Probabilistic Planning for Behavior-Based Robots

Abstract

Partially Observable Markov Decision Process models (POMDPs) have been applied to low-level robot control. We show how to use POMDPs differently, namely for sensor planning in the context of behavior-based robot systems. This is possible because solutions of POMDPs can be expressed as policy graphs, which are similar to the finite state automata that behavior-based systems use to sequence their behaviors. An advantage of our system over previous POMDP navigation systems is that it is able to find close-to-optimal plans since it plans at a higher level and thus with smaller state spaces. An advantage of our system over behavior-based systems that need to get programmed by their users is that it can optimize plans during missions and thus deal robustly with probabilistic models that are initially inaccurate.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2001
Accession Number
ADA443594

Entities

People

  • Amin Atrash
  • Sven Koenig

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automata
  • Autonomous Navigation
  • Autonomous Systems
  • Bayesian Networks
  • Dynamic Programming
  • Markov Processes
  • Models
  • Navigation
  • Observation
  • Operations Research
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Robot Navigation
  • Robotics
  • Robots

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Space
  • Space - Spacecraft Maneuvers