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Experimental Approaches Toward a Functional Understanding of Insect Flight Control

  • Steven N. FryEmail author
Chapter

Abstract

This chapter describes experimental approaches exploring free-flight control in insects at various levels, in view of the biomimetic design principles they may offer for MAVs. Low-level flight control is addressed with recent studies of the aerodynamics of free-flight control in the fruit fly. The ability to measure instantaneous kinematics and aerodynamic forces in free-flying insects provides a basis for the design of flapping airfoil MAVs. Intermediate-level flight control is addressed by presenting a behavioral system identification approach. In this work, the motion processing and speed control pathways of the fruit fly were reverse engineered based on transient visual flight speed responses, providing a quantitative control model suited for biomimetic implementation. Finally, high-level flight control is addressed with the analysis of landmark-based goal navigation, for which bees combine and adapt basic visuomotor reflexes in a context-dependent way. Adaptive control strategies are also likely suited for MAVs that need to perform in complex and unpredictable environments. The integrative analysis of flight control mechanisms in free-flying insects promises to move beyond isolated emulations of biological subsystems toward a generalized and rigorous approach.

Keywords

Aerodynamic Force Flight Control Flight Speed Free Flight Wing Kinematic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

I wish to thank to reviewers for useful comments, Chauncey Grätzel for advice on the writing, Jan Bartussek, Vasco Medici and Nicola Rohrseitz for useful comments and discussions. The work described in this chapter was funded by the following institutions: Human Frontiers Science Program (HFSP), Swiss Federal Institute of Technology (ETH) Zurich; Swiss National Science Foundation (SNSF), University of Zurich and Volkswagen Foundation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Institute of NeuroinformaticsUniversity of Zurich and ETH Zurich; Institute of Robotics and Intelligent systems, ETH ZurichZurichSwitzerland

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