Journal of Intelligent & Robotic Systems

, Volume 95, Issue 1, pp 211–227 | Cite as

Analysis of Using Mixed Reality Simulations for Incremental Development of Multi-UAV Systems

  • Martin SeleckýEmail author
  • Jan Faigl
  • Milan Rollo


Developing complex robotic systems requires expensive and time-consuming verification and testing which, especially in a case of multi-robot unmanned aerial systems (UASs), aggregates risk of hardware failures and may pose legal issues in experiments where operating more than one unmanned aircraft simultaneously is required. Thus, it is highly favorable to find and resolve most of the eventual design flaws and system bugs in a simulation, where their impacts are significantly lower. On the other hand, as the system development process approaches the final stages, the fidelity of the simulation needs to rise. However, since some phenomena that can significantly influence the system behavior are difficult to be modeled precisely, a partial embodiment of the simulation in the physical world is necessary. In this paper, we present a method for incremental development of complex unmanned aerial systems with the help of mixed reality simulations. The presented methodology is accompanied with a cost analysis to further show its benefits. The generality and versatility of the method is demonstrated in three practical use cases of various aviation systems development: (i) an unmanned system consisting of heterogeneous team of autonomous unmanned aircraft; (ii) a system for verification of collision avoidance methods among fixed wing unmanned aerial vehicles; and (iii) a system for planning collision-free paths for light-sport aircraft.


UAS development Mixed-reality simulations UAS applications Multi-UAV systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The presented work has been supported by the Czech Science Foundation (GAČR) under research project No. 16-24206S, Ministry of Agriculture of the Czech Republic under contract No. QJ1520187, and by the Technology Agency of the Czech Republic under project No. TA01030847.


