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Modeling and control of biologically inspired flying robots

Published online by Cambridge University Press:  27 April 2011

Micael S. Couceiro ,
J. Miguel A. Luz ,
Carlos M. Figueiredo  and
N. M. Fonseca Ferreira
Micael S. Couceiro*
Affiliation:
Institute of Systems and Robotics, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal E-mail: [email protected]
J. Miguel A. Luz
Affiliation:
Department of Electrotechnics Engineering, Coimbra Institute of Engineering Rua Pedro Nunes, 3031-601 Coimbra, Portugal E-mails: [email protected], [email protected], [email protected]
Carlos M. Figueiredo
Affiliation:
Department of Electrotechnics Engineering, Coimbra Institute of Engineering Rua Pedro Nunes, 3031-601 Coimbra, Portugal E-mails: [email protected], [email protected], [email protected]
N. M. Fonseca Ferreira
Affiliation:
Department of Electrotechnics Engineering, Coimbra Institute of Engineering Rua Pedro Nunes, 3031-601 Coimbra, Portugal E-mails: [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper covers a wide knowledge of physical and dynamical models useful for building flying robots and a new generation of flying platform developed in the similarity of flying animals. The goal of this work is to develop a simulation environment and dynamic control using the high-level calculation tool MatLab and the modeling, simulation, and analysis of dynamic systems tool Simulink. Once created the dynamic models to study, this work involves the study and understanding of the dynamic stability criteria to be adopted and their potential use in the control of flying models.

Keywords

AerodynamicsControlRoboticsSeagullDragonfly
Type
Articles
Information
Robotica , Volume 30 , Issue 1 , January 2012 , pp. 107 - 121
Copyright
Copyright © Cambridge University Press 2011

