Adaptive algorithms for autonomous data-ferrying in nonstationary environments

Allan M. Axelrod, Girish V. Chowdhary

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


Unattended ground sensors (UGS) in long-term distributed sensing deployments benefit greatly from the incorporation of unmanned aerial systems (UAS). For instance, the mobility of data-ferrying UAS may be leveraged to reduce the cost of communication between UGS, as well as extend the effective coverage and endurance of the distributed UGS net-work. Since the UAS are also limited in endurance, a UAS may only ferry data between a subset of the UGS during each sortie. This is particularly problematic for extended operations in nonstationary spatio-temporal domains, as the model obtained from the set of UGS may rapidly lose relevance. Moreover, the informativeness of- or the Value-of-Information (VoI) available at each UGS may not be equal. Our approach, termed Exploitation by Informed Exploration between Isolated Operatives (EIEIO), learns a generative spatiotemporal model for the arrival of VoI at each UGS. Through EIEIO, we anticipate and prioritize the subset of UGS with the highest VoI for each data ferrying sortie. Further- more, a lower bound on the requisite sampling time for homogeneous Poisson processes is leveraged to provide a bound on how many times the UAS must visit each UGS in order to learn a spatiotemporal VoI model.

Original languageEnglish (US)
Title of host publicationAIAA Infotech at Aerospace
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Electronic)9781624103384
StatePublished - 2015
Externally publishedYes
EventAIAA Infotech @ Aerospace 2015 - Kissimmee, United States
Duration: Jan 5 2015Jan 9 2015

Publication series

NameAIAA Infotech at Aerospace


OtherAIAA Infotech @ Aerospace 2015
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Aerospace Engineering


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