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Graphical composite modeling and simulation for multi-aircraft collision avoidance

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Abstract

Modeling and simulation for multi-aircraft collision avoidance to understand the mechanistic behavior is an important activity. Building models using general programming language typically requires specialist knowledge, and this limits the spread of modeling and simulation approach among multi-aircraft collision avoidance scenario. Thus, a software environment is needed to support convenient development of models by assembling components, when analysis demands changes. In this work, the graphical composite modeling and simulation software (GMAS extended) for multi-aircraft collision avoidance is introduced, with the basic graphical components and a graphical assembly editor. We define the serial and parallel execution semantics of GMASE-based model and then introduce the high-level graphical modeling interface, the low-level runtime engine of GMAS, and the simulation-based decision tree, which transforms a complex decision-making process into a collection of simpler decisions of finding the no collision or optimal sequence from some initial state to the goal state. To validate its efficiency and practicability, a three-aircraft collision avoidance model with TCAS operations is built on GMAS, which shows that using GMAS increases reusability and hiding complexity in graphical programming by splitting complex behavior into data flow and function components. The experimental result proves that GMAS not only provides a better representation for multi-aircraft collision avoidance, but also a useful approach for analyzing the potential collision occurrences.

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References

  1. Kuchar, J.K., Yang, L.C.: A review of conflict detection and resolution modeling methods. IEEE Trans. Intell. Transp. Syst. 1(4), 179–189 (2000)

    Article  Google Scholar 

  2. Love, W.D.: Preview of TCAS II Version 7. Air Traffic Control Q. Int. J. Eng. Oper. 6(4), 231–247 (2016)

    Article  Google Scholar 

  3. Billingsley, T. B., Espindle, I. P., Griffith, J. D.: TCAS multiple threat encounter analysis[R]. MIT Lincoln Laboratory, Project Report ATC-359, 2009

  4. Tang, J., Piera, M.A., Guasch, T.: Colored Petri net-based traffic collision avoidance system encounter model for the analysis of potential induced collisions. Transp. Res. Part C 67, 357–377 (2016)

    Article  Google Scholar 

  5. Haeusler, M., Trojer, T., Kessler, J., et al.: ChronoSphere: a graph-based EMF model repository for IT landscape models. Softw. Syst. Model. 4, 1–40 (2019)

    Google Scholar 

  6. Mirko, W., Simon, O., Manfred, K., Nikolaus, V., Asfour, T.: The ArmarX statechart concept: graphical programing of robot behavior. Front. Robot. AI 3, 33 (2016)

    Google Scholar 

  7. Tang, J., Zhu, F., Fan, L.: Simulation modelling of traffic collision avoidance system with wind disturbance. IEEE A&E Syst. Mag. 33(4), 36–45 (2018)

    Article  Google Scholar 

  8. Billingsley, T.B., Kochenderfer, M.J., Chryssanthacopoulos, J.P.: Collision avoidance for general aviation. IEEE Aerosp. Electron. Syst. Mag. 27(7), 4–12 (2012)

    Article  Google Scholar 

  9. Kochenderfer, M.J., Holland, J.E., Chryssanthacopoulos, J.P.: Next-generation airborne collision avoidance system. Linc. Lab. J. 19(1), 17–33 (2012)

    Google Scholar 

  10. Kochenderfer, M.J., Chryssanthacopoulos, J.P., Weibel, R.E.: A new approach for designing safer collision avoidance systems. Air Traffic Control Q. 20(1), 27–45 (2012)

    Article  Google Scholar 

  11. Holland, J.E., Kochenderfer, M.J., Olson, W.A.: Optimizing the next generation collision avoidance system for safe, suitable, and acceptable operational performance. Air Traffic Control Q. 21(3), 275–297 (2013)

    Article  Google Scholar 

  12. Yang, L., Yang, J. H., Kuchar, J., et al.: A real-time Monte Carlo implementation for computing probability of conflict. In: AIAA Guidance, Navigation and Control Conference. August 2004

  13. Kochenderfer, M. J., Kuchar, J., Griffith, J., Espindle, L.: A Bayesian approach to aircraft encounter modeling. In: AIAA Guidance, Navigation and Control Conference. August 2008

  14. Griffith, J. D., Edwards, M. W., Miraflor, R. M., Weinert. A. J.: “Due regard encounter model version 1.0,” Massachusetts Institute of Technology, Lincoln Laboratory, Lexington, MA, Project Report ATC-397, August 2013

  15. Kochenderfer, M.J., Edwards, M.W., Espindle, L.P., et al.: Airspace encounter models for estimating collision risk. J. Guid. Control Dyn. 33(2), 487–499 (2010)

    Article  Google Scholar 

  16. Asmar, D.M., Kochenderfer, M.J., Chryssanthacopoulos, J.P.: Vertical state estimation for aircraft collision avoidance with quantized measurements. J. Guid. Control Dyn. 36(6), 1797–1802 (2013)

