THE SYNTHESIS OF ROUTES OF UAVS’ SUB-SWARMS BASED ON HOPFIELD NEURAL NETWORK FOR INSPECTION OF TERRITORIES

Authors

  • I. M. Zhuravska Petro Mohyla Black Sea National University, Mykolaiv, Ukraine
  • M. P. Musiyenko Petro Mohyla Black Sea National University, Mykolaiv, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2017-3-10

Keywords:

Unmanned air vehicle, sub-swarm, flock, Hopfield neural network, dubbed tasks.

Abstract

Context. The urgent task the economy of the limited power, computing and technological resources of small unmanned aerial vehicles (UAVs) has been solved.

Objective is a creation of sub-swarms’ routes synthesis method with increasing the time of UAV flock viability.

Method. The method for model building of the UAVs’ sub-swarm is offered. It allows to avoid the dubbed tasks at any node of grids that cover the survey territory. Combining Hopfield neural network’s map and flight map for each sub-swarm provides an information via wireless communication modules of UAV about the executed facts of monitoring or technological tasks by any individual UAV of sub-swarm to rest of UAVs. This approach allows to use the self-healing properties of the sub-swarms in flocks of bird-like objects (“boids”) by means redefining the tasks of sub-swarms as a cyber-physical system in case of loss of several boids during a critical usage. The structure of the resulting sub-swarms’ behavior models is implemented in two-dimensional spatial corridors of arbitrary shape; then achieved 2D-solving are concatenated. This can significantly speed up the tasks survey territories.

Results. The software implementing proposed method have been developed and used in computational experiments investigating the properties of the method. The experiments confirmed the efficiency of the proposed method and software.

Conclusions. The experiments also allow to recommend them for use in practice to solve the problems survey area using boids’ flock.

Author Biographies

I. M. Zhuravska, Petro Mohyla Black Sea National University, Mykolaiv

Ph.D., Associate Professor, Doctorant of Department of Computer Engineering

M. P. Musiyenko, Petro Mohyla Black Sea National University, Mykolaiv

Dr.Sc., Professor, Dean of Faculty of Computer Science

References

Аchаsоvа А. Drones – modern tools for farmers [Electronic resource], АgroPRO, 2016, Oct. (special issue), pp. 44–46. Access mode: https://agropro.club/articles/bezpilotniki-suchasnijinstrument-dlya agrariya/.

Perdix fact sheet: Release of the Strategic capabilities office DoD [Electronic resource]. Access mode: https://www.defense.gov/ Portals/1/Documents/pubs/Perdix%20Fact%20Sheet.pdf.

Darintsev O. V., Migranov A. B., Yudincev B. S. Neural network algorithm of planning trajectories for a group of mobile robots, Artificial Intelligence ; Ufa state aviation technical university, 2011, No. 1, pp. 154–160.

Rahman T., Hariadi M., Sumpeno S. NCP striking pattern in combat situation using boids behaviour, Intelligent technology and its application (ISITIA) : IEEE international seminar. Surabaya, Indonesia, 22–24 May, 2014 : proceedings [Electronic resource]. Access mode: http://isitia.its.ac.id/base/index.php/SITIA/2014/paper/viewFile/343/143.

Wang N., Wang L., Go X., Chen L., Shen L. Hopfield neural network guided evolutionary algorithm for aircraft penetration path planning, Advances in neural network research and applications (LISEE, 67). Berlin, Heidelberg, Springer-Verlag, 2010, pp. 235–243.

Subbotin S. A. Building a fully defined neuro-fuzzy network with a regular partition of a feature space based on large sample, Radio electronics, computer science, control, 2016, No. 3, pp. 47–53.

Hopfield J. J., Brody C. D. What is a Moment? Transient synchrony as a collective mechanism for spatiotemporal integration, Proceedings of the NAS of the USA, 2001, Vol. 98, No. 3, pp. 1282–1287. DOI: 10.1073/pnas.98.3.1282.

Musiyenko M. P., Zhuravska I. M., Kulakovska I. V., Kulakovska A. V. Simulation the behavior of robot sub-swarm in spatial corridors, Electronics and nanotechnology (ELNANO) : 36th international conference, Kyiv, 19–21 Apr. 2016 : proceedings. Kyiv : IEEE; NTUU “KPI”, 2016, P. 382–387. DOI: 10.1109/ELNANO.2016.7493090.

Melnyk A., Golembo V., Bochkaryov A. Multiagent approach to the distributed autonomous explorations, Adaptive hardware and systems AHS 2007 : conference, Edinburgh, 5–8 Aug. 2007 : proceedings. Scotland, UK, NASA/ESA, 2007, pp. 568–572.

Zhuravska I. Ensuring a stable wireless communication in cyberphysical systems with moving objects, Technology audit and production reserves, 2016, Vol. 5, No. 2(31), pp. 58–64. DOI: 10.15587/2312-8372.2016.80784.

How to Cite

Zhuravska, I. M., & Musiyenko, M. P. (2017). THE SYNTHESIS OF ROUTES OF UAVS’ SUB-SWARMS BASED ON HOPFIELD NEURAL NETWORK FOR INSPECTION OF TERRITORIES. Radio Electronics, Computer Science, Control, (3), 86–94. https://doi.org/10.15588/1607-3274-2017-3-10

Issue

Section

Neuroinformatics and intelligent systems