UAV perimeter patrol operations optimization using efficient Dynamic Programming Conference Paper

abstract

• A reduced order Dynamic Programming (DP) method that efficiently computes the optimal policy and value function for a class of controlled Markov chains is developed. We assume that the Markov chains exhibit the property that a subset of the states have a single (default) control action associated with them. Furthermore, we assume that the transition probabilities between the remaining (decision) states can be derived from the original Markov chain specification. Under these assumptions, the suggested reduced order DP method yields significant savings in computation time and also leads to faster convergence to the optimal solution. Most importantly, the reduced order DP has been shown analytically to give the exact same solution that one would obtain via performing DP on the original full state space Markov chain. The method is illustrated via a multi UAV perimeter patrol stochastic optimal control problem. 2011 AACC American Automatic Control Council.

name of conference

• Proceedings of the 2011 American Control Conference

authors

• Darbha, Swaroop

published proceedings

• Proceedings of the 2010 American Control Conference

author list (cited authors)

• Krishnamoorthy, K., Pachter, M., Chandler, P., Casbeer, D., & Darbha, S.

citation count

• 6

complete list of authors

• Krishnamoorthy, K||Pachter, M||Chandler, P||Casbeer, D||Darbha, S

publication date

• January 2011

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... American Control Conference. American Control Conference  Journal

Digital Object Identifier (DOI)

• 10.1109/acc.2011.5990603

International Standard Book Number (ISBN) 13

• 9781457700804

start page

• 462

end page

• 467

URL

• https://doi.org/10.1109/ACC18948.2011