next generation flight management system for real-time trajectory based operations, Applied Mechanics and Materials, 2014, 629, pp 344–349, https://doi.org/10.4028/www.scientific.net/AMM.629.344.Google Scholar
15. Li, Y. On deadlock-free modular supervisory control of discrete-event systems, IEEE Transactions on Automatic Control, 1997, 42, (12), pp 1705–1708, https://doi.org/10.1109/9.650022.Google Scholar
16. Misra, J. Distributed discrete-event simulation, ACM Computing Survey, 1986, 18, (1), pp 39–65, https://doi.org/10.1145/6462.6485.Google Scholar
17. Hadzic, M., Wongthongtham, P., Dillon, T. and Chang, E. Ontology-Based Multi-Agent Systems, 2009, Vol. 219, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-01904-3.Google Scholar
18. Chaloulos, G., Roussos, G. P., Lygeros, J. and Kyriakopoulos, K. J. Mid and short term conflict resolution in autonomous aircraft operations, 8th Innov Res Work Exhib Proc, December 2009.Google Scholar
19. Tang, J., Piera, M. A. and Guasch, T. Coloured petri net-based traffic collision avoidance system encounter model for the analysis of potential induced collisions, Transportation Research Part C: Emerging Technologies, 2016, 67, pp 357–377, https://doi.org/10.1016/j.trc.2016.03.001.Google Scholar
20. Vela, P. A., Vela, A. E. and Ogunmakin, G. Topologically based decision support tools for aircraft routing, AIAA/IEEE Digital Avionics Systems Conference – Proceedings, December 2010, https://doi.org/10.1109/DASC.2010.5655530.Google Scholar
21. Dowek, G. and Munoz, C. Conflict detection and resolution for 1,2,… ,N aircraft, 7th AIAA Aviation Technology, Integration and Operations Conference, September 2007, https://doi.org/10.2514/6.2007-7737.Google Scholar
22. Radanovic, M. and Eroles, M. A. P. Spatially-temporal interdependencies for the aerial ecosystem identification, Procedia Computer Science, 2017, 104, pp 242–249, https://doi.org/10.1016/j.procs.2017.01.131.Google Scholar
23. Wandelt, S. and SUN, X. Efficient compression of 4D-trajectory data in air traffic management, IEEE Transactions on Intelligent Transportation Systems, 2015, 16, (2), pp 844–853, https://doi.org/10.1109/TITS.2014.2345055.Google Scholar