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A Sampling Approach to Modeling and Control of a Large-Size Robot Population

[+] Author Affiliations
Dejan Milutinović

University of California, Santa Cruz, Santa Cruz, CA

Devendra P. Garg

Duke University, Durham, NC

Paper No. DSCC2010-4121, pp. 631-637; 7 pages
doi:10.1115/DSCC2010-4121
From:
  • ASME 2010 Dynamic Systems and Control Conference
  • ASME 2010 Dynamic Systems and Control Conference, Volume 2
  • Cambridge, Massachusetts, USA, September 12–15, 2010
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4418-2 | eISBN: 978-0-7918-3884-6
  • Copyright © 2010 by ASME

abstract

Motivated by the close relation between estimation and control problems, we explore the possibility to utilize stochastic sampling for computing the optimal control for a large-size robot population. We assume that the individual robot state is composed of discrete and continuous components, while the population is controlled in a probability space. Utilizing a stochastic process, we can compute the state probability density function evolution, as well as use the stochastic process samples to evaluate the Hamiltonian defining the optimal control. The proposed method is illustrated by an example of centralized optimal control for a large-size robot population.

Copyright © 2010 by ASME

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