TY - GEN
T1 - Adaptive algorithms for autonomous data-ferrying in nonstationary environments
AU - Axelrod, Allan M.
AU - Chowdhary, Girish V.
N1 - Publisher Copyright:
© 2015, American Institute of Aeronautics and Astronautics Inc. All rights received.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085850027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085850027&partnerID=8YFLogxK
U2 - 10.2514/6.2015-0117
DO - 10.2514/6.2015-0117
M3 - Conference contribution
AN - SCOPUS:85085850027
T3 - AIAA Infotech at Aerospace
BT - AIAA Infotech at Aerospace
PB - American Institute of Aeronautics and Astronautics Inc.
T2 - AIAA Infotech @ Aerospace 2015
Y2 - 5 January 2015 through 9 January 2015
ER -