Skip to main content
SpringerLink
Log in

Fuzzy logic, neural-fuzzy network and honey bees algorithm to develop the swarm motion of aerial robots

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

In this study, a novel nature-inspired autonomous motion was investigated using the honey-bee algorithm for aerial robots. The main idea belonged to a novel analogy between optimal honey bees and aerial robots’ motion in proposing autonomous guidance. Three-dimensional simulations for aerial robots were considered to show the efficient performance of autonomous guidance. A new equation system was also developed based on the yaw angle control to simplify dynamic flight calculations. Moreover, different uncertainties such as lateral wind current and navigation’ noise were considered and checked precisely using a neural-fuzzy network to enhance autonomous guidance reliability. Accordingly, aerial robots’ autonomous motions were developed by fuzzy logic to overcome low-quality data linkages between aerial robots. The results of this study illustrated that the integrated nature-inspired guidance by fuzzy logic had a lower total passing and the final time of 24.64% and 21.87% for aerial robots, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  • Al-Rabayah M, Malaney R (2012) A new scalable hybrid routing protocol for VANETs. IEEE Trans Veh Technol 61:2625–2635

    Article  Google Scholar 

  • Babaei AR, Mortazavi M, Moradi MH (2011) Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles. Appl Soft Comput 11:365–372

    Article  Google Scholar 

  • Bernsen J, Manivannan D (2012) RIVER: a reliable inter-vehicular routing protocol for vehicular ad hoc networks. Comput Netw 56:3795–3807

    Article  Google Scholar 

  • Bitam S, Mellouk A, Zeadally S (2013) HyBR: a hybrid bio-inspired bee swarm routing protocol for safety applications in vehicular ad hoc networks (VANETs). J Syst Archit 59:953–967

    Article  Google Scholar 

  • Chen Y, Yu J, Mei Y et al (2016) Modified central force optimization (MCFO) algorithm for 3D UAV path planning. Neurocomputing 171:878–888

    Article  Google Scholar 

  • Chen Y, Jia Z, Ai X et al (2017) A modified two-part wolf pack search algorithm for the multiple traveling salesmen problem. Appl Soft Comput 61:714–725

    Article  Google Scholar 

  • Dadkhah N, Mettler B (2012) Survey of motion planning literature in the presence of uncertainty: considerations for UAV guidance. J Intell Robot Syst 65:233–246

    Article  Google Scholar 

  • Eng P, Mejias L, Liu X, Walker R (2010) Automating human thought processes for a UAV forced landing. J Intell Robot Syst 57:329

    Article  Google Scholar 

  • Fallah-Zazuli M, Vafaeinejad A, Alesheykh AA et al (2019) Mapping landslide susceptibility in the Zagros Mountains, Iran: a comparative study of different data mining models. Earth Sci Inform 12:615–628

    Article  Google Scholar 

  • Goerzen C, Kong Z, Mettler B (2010) A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J Intell Robot Syst 57:65

    Article  Google Scholar 

  • Hoffer NV, Coopmans C, Jensen AM, Chen Y (2014) A survey and categorization of small low-cost unmanned aerial vehicle system identification. J Intell Robot Syst 74:129–145

    Article  Google Scholar 

  • Huang L, Qu H, Ji P et al (2016) A novel coordinated path planning method using k-degree smoothing for multi-UAVs. Appl Soft Comput 48:182–192

    Article  Google Scholar 

  • Lange S, Sunderhauf N, Protzel P (2009) A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments. In: 2009 International conference on advanced robotics, IEEE, pp 1–6

  • 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. J Theor Biol 364:21–30

    Article  MathSciNet  Google Scholar 

  • 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. Aerosp Sci Technol 58:92–102

    Article  Google Scholar 

  • Luo Q, Yang X, Zhou Y (2019) Nature-inspired approach: an enhanced moth swarm algorithm for global optimization. Math Comput Simul 159:57–92

