Unmanned vehicle mission planning given limited sensory information

Hossein Rastgoftar, Ella M. Atkins

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

1 Scopus citations


This paper proposes an approach to optimal planning under uncertainty with limited sensory information for an autonomous unmanned vehicle (UXV). We consider a surveillance application in which identification of environmental targets is impacted by controllable features commanded by the UXV as well as ambient features with dynamics that are uncontrollable. To manage computational complexity, mission states are defined by abstract or discretized features that are maximally influential based on Shannon information content. A receding horizon optimization method is applied to find optimal actions given uncertain and potentially erroneous sensor readings. Ambient feature transition probabilities are learned from empirical data then integrated with controllable features that evolve as a function of UXV actions.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781509059928
StatePublished - Jun 29 2017
Externally publishedYes
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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