Autonomous UAV search planning with possibilistic inputs
Emily Grayson, Paul Elmore, Don Sofge, Frederick E. Petry · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
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.