Stochastic Language-based Motion Control

Abstract

In this work we present an efficient environment representation based on the use of landmarks and language based motion programs. The approach is targeted towards applications involving expansive, imprecisely known terrain without a single global map. To handle the uncertainty inherent in real-world applications a partially-observed controlled Markov chain structure is used in which the state space is the set of landmarks and the control space is a set of motion programs. Using dynamic programming, we derive an optimal controller to maximize the probability of arriving at a desired landmark after a finite number of steps. A simple simulation is presented to illustrate the approach.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA439769

Entities

People

  • Dimitrios Hristu-varsakelis
  • Sean B Andersson

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Autonomous Systems
  • Computer Programming
  • Control Systems
  • Coordinate Systems
  • Differential Equations
  • Equations
  • Formal Languages
  • Language
  • Markov Chains
  • Motion Planning
  • Navigation
  • Probability
  • Robotics
  • Robots
  • Simulations
  • Simulators

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space
  • Space - Spacecraft Maneuvers