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.
REFERENCES(20)
1.
Al-Rabayah M., Malaney R., 2012, A new scalable hybrid routing protocol for VANETs, IEEE Transactions on Vehicular Technology, 61, 6, 2625-2635.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.