Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation.

Abstract

Navigation through obstacles such as mine fields is an important capability for autonomous underwater vehicles. One way to produce robust behavior is to perform projective planning. However, real-time performance is a critical requirement in navigation. What is needed for a truly autonomous vehicle are robust reactive rules that perform well in a wide variety of situations, and that also achieve real-time performance. In this work, SAMUEL, a learning system based on genetic algorithms, is used to learn high-performance reactive strategies for navigation and collision avoidance. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA294053

Entities

People

  • Alan C. Schultz

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Autonomous Underwater Vehicles
  • Autonomous Vehicles
  • Collision Avoidance
  • Computer Science
  • Genetic Algorithms
  • Genetics
  • Language
  • Machine Learning
  • Navigation
  • Population Genetics
  • Random Walk
  • Simulations
  • Systems Engineering
  • Test And Evaluation
  • Underwater Vehicles

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Autonomy
  • Biotechnology