Biologically Inspired Direct Adaptive Guidance and Control for High-Bandwidth Flight Systems

Abstract

The capability of biological flight systems to autonomously maneuver, track, and pursue evasive targets in a cluttered environment is vastly superior to current engineered systems. The reported effort seeks to fully characterize the tracking, guidance and control functions of one such biological system (the male flesh fly in pursuit of the female), and to use the developed understanding to improve the design of engineered guidance and control systems for small autonomous air vehicles. The phase I effort produced demonstration of feasibility in three distinct areas. development and demonstration in simulation of a direct neural network adaptive guidance law for intercept; development and demonstration of experimental methods to capture and quantify the trajectories and orientation of the insects in pursuit/evasion scenarios; and further development and demonstration of practical image processing algorithms for implementation of the developed guidance system concepts. The proposed phase II program will extend and ex and the work in these three areas to produce practical biologically.

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Document Details

Document Type
Technical Report
Publication Date
Jul 15, 2001
Accession Number
ADA415546

Entities

People

  • Allen Tannenbaum
  • Anthony Calise
  • Cole Gilbert
  • Eric Corban

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Airframes
  • Algorithms
  • Collision Avoidance
  • Computational Science
  • Computer Vision
  • Control Systems
  • Guidance
  • Image Processing
  • Navigation
  • Neural Networks
  • Proportional Navigation
  • Systems Biology
  • Three Dimensional
  • Trajectories
  • Two Dimensional
  • Vehicles

Readers

  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms