Particle Filters for Real-Time Fault Detection in Planetary Rovers

Abstract

Planetary rovers provide a considerable challenge for artificial intelligence in that they must operate for long periods autonomously, or with relatively little intervention. To achieve this, they need to have on-board fault detection and diagnosis capabilities. Traditional model-based diagnosis techniques are not suitable for rovers due to the tight coupling between the vehicle's performance and its environment. Hybrid diagnosis using particle filters is presented as an alternative, and its strengths and weaknesses are examined. We also present some extensions to particle filters that are designed to make them more suitable for use in diagnosis problems.

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Document Details

Document Type
Technical Report
Publication Date
May 04, 2002
Accession Number
ADP012686

Entities

People

  • Dan Clancy
  • Richard Dearden

Organizations

  • National Aeronautics and Space Administration

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Autonomous Navigation
  • Computations
  • Computer Science
  • Equations
  • Failure Mode And Effect Analysis
  • Filters
  • Gaussian Noise
  • Hybrid Systems
  • Information Processing
  • Information Systems
  • Kalman Filters
  • Particles
  • Probability
  • Probability Distributions
  • Sequential Monte Carlo Methods

Readers

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  • Systems Analysis and Design

Technology Areas

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