    Article  MathSciNet  Google Scholar 

  • Ma L, Stepanyan V, Cao C et al (2006) Flight test bed for visual tracking of small UAVs. In: AIAA guidance, navigation, and control conference and exhibit, p 6609

  • Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol Comput 33:1–17

    Article  Google Scholar 

  • Mettler B, Dadkhah N, Kong Z (2010) Agile autonomous guidance using spatial value functions. Control Eng Pract 18:773–788

    Article  Google Scholar 

  • Moayedi H, Hayati S (2018a) Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput 66:208–219

    Article  Google Scholar 

  • Moayedi H, Hayati S (2018b) Applicability of a CPT-based neural network solution in predicting load-settlement responses of bored pile. Int J Geomech 18:6018009

    Article  Google Scholar 

  • Moayedi H, Rezaei A (2019) An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput Appl 31:327–336

    Article  Google Scholar 

  • Moayedi H, Tien Bui D, Gör M et al (2019) The feasibility of three prediction techniques of the artificial neural network, adaptive neuro-fuzzy inference system, and hybrid particle swarm optimization for assessing the safety factor of cohesive slopes. ISPRS Int J Geo Inf 8:391

    Article  Google Scholar 

  • Moayedi H, Moatamediyan A, Nguyen H et al (2020) Prediction of ultimate bearing capacity through various novel evolutionary and neural network models. Eng Comput 36:671–687

    Article  Google Scholar 

  • Mosallanezhad M, Moayedi H (2017) Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci 10:479

    Article  Google Scholar 

  • Nguyen H, Mehrabi M, Kalantar B et al (2019) Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping. Geomat Nat Hazards Risk 10:1667–1693

    Article  Google Scholar 

  • Paw YC, Balas GJ (2011) Development and application of an integrated framework for small UAV flight control development. Mechatronics 21:789–802

    Article  Google Scholar 

  • Rajasekhar A, Lynn N, Das S, Suganthan PN (2017) Computing with the collective intelligence of honey bees—a survey. Swarm Evol Comput 32:25–48

    Article  Google Scholar 

  • Samani M, Tafreshi M, Shafieenejad I, Nikkhah AA (2015) Minimum-time open-loop and closed-loop optimal guidance with GA-PSO and neural fuzzy for Samarai MAV flight. IEEE Aerosp Electron Syst Mag 30:28–37

    Article  Google Scholar 

  • Torres M, Pelta DA, Verdegay JL, Torres JC (2016) Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction. Expert Syst Appl 55:441–451

    Article  Google Scholar 

  • Zedadra O, Guerrieri A, Jouandeau N et al (2018) Swarm intelligence-based algorithms within IoT-based systems: a review. J Parallel Distrib Comput 122:173–187

    Article  Google Scholar 

  • Zhou Y, van Kampen E-J, Chu Q (2019) Hybrid hierarchical reinforcement learning for online guidance and navigation with partial observability. Neurocomputing 331:443–457

    Article  Google Scholar 

  • Zhou G, Moayedi H, Bahiraei M, Lyu Z (2020) Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. J Clean Prod 254:120082

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. A & S Research Institute, Ministry of Science Research and Technology, Tehran, Iran

    Iman Shafieenejad & Shervin Zahedi

  2. Faculty of Electrical and Computer Engineering Islamic Azad University, Tehran North Branch, Tehran, Iran

    Elham Dehghan Rouzi

  3. Department of Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Jamshid Sardari & Mohammad Siami Araghi

  4. School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran

    Amirhosein Esmaeili

Authors
  1. Iman Shafieenejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elham Dehghan Rouzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jamshid Sardari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Siami Araghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amirhosein Esmaeili
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shervin Zahedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iman Shafieenejad.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shafieenejad, I., Rouzi, E.D., Sardari, J. et al. Fuzzy logic, neural-fuzzy network and honey bees algorithm to develop the swarm motion of aerial robots. Evolving Systems 13, 319–330 (2022). https://doi.org/10.1007/s12530-021-09391-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-021-09391-4

Search

Navigation