Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

A Framework for Autonomous UAV Swarm Behavior Simulation

DOI: http://dx.doi.org/10.15439/2019F278

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 471–478 ()

Full text

Abstract. In the last several years a large interest in the unmanned aerial vehicles (UAVs) has been seen. This is mostly due to an increase of computational power and decreasing cost of the UAVs itself. One of an intensively researched area is an application of a swarm behavior within team of such UAVs. Simulation tools are one of the means with which quality of solutions in this matter can be measured. In this paper such simulation framework is proposed. The proposed framework is capable of taking under consideration interferences between communicating UAVs, as well as interaction between UAV and surrounding environment. Mathematical models based on which simulation is performed were described, definition of simulation scenario and results of exemplary simulation were also presented.

References

  1. A. Kolling, P. M. Walker, N. Chakraborty, K. P. Sycara, and M. Lewis, “Human interaction with robot swarms: A survey,” IEEE Transactions on Human-Machine Systems, vol. 46, pp. 9–26, 2016.
  2. C. Gao, Z. Zhen, and H. Gong, “A self-organized search and attack algorithm for multiple unmanned aerial vehicles,” Aerospace Science and Technology, 2016.
  3. M. Kim, H. Baik, and S. Lee, “Response threshold model based uav search planning and task allocation,” Journal of Intelligent & Robotic Systems, 2014.
  4. H. Cheng, J. Page, and J. Olsen, “Dynamic mission control for uav swarm via task stimulus approach,” American Journal of Intelligent System, vol. 2, pp. 177–183, 01 2013.
  5. G. Wang, Q. Li, and L. Guo, “Multiple uavs routes planning based on particle swarm optimization algorithm,” International Symposium on Information Engineering and Electronic Commerce, 07 2010.
  6. W. Zhenhua, Z. Weiguo, S. Jingping, and H. Ying, “Uav route planning using multiobjective ant colony system,” in Conference on Cybernetics and Intelligent Systems, pp. 797 – 800, 10 2008.
  7. T. Ahmed, D. Feil-Seifer, T. Jiang, S. Jose, S. Liu, and S. Louis, “Development of a swarm uav simulator integrating realistic motion control models for disaster operations,” 10 2017.
  8. M. Brust, G. Danoy, P. Bouvry, D. Gashi, H. Pathak, and M. P. Goncalves, “Defending against intrusion of malicious uavs with networked uav defense swarms,” pp. 103–111, 10 2017.
  9. J. Wang, Y. Tang, J. Kavalen, A. Abdelzaher, and S. P. Pandit, “Autonomous uav swarm: Behavior generation and simulation,” in Conference on Unmanned Aircraft Systems (ICUAS), pp. 1–8, 06 2018. 10] S. Rasmussen, J. Mitchell, P. Chandler, C. Schumacher, and A. Smith, “Introduction to the multiuav2 simulation and its application to cooperative control research,” pp. 4490 – 4501 vol. 7, 07 2005. 11] R. Garcia and L. Barnes, “Multi-uav simulator utilizing x-plane,” Journal of Intelligent and Robotic Systems, vol. 57, pp. 393–406, 01 2010. 12] J. Bachrach, J. Mclurkin, and A. Grue, “Protoswarm: A language for programming multi-robot systems using the amorphous medium abstraction,” vol. 2, pp. 1175–1178, 01 2008. 13] F. Bullo, J. Cortés, and S. Martínez, Distributed Control of Robotic Networks. Applied Mathematics Series, Princeton University Press, 2009. Electronically available at http://coordinationbook.info. 14] P. Kruchten, “Architectural blueprints – the 4+1 view model of software architecture,” IEEE Software 12, 1995. 15] “https://www.payara.fish/. 16] “https://javaee.github.io/glassfish/.