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A Biomimetic Neuronal Network-Based Controller for Guided Helicopter Flight

  • Anthony Westphal
  • Daniel Blustein
  • Joseph Ayers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8064)

Abstract

As part of the Robobee project, we have modified a coaxial helicopter to operate using a discrete time map-based neuronal network for the control of heading, altitude, yaw, and odometry. Two concepts are presented: 1. A model for the integration of sensory data into the neural network. 2. A function for transferring the instantaneous spike frequency of motor neurons to a pulse width modulated signal required to drive motors and other types of actuators. The helicopter is provided with a flight vector and distance to emulate the information conveyed by the honeybee’s waggle dance. This platform allows for the testing of proposed networks for adaptive navigation in an effort to simulate honeybee foraging on a flying robot.

Keywords

robotic flight honeybee neuronal control helicopter 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anthony Westphal
    • 1
  • Daniel Blustein
    • 1
  • Joseph Ayers
    • 1
  1. 1.Depts. of Marine and Environmental Sciences, Biology, and Marine Science CenterNortheastern UniversityNahantUSA

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