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


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.

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  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)

    Article  Google 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)

  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)

    Article  Google 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)

  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)

  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)

  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)

    Article  Google 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)

  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)

  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)

  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)

  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)

  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)

  14. 14.

    Goktogan, A.H., Sukkarieh, S.: Distributed simulation and middleware for networked UAS. In: Unmanned aircraft systems, pp. 331–357 (2008)

  15. 15.

    Šišlák, D., Volf, P., Kopřiva, Š., Pěchouček, M.: Agentfly: A multi-agent airspace test-bed (2008)

  16. 16.

    Burkle, A., Segor, F., Kollman, M.: Towards autonomous micro UAV swarms. J. Intell. Robot. Syst. 61 (1), 339–353 (2011)

    Article  Google 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)

  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)

    Article  Google 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)

  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)

    Article  Google 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)

  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)

    Article  Google 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)

    Article  Google Scholar 

  24. 24.

    Pellebergs, J., Aeronautics, S.: The MIDCAS project. Saab Aeronautics (2012)

  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)

  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)

  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)

  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)

    Article  Google Scholar 

  31. 31.

    Meiser, T.: Distributed topology control in MANETs. Master’s thesis, Czech Technical University in Prague (2012)

  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)

    Article  Google 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)

    Article  MATH  Google 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)

  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)

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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.

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Correspondence to Martin Selecký.

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Selecký, M., Faigl, J. & Rollo, M. Analysis of Using Mixed Reality Simulations for Incremental Development of Multi-UAV Systems. J Intell Robot Syst 95, 211–227 (2019).

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