Stochastic Model-Based Control of Multi-Robot Systems

Abstract

In this report we consider control of single- and multi-robot systems as an optimal control problem. Solution of this problem may be of enormous complexity because of a large-number of robots, a large number of redundant states, and environment uncertainties. Motivated by estimation methods based on statistical sampling employed for solving complex estimation problems, we explore the possibility of using stochastic process samples for computing the optimal control. This approach can ultimately provide small-size, low-cost and efficient computational hardware for solving complex multi-robot control problems and in which computations are driven by laws of statistical physics.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 2009
Accession Number
ADA520667

Entities

People

  • Dejan Milutinović
  • Devendra P. Garg

Organizations

  • Duke University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Cells
  • Computational Fluid Dynamics
  • Computational Science
  • Computations
  • Data Analysis
  • Differential Equations
  • Equations
  • Materials Science
  • Partial Differential Equations
  • Probability
  • Probability Density Functions
  • Random Variables
  • Random Walk
  • Simulations
  • Statistical Sampling
  • Stochastic Processes

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Operations Research
  • Regression Analysis.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control