Coevolution of Form and Function in the Design of Micro Air Vehicles

Abstract

This paper discusses approaches to cooperative coevolution of form and function for autonomous vehicles, specifically evolving morphology and control for an autonomous micro air vehicle (MAV). The evolution of a sensor suite with minimal size, weight, and power requirements, and reactive strategies for collision-free navigation for the simulated MAV is described. Results are presented for several different coevolutionary approaches to evolution of form and function (single- and multiple-species models) and for two different control architectures (a rulebase controller based on the SAMUEL learning system and a neural network controller implemented and evolved using ECkit).

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA480642

Entities

People

  • Alan C. Schultz
  • Magdalena D. Bugajska

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Autonomous Vehicles
  • Collision Avoidance
  • Computational Complexity
  • Control Systems
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Learning
  • Micro Air Vehicles
  • Navigation
  • Neural Networks
  • Personal Information Managers
  • Simulators
  • Three Dimensional
  • Vehicles

Readers

  • Calculus or Mathematical Analysis
  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Autonomy - Autonomous System Control