Solving Trajectory Optimization Problems by Influence Diagrams

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)

Abstract

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.

Keywords

Influence diagrams Probabilistic graphical models Optimal control theory Brachistochrone problem Goddard problem 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Information Theory and AutomationCzech Academy of SciencesPrague 8Czechia
  2. 2.Faculty of ManagementUniversity of EconomicsJindřichův HradecCzechia

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