Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

Abstract

Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2004
Accession Number
ADA424283

Entities

People

  • Kevin M. Milam

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Artificial Intelligence
  • Autonomous Navigation
  • Collision Avoidance
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Computers
  • Control Systems
  • Evolutionary Algorithms
  • Experimental Design
  • Information Systems
  • Motion Planning
  • Operating Systems
  • Unmanned Aerial Vehicles

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Operations Research
  • Systems Analysis and Design

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control
  • Autonomy - UAVs
  • Biotechnology