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Proceedings Paper

Autonomous UAV search planning with possibilistic inputs
Author(s): Emily Grayson; Paul Elmore; Don Sofge; Fred Petry
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Paper Abstract

Many aspects of decision making processes for autonomous systems involve human subjective information in some form. Methods for informing decision making processes with human information are needed to inform probabilistic information used in an autonomous system. This can provide better decisions and permit a UAV to more quickly and efficiently complete tasks. Specifically we use possibility theory to represent the subjective information and apply possibilistic conditioning of the probability distribution. A simulation platform was developed to evaluate approaches to using possibilistic inputs and showed that is was feasible to make effective usage of such information.

Paper Details

Date Published: 5 May 2017
PDF: 8 pages
Proc. SPIE 10195, Unmanned Systems Technology XIX, 1019508 (5 May 2017); doi: 10.1117/12.2261112
Show Author Affiliations
Emily Grayson, Carnegie Mellon Univ. (United States)
Paul Elmore, U.S. Naval Research Lab. (United States)
Don Sofge, U.S. Naval Research Lab. (United States)
Fred Petry, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 10195:
Unmanned Systems Technology XIX
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Hoa G. Nguyen, Editor(s)

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