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Novel nature-inspired autonomous guidance of aerial robots formation regarding honey bee artificial algorithm and fuzzy logic

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this article, a novel nature-inspired autonomous guidance is investigated regarding the honey bee motion algorithm for aerial robots and fuzzy logic. Combination of the bee al- gorithm and fuzzy logic is proposed to achieve an on-line guidance for methodology of this research. The main idea of this work belongs to a novel analogy between honey bees and aerial robots motions. Moreover, information links between the aerial robots are demon- strated to construct a formation of vehicles by updating motions based on fuzzy decision making. Three dimensional simulations for the aerial robots are considered to show the ef- ficient performance of autonomous guidance. The simulation results show precise ability of the proposed method for aerospace and robotics engineers based on a nature phenomenon to present an innovative guidance method.
Rocznik
Strony
791--798
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • Aerospace Research Institute, Ministry of Science Research and Technology, Tehran, Iran
Bibliografia
  • 1. Al-Rabayah M., Malaney R., 2012, A new scalable hybrid routing protocol for VANETs, IEEE Transactions on Vehicular Technology, 61, 6, 2625-2635.
  • 2. Babaei A.R., Mortazavi M., Moradi M.H., 2011, Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles, Applied Soft Computing, 11, 1, 365-372.
  • 3. Bernsen J., Manivannan D,, 2012, RIVER: a reliable inter-vehicular routing protocol for vehicular ad hoc networks, Computer Networks, 56, 17, 3795-3807.
  • 4. Bitam S., Mellouk A., Zeadally S., 2013, HyBR: A hybrid bio-inspired bee swarm routing protocol for safety applications in vehicular ad hoc NET works (VANETs), Systems Architecture, 59, 10, 953-967.
  • 5. Chen Y., Jia Z., Ai X., Yang D., Yu J., 2017, A modified two-part wolf pack search algorithm for the multiple traveling salesmen problem, Applied Soft Computing, 61, 714-725.
  • 6. Chen Y., Yu J., Mei Y., Wang Y., Su X., 2016, Modified central force optimization (MCFO) algorithm for 3D UAV path planning, Neurocomputing, 171, C.
  • 7. Eng P., Mejias L., Liu X., Walker R., 2010, Automating human thought processes for a UAV forced landing, Intelligent Robot System, 57, 329.
  • 8. Huang L., Qu H., Ji P., Liu X., Fan Z., 2016, A novel coordinated path planning method using k-degree smoothing for multi-UAVs, Applied Soft Computing, 48, 182-192.
  • 9. Laomettachit T., Termsaithong T., Sae-Tang A., Duangphakdee O., 2015, Decision-making in honeybee swarms based on quality and distance information of candidate nest sites, Journal of Theoretical Biology, 364, 7, 21-30.
  • 10. Liu Y., Zhang X., Guan X., Delahaye D., 2016, Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization, Aerospace Science and Technology, 58.
  • 11. Luo Q., Yang X., Zhou Y.-Q., 2019, Nature-inspired approach: an enhanced moth swarm algorithm for global optimization, Mathematics and Computers in Simulation, 159, 57-92.
  • 12. Ma L., Stepanyan V., Cao C., Faruque I., Woolsey C., Hovakimyan N., 2006, Flight test bed for visual tracking of small UAVs, AIAA Guidance, Navigation, and Control Conference and Exhibit, DOI: 10.2514/6.2006-6609.
  • 13. Mavrovouniotis M., Li C., Yang S., 2017, A survey of swarm intelligence for dynamic optimization: algorithms and applications, Swarm and Evolutionary Computation, 33, 1-17.
  • 14. Mettler B., Dadkhah N., Kong Z., 2010, Agile autonomous guidance using spatial value functions, Control Engineering Practice, 18, 7, 773-788.
  • 15. Paw C.Y., Balas G.J., 2011, Development and application of an integrated framework for small UAV flight control development, Mechatronics, 21, 5, 789-802.
  • 16. Rajasekhar A., Lynn N., Das S., Suganthan P.N., 2017, Computing with the collective intelligence of honey bees – A survey, Swarm and Evolutionary Computation, 32, February, 25-48.
  • 17. Samani M., Tafreshi M., Shafieenejad I., Nikkhah A.A., 2015, Minimum-time open-loop and closed-loop optimal guidance with GA-PSO and neural-fuzzy for Samarai MAV flight, Aerospace and Electronic Systems Magazine, IEEE, 30, 5, 28-37.
  • 18. Torres M., Pelta D.A., Verdegay J.L., Torres J.C., 2016, Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction, Expert Systems with Applications, 55.
  • 19. Zedadra O., Guerrieri A., Jouandeau N., Spezzano G., Seridi H., Fortino G., 2018, Swarm intelligence-based algorithms within IoT-based systems, A Review Journal of Parallel and Distributed Computing, 122.
  • 20. Zhou Y., van Kampen E.J., Chu Q., 2019, Hybrid hierarchical reinforcement learning for online guidance and navigation with partial observability, Neurocomputing, DOI: 10.1016/j.neucom.2018.11.072.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2038828c-d4a9-498a-b80d-583548e82000
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