    Article  Google Scholar 

  17. Alonso-Ayuso, A., Escudero, L.F., Martín-Campo, F.J.: On modeling the air traffic control coordination in the collision avoidance problem by mixed integer linear optimization. Ann. Oper. Res. 222(1), 89–105 (2014)

    Article  MathSciNet  Google Scholar 

  18. Underhill, N., Harkleroad, E., Guendel, R., Maki, D., Edwards, M.: Correlated encounter model for cooperative aircraft in the national airspace system; Version 2.0,” Massachusetts Institute Technology Lincoln Laboratory Lexington United States, May 2018

  19. Smith, K.A., Vela, A.E., Kochenderfer, M.J., et al.: Optimizing a collision-avoidance system for closely spaced parallel operations. J. Aerosp. Inf. Syst. 12(10), 1–16 (2015)

    Google Scholar 

  20. Murata T.: Petri nets: properties, analysis and applications. In: Proceedings of the IEEE, pp. 541–580 (1989)

  21. Sarkar, S., Dutta, A.: Petri net based modelling of railway intersection collision avoidance system. In: IEEE international conference on intelligent rail transportation, pp. 356–361 (2016)

  22. Tang, J., Zhu, F., Piera, M.A.: A causal encounter model of traffic collision avoidance system operations for safety assessment and advisory optimization in high-density airspace. Transp. Res. Part C 96, 347–365 (2018)

    Article  Google Scholar 

  23. Jensen, L., Kristensen, L.M.: Coloured petri nets—modelling and validation of concurrent systems[M]. Springer, Berlin (2009)

    Book  Google Scholar 

  24. Kummer, O., Wienberg, F., Duvigneau, M., et al.: An extensible editor and simulation engine for petri nets: renew. In: International conference on applications and theory of petri nets and other models of concurrency (ICATPN 2004), vol. 3099, pp. 484–493 (2004)

  25. Aalst, W. M., Stahl, C., Westergaard, M.: Strategies for modeling complex processes using colored petri nets. Transactions on Petri Nets and Other Models of Concurrency VII, pp. 6–55 (2013)

  26. Schruben, L.: Simulation modeling with event graphs. Commun. ACM 26(11), 957–963 (1983)

    Article  Google Scholar 

  27. Buss, A., Blais, C.: Composability and component-based discrete event simulation. In: Proceedings of the 2007 Winter Simulation Conference, pp. 694–702 (2007)

  28. Lara, J.D.: Distributed event graphs formalizing component-based modeling and simulation. Electron. Notes Theor. Comput. Sci. 127, 145–162 (2005)

    Article  Google Scholar 

  29. Wang, B., Deng, B., Xing, F., Wang, D., Yao, Y.: Partitioned event graph: formalizing LP-based modelling of parallel discrete-event simulation. Math. Comput. Modell. Dyn. Syst. 21(2), 153–179 (2014)

    Article  MathSciNet  Google Scholar 

  30. Harel, D.: Statecharts: a visual formalism for computer system. Sci. Comput. Program. 8(3), 231–274 (1987)

    Article  Google Scholar 

  31. Cicirelli, F., Furfaro, A., Nigro, L.: Modelling and simulation of complex manufacturing systems using statechart-based actors. Simul. Model. Pract. Theory 19(2), 685–703 (2011)

    Article  Google Scholar 

  32. Zeigler, B.P., Praehofer, H., Kim, T.G.: Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems. Academic Press, Hoboken (2000)

    Google Scholar 

  33. Palaniappan, S., Sarjoughian, H. S.: Application of the DEVS framework in construction simulation. In: Proceedings of the 2006 winter simulation conference, pp. 2077–2086 (2006)

  34. Wainer, G. A.: Modeling and simulation of complex systems with CELL-DEVS. In: Proceedings of the 2004 Winter Simulation Conference, pp. 49–60 (2004)

  35. Cobanoglu, B., Zengin, A., Ekiz, H., et al.: Implementation of DEVS based distributed network simulator for large-scale networks. Int. J. Simul. Modell. 13(2), 147–158 (2014)

    Article  Google Scholar 

  36. Tang, J., Zhu, F.: Graphical modelling and analysis software for state space-based optimization of discrete event systems. IEEE Access. 99, 1–15 (2018)

    Google Scholar 

  37. Tang, W., Yao, Y., Zhu, F.: A hierarchical parallel discrete event simulation kernel for multicore platform. Cluster Comput. 16(3), 379–387 (2013)

    Article  Google Scholar 

  38. FAA. Introduction to TCAS II version 7.1. Federal Aviation Administration, 2011

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Authors and Affiliations

  1. College of Systems Engineering, National University of Defense Technology, Changsha, China

    Feng Zhu & Jun Tang

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  1. Feng Zhu
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  2. Jun Tang
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Correspondence to Jun Tang.

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Communicated by Robert Pettit.

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This work was supported in part by the National Natural Science Foundation of China under Grant 61903368.

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Zhu, F., Tang, J. Graphical composite modeling and simulation for multi-aircraft collision avoidance. Softw Syst Model 20, 821–835 (2021). https://doi.org/10.1007/s10270-020-00830-5

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