  1. 1.
    Jakob, M., Pěchouček, M., Čáp, M., Novák, P., Vaněk, O.: Mixed-reality testbeds for incremental development of HART applications. IEEE Intell. Syst. 27(2), 19–25 (2012)CrossRefGoogle Scholar
  2. 2.
    Garcia, R., Barnes, L.: Multi-UAV simulator utilizing x-plane. In: Selected papers from the 2nd international symposium on UAVs, Reno, Nevada, USA June 8-10, pp. 393–406 (2009)Google Scholar
  3. 3.
    Komenda, A., Vokřínek, J., Čáp, M., Pěchouček, M.: Developing multiagent algorithms for tactical missions using simulation. IEEE Intell. Syst. 28(1), 42–49 (2013)CrossRefGoogle Scholar
  4. 4.
    Honig, W., Milanes, C., Scaria, L., Phan, T., Bolas, M., Ayanian, N.: Mixed reality for robotics. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 5382–5387 (2015)Google Scholar
  5. 5.
    Chen, I.Y.H., MacDonald, B., Wunsche, B.: Mixed reality simulation for mobile robots. In: IEEE International conference on robotics and automation (ICRA), pp. 232–237 (2009)Google Scholar
  6. 6.
    Day, M.A., Clement, M.R., Russo, J.D., Davis, D., Chung, T.H.: Multi-uav software systems and simulation architecture. In: International conference on unmanned aircraft systems (ICUAS), pp. 426–435 (2015)Google Scholar
  7. 7.
    Pizetta, I.H.B., Brandao, A.S., Sarcinelli-Filho, M.: A hardware-in-the-loop platform for rotary-wing unmanned aerial vehicles. J. Intell. Robot. Syst. 84(725), 725–743 (2016)CrossRefGoogle Scholar
  8. 8.
    Selecký, M., Rollo, M., Losiewicz, P., Reade, J., Maida, N.: Framework for incremental development of complex unmanned aircraft systems. In: Integrated communication, navigation, and surveillance conference (ICNS), pp. J3–1. IEEE (2015)Google Scholar
  9. 9.
    Selecký, M., Štolba, M., Meiser, T., Čáp, M., Komenda, A., Rollo, M., Vokřínek, J., Pěchouček, M.: Deployment of multi-agent algorithms for tactical operations on UAV hardware. In: International conference on autonomous agents and multi-agent systems (AAMAS), pp. 1407–1408 (2013)Google Scholar
  10. 10.
    Selecký, M., Rollo, M.: Distributed control of heterogeneous team of autonomous uavs. In: Proceedings of EXPONENTIAL 2016: Association for unmanned vehicle systems (AUVSI), pp. 707–717 (2016)Google Scholar
  11. 11.
    Selecký, M., Faigl, J., Rollo, M.: Mixed reality simulation for incremental development of multi-uav systems. In: International conference on unmanned aircraft systems (ICUAS), pp. 1530–1538. IEEE (2017)Google Scholar
  12. 12.
    Mutter, F., Gareis, S., Schatz, B., Bayha, A., Gruneis, F., Kanis, M., Koss, D.: Model-driven in-the-loop validation: Simulation-based testing of UAV software using virtual environments. In: 18th IEEE International Conference and Workshops on Engineering of Computer Based Systems (ECBS), pp. 269–275 (2011)Google Scholar
  13. 13.
    Demers, S., Gopalakrishnan, P., Kant, L.: A generic solution to software-in-the-loop. In Military communications conference (MILCOM), pp. 1–6. IEEE (2007)Google Scholar
  14. 14.
    Goktogan, A.H., Sukkarieh, S.: Distributed simulation and middleware for networked UAS. In: Unmanned aircraft systems, pp. 331–357 (2008)Google Scholar
  15. 15.
    Šišlák, D., Volf, P., Kopřiva, Š., Pěchouček, M.: Agentfly: A multi-agent airspace test-bed (2008)Google Scholar
  16. 16.
    Burkle, A., Segor, F., Kollman, M.: Towards autonomous micro UAV swarms. J. Intell. Robot. Syst. 61 (1), 339–353 (2011)CrossRefGoogle Scholar
  17. 17.
    Scerri, P., Von Gonten, T., Fudge, G., Owens, S., Sycara, K.: Transitioning multiagent technology to UAV applications. In: International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS): Industrial track, pp. 89–96 (2008)Google Scholar
  18. 18.
    Sanchez-Lopez, J.L., Pestana, J., de la Puente, P., Campoy, P.: A reliable open-source system architecture for the fast designing and prototyping of autonomous multi-uav systems: Simulation and experimentation. J. Intell. Robot. Syst. 84(1-4), 779–797 (2016)CrossRefGoogle Scholar
  19. 19.
    Chudy, P., Dittrich, P., Rzucidlo, P.: HIL simulation of a light aircraft flight control system. IEEE/AIAA 31st digital avionics systems conference (DASC), pp. 6D1–1 (2012)Google Scholar
  20. 20.
    Aydemir, M.E.: Design and implementation of a compact avionics instrument for light aviation. Turk. J. Electr. Eng. Comput. Sci. 24(5), 3471–3482 (2016)CrossRefGoogle Scholar
  21. 21.
    Pačes, P., Levora, T., Bruna, O., Popelka, J., Mlejnek, J.: Integrated modular avionics onboard of small airplanes: Fiction or reality?. In: IEEE/AIAA 30th digital avionics systems conference (DASC), pp. 7A1–1 (2011)Google Scholar
  22. 22.
    Rydlo, K., Rzucidlo, P., Chudy, P.: Simulation and prototyping of FCS for sport aircraft. Aircraft Eng. Aerospace Technol. 85(6), 475–486 (2013)CrossRefGoogle Scholar
  23. 23.
    Haberkorn, T., Koglbauer, I., Braunstingl, R., Prehofer, B.: Requirements for future collision avoidance systems in visual flight: a human-centered approach. IEEE Trans. Human-Mach. Syst. 43(6), 583–594 (2013)CrossRefGoogle Scholar
  24. 24.
    Pellebergs, J., Aeronautics, S.: The MIDCAS project. Saab Aeronautics (2012)Google Scholar
  25. 25.
    Munoz, C., Narkawicz, A., Hagen, G., Upchurch, J., Dutle, A., Consiglio, M., Chamberlain, J.: DAIDALUS: detect and avoid alerting logic for unmanned systems. In: IEEE/AIAA 34th Digital Avionics Systems Conference (DASC), pp. 5A1–1 (2015)Google Scholar
  26. 26.
    Chen, I.Y.H., MacDonald, B., Wunsche, B.: Evaluating the effectiveness of mixed reality simulations for developing uav systems. In: International conference on simulation, modeling, and programming for autonomous robots, pp. 388–399 (2012)Google Scholar
  27. 27.
    Selecký, M., Meiser, T.: Integration of autonomous UAVs into multi-agent simulation. Acta Polytechnica 52(5), 93–99 (2012)Google Scholar
  28. 28.
    Chiariglione, L.: FIPA: Foundation for intelligent physical agents, 2001. [cited 5] (2017)Google Scholar
  29. 29.
    Jensen, F., Petersen, N.E.: Burn-in: an engineering approach to the design and analysis of burn-in procedures. Wiley, New York (1982)Google Scholar
  30. 30.
    Xie, M., Lai, C.D.: Reliability analysis using an additive weibull model with bathtub-shaped failure rate function. Reliability Engineering & System Safety 52(1), 87–93 (1996)CrossRefGoogle Scholar
  31. 31.
    Meiser, T.: Distributed topology control in MANETs. Master’s thesis, Czech Technical University in Prague (2012)Google Scholar
  32. 32.
    Asadpour, M., Van den Bergh, B., Giustiniano, D., Hummel, K., Pollin, S., Plattner, B.: Micro aerial vehicle networks: An experimental analysis of challenges and opportunities. IEEE Commun. Mag. 52(7), 141–149 (2014)CrossRefGoogle Scholar
  33. 33.
    Nuic, A., Poles, D., Bada, V. Mouillet.: An advanced aircraft performance model for present and future atm systems. Int. J. Adapt Control Signal Process. 24(10), 850–866 (2010)CrossRefzbMATHGoogle Scholar
  34. 34.
    Šišlák, D., Volf, P., Pěchouček, M.: Accelerated A* path planning. In: Proceedings of The 8th international conference on autonomous agents and multiagent systems-volume 2, pp. 1133–1134. International Foundation for Autonomous Agents and Multiagent Systems (2009)Google Scholar
  35. 35.
    Šišlák, D., Volf, P., Komenda, A., Samek, J., Pěchouček, M.: Agent-based multi-layer collision avoidance to unmanned aerial vehicles. In: International conference on integration of knowledge intensive multi-agent (KIMAS), pp. 365–370. IEEE (2007)Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic

Personalised recommendations