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References

1.Alrashidi, M. R. and El-Hawary, M. E., “A survey of particle swarm optimization applications in power system operations,” Electr. Power Compon Syst. 34 (12), 1349–1357 (2006).CrossRefGoogle Scholar
2.Baudinette, R. V. and Schmidt-Nielsen, K., “Energy cost of gliding flight in herring gulls,” Nature 248, 83–84 (1974).CrossRefGoogle Scholar
3.Cianchi, M., Leonardo da Vinci's Machines (Becocci Editore, Florence, 1988) ISBN 8882000036.Google Scholar
4.Cohen, Y. B. and Breazeal, C., Biologically-Inspired Intelligente Robots (SPIE Press, Bellingham, WA, USA, 2003) vol. PM122.CrossRefGoogle Scholar
5.Colozza, A., “Fly like a bird,” IEEE Spectr. 44 (5), 38–43 (2007).CrossRefGoogle Scholar
6.Couceiro, M. S., Figueiredo, C. M., Ferreira, N. M. F. and Machado, J. A. T., “Biological Inspired Flying Robot,” Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE '09), San Diego (Aug. 30–Sep. 2, 2009).Google Scholar
7.Couceiro, M. S., Figueiredo, C. M., Ferreira, N. M. F. and Machado, J. A. T., “The Dynamic Modeling of a Bird Robot,” Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions - Robotica, Castelo Branco, Portugal (May 7, 2009).Google Scholar
8.Couceiro, M. S., Mendes, R. M., Ferreira, N. M. F. and Machado, J. A. T., “Control Optimization of a Robotic Bird,” Proceedings of the European Workshop on Movement Science (EWOMS '09), Lisbon, Portugal (Jun. 4–6, 2009).Google Scholar
9.Denavit, J. and Hartenberg, R. S., “A kinematic notation for lower-pair mechanisms based on matrices,” Trans. ASME J. Appl. Mech. 23, 215–221 (1955).CrossRefGoogle Scholar
10.Dial, K. P., Randall, R. J. and Dial, T. R., “BioScience - What use is half a wing in the ecology and evolution of birds,” BioScience 56 (5), 437–445 (2006).CrossRefGoogle Scholar
11.Duncan, J., Manitoba Dragonfly Survey: Basic Biology, NatureNorth.com (1999).Google Scholar
12.Ellison, A., “Cybird,” In: Product Review in FlyingToys, Manufactured by Skytech International and distributed by FlyingToys (2006) pp. 92–94.Google Scholar
13.Etkin, B. and Reid, L. D.Dynamics of Flight: Stability and Control, 3rd ed. (John Wiley & Sons, New York, USA, 1995) 400 pp., ISBN: 978-0-471-03418-6.Google Scholar
14.Hamill, J. and Knutzen, K. M., Biomechanical Basics of Human Movement, 3rd ed. (Lippincott, Williams and Wilkins, Baltimore, MD, USA, 2009).Google Scholar
15.Hu, H., Gopa-Kumar, A., Abate, G. and Albertani, R., An Experimental Study of Flexible Membrane Wings in Flapping Flight (Aerospace Science and Technology, 2010).Google Scholar
16.Kennedy, J. and Eberhard, R. C., “Particle Swarm Optimization,” Proceedings of the IEEE International Conference on Neural Networks (1995) pp. 1942–1948.Google Scholar
17.Kesel, Antonia B., “Aerodynamic characteristics of dragonfly wing sections compared with technical aerofoils,” J. Exp. Biol. 203 (20), 3125–3135 (2000).CrossRefGoogle ScholarPubMed
18.Kulemin, G. P., Millimeter-Wave Radar Targets and Clutter (Artech House, Norwood, 2003) pp. 58–65.Google Scholar
19.Lauder, G. V., Flight of the Robofly (Nature, Macmillan Publishing Ltd., 2001) pp. 688–689, ISSN 0028-0836.Google ScholarPubMed
20.Liang, J., Chen, Y. Q. and Fullmer, R., “Simulation Studies on the Boundary Stabilization and Disturbance Rejection for Fractional Diffusion-Wave Equation,” Proceedings of the 2004 American Control Conference, Boston, Massachusetts (2004) pp. 5010–5015.Google Scholar
21.Mehra, R. D., Kessel, W. C. and Carrol, J. V., Global stability and control analysis of aircraft at high angles-of-attack, Annual Technical Reports 1/2/3, ONR-CR215-(1/2/3), Scientific Systems Inc., USA (1977).Google Scholar
22.Monje, C. A., Vinagre, B. M., Chen, Y. Q., Feliu, V., Lanusse, P. and Sabatier, J., “Proposals for Fractional PIλDμ Tuning,” Proceedings of the First IFAC Workshop on Fractional Differentiation and Its Application Bordeaux, Francepp (2004) pp. 156–161.Google Scholar
23.Parslew, B., Low Order Modelling of Flapping Wing Aerodynamics for Real-Time Model Based Animation of Flapping Flight M.Sc. Thesis (Manchester, UK: School of Mathematics, University of Manchester, 2005).Google Scholar
24.Pennycuick, C. J., “Wingbeat frequency of birds in steady cruising flight: New data and improved predictions,” J. Exp. Biol. 199, 1613–1618 (1996), printed in Great Britain, The Company of Biologists Limited.Google ScholarPubMed
25.Pennycuick, C. J., “Speeds and wingbeat frequencies of migrating birds compared with calculated benchmarks”. J. Exp. Biol. 204, 3283–3294 (2001), printed in Great Britain, The Company of Biologists Limited.CrossRefGoogle ScholarPubMed
26.Pires, E. J. S., Oliveira, P. B. M., Machado, J. A. T. and Cunha, J. B., “Particle Swarm Optimization versus Genetic Algorithm in Manipulator Trajectory Planning,” Proceedings of the 7th Portuguese Conference on Automatic Control (Sep. 11–13, 2006).Google Scholar
27.Schenato, L., Deng, X., Wu, W. C. and Sastry, S., “Virtual Insect Flight Simulator (VIFS): A Software Testbed for Insect Flight,” Proceedings of the IEEE International Conference Robotics and Automation, Seoul, Korea (2001).Google Scholar
28.Snyman, J. A., An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms (Springer, New York, USA, 2005), ISBN 038729824X.Google Scholar
29.Spranklin, B. W., Design, Analysis, and Fabrication of a Snake-Inspired Robot with a Rectilinear Gait M.S. Thesis (College Park: University of Maryland, 2006) 218 pp., AAT 1436363.Google Scholar
30.Stevens, B. L. and Lewis, F. L.Aircraft Control and Simulation, 2nd ed. (John Wiley & Sons, New York, USA, 2003) 680 pp., ISBN: 978-0-471-37145-8.Google Scholar
31.Tamai, M., Wang, Z., Rajagopalan, G. and Hu, H., “Aerodynamic Performance of a Corrugated Dragonfly Airfoil Compared with Smooth Airfoils at Low Reynolds Numbers,” Proceedings of the 45th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada (2007) pp. 1–12.Google Scholar
32.Tanaka, H., Hoshino, K., Matsumoto, K. and Shimoyama, I., “Flight Dynamics of a Butterfly-Type Ornithopter,” Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '05), Edmonton, Alberta, Canada (Aug. 2–6, 2005) pp. 310–315.CrossRefGoogle Scholar
33.Tang, J., Zhu, J. and Sun, Z., “A Novel Path Panning Approach Based on Appart and Particle Swarm Optimization,” Proceedings of the 2nd International Symposium on Neural Networks, LNCS 3498 (2005) pp. 253–258.Google Scholar
34.Tong, J. and Schwab, A., The Flight of Birds (MIT Press, Cambridge, MA, USA, 1997).Google Scholar
35.Vallidis, N., A Hexapod Robot and Novel Training Approach for Artificial Neural Networks, CiteSeer (2008).Google Scholar
36.Van, C.Berg, Den and Rayner, J. M. V., “The moment of inertia of bird wings and the inertial power requirement for flapping flight,” J. Exp. Biol. 198 (8), 1655–1664 (1995).Google Scholar
37.Wakeling, J. M. and Ellington, C. P., “Dragonfly flight II. Velocities, accelerations, and kinematics of flapping flight,” J. Exp. Biol. 200, 557–582 (1997).CrossRefGoogle ScholarPubMed
38.Wang, Z. J., “Dissecting Insect Flight,” Annu. Rev. Fluid Mech. 37, 183–210 (2005).CrossRefGoogle Scholar
39.Zhu, C., Muraoka, K., Kawabata, T., Cao, C., Fujimoto, T. and Chiba, N.Real-Time Animation of Bird Flight Based on Aerodynamics (Institute of Technology, Iwate University, 2006).CrossRefGoogle Scholar
40.Zuo, D., Peng, S., Chen, W. and Zhang, W., “Numerical simulation of flapping-wing insect hovering flight at unsteady flow” Int. J. Numer. Methods in Fluids 53, 1801–1817 (2007).Google Scholar