Planning under uncertainty using bayesian nonparametric models

Trevor Campbell, Sameera Ponda, Girish Chowdhary, Jonathan P. How

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The ability to plan actions autonomously to achieve predefined objectives in the presence of environmental uncertainties is critical to the success of many Unmanned Aerial Vehicle missions. One way to plan in the presence of such uncertainties is by learning a model of the environment through Bayesian inference, and using this model to improve the predictive capability of the planning algorithm. Traditional parametric models of the environment, however, can be ineflective if the data cannot be explained using an a priori fixed set of parameters. In Bayesian nonparametric models (BNPs), on the other hand, the number of parameters grows in response to the data. This paper investigates the use of BNPs in the context of planning under uncertainty. Two illustrative planning examples are used to demonstrate that the additional flexibility of BNPs over their parametric counterparts can be leveraged to improve planning performance and to provide the capability to identify and respond to unforeseen anomalous behaviors within the environment.

Original languageEnglish (US)
Title of host publicationAIAA Guidance, Navigation, and Control Conference 2012
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Print)9781600869389
StatePublished - 2012
Externally publishedYes
EventAIAA Guidance, Navigation, and Control Conference 2012 - Minneapolis, MN, United States
Duration: Aug 13 2012Aug 16 2012

Publication series

NameAIAA Guidance, Navigation, and Control Conference 2012


ConferenceAIAA Guidance, Navigation, and Control Conference 2012
Country/TerritoryUnited States
CityMinneapolis, MN

ASJC Scopus subject areas

  • Aerospace Engineering
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Planning under uncertainty using bayesian nonparametric models'. Together they form a unique fingerprint.

Cite this