Solving Trajectory Optimization Problems by Influence Diagrams

0477182 - ÚTIA 2018 RIV CH eng C - Conference Paper (international conference)
Vomlel, Jiří - Kratochvíl, Václav
Solving Trajectory Optimization Problems by Influence Diagrams.
Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Cham: Springer, 2017 - (Antonucci, A.; Cholvy, L.; Papini, O.), s. 146-155. Lecture Notes in Computer Science, 10369. ISBN 978-3-319-61580-6.
[ECSQARU: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty. Lugano (CH), 10.07.2017-14.07.2017]
R&D Projects: GA ČR(CZ) GA16-12010S; GA ČR GA17-08182S
Institutional support: RVO:67985556
Keywords : Influence diagrams * Probabilistic graphical models * Optimal control theory * Brachistochrone problem * Goddard problem
OECD category: Automation and control systems
http://library.utia.cas.cz/separaty/2017/MTR/vomlel-0477182.pdf

Influence diagrams are decision-theoretic extensions of Bayesian networks. In this paper we show how influence diagrams can be used to solve trajectory optimization problems. These problems are traditionally solved by methods of optimal control theory but influence diagrams offer an alternative that brings benefits over the traditional approaches. We describe how a trajectory optimization problem can be represented as an influence diagram. We illustrate our approach on two well-known trajectory optimization problems – the Brachistochrone Problem and the Goddard Problem. We present results of numerical experiments on these two problems, compare influence diagrams with optimal control methods, and discuss the benefits of influence diagrams.
Permanent Link: http://hdl.handle.net/11104/0273650