THE SYNTHESIS OF ROUTES OF UAVS’ SUB-SWARMS BASED ON HOPFIELD NEURAL NETWORK FOR INSPECTION OF TERRITORIES
Keywords:Unmanned air vehicle, sub-swarm, flock, Hopfield neural network, dubbed tasks.
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.
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
Copyright (c) 2017 I. M. Zhuravska, M. P. Musiyenko
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.