Distributed Control of a Swarm of Autonomous Unmanned Aerial Vehicles

Abstract

With the increasing use of Unmanned Aerial Vehicles (UAV)s military operations, there is a growing need to develop new methods of control and navigation for these vehicles. This investigation proposes the use of an adaptive swarming algorithm that utilizes local state information to influence the overall behavior of each individual agent in the swarm based upon the agent's current position in the battlespace. In order to investigate the ability of this algorithm to control UAVs in a cooperative manner, a swarm architecture is developed that allows for on-line modification of basic rules. Adaptation is achieved by using a set of behavior coefficients that define the weight at which each of four basic rules is asserted in an individual based upon local state information. An Evolutionary Strategy (ES) is employed to create initial metrics of behavior coefficients. Using this technique, three distinct emergent swarm behaviors are evolved, and each behavior is investigated in terms of the ability of the adaptive swarming algorithm to achieve the desired emergent behavior by modifying the simple rules of each agent. Finally. each of the three behaviors is analyzed visually using a graphical representation of the simulation, and numerically, using a set of metrics developed for this investigation.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2003
Accession Number
ADA416402

Entities

People

  • James T. Lotspeich

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Airframes
  • Algorithms
  • Collision Avoidance
  • Computer Programming
  • Control Systems
  • Coordinate Systems
  • Evolutionary Algorithms
  • Global Positioning Systems
  • Inertial Navigation
  • Multiagent Systems
  • Navigation
  • Parallel Computing
  • Remotely Piloted Vehicles
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Artificial Intelligence
  • Computational Modeling and Simulation

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control