Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions
Name
How_Decentralized information-rich.pdf
Size
2.23 MB
Format
Adobe PDF
Checksum (MD5)
7ddbe40dac71760ee4d851261959815d
Author(s)
Ahmed, Nisar
Luders, Brandon Douglas
Sample, Eric
Shah, Danelle
Campbell, Mark
How, Jonathan P.
Ponda, Sameera S.
Date Issued
August 2011
Journal
Proceedings of the AIAA Guidance, Navigation, and Control Conference
Publisher
American Institute of Aeromautics and Astronautics
Citation
Sameera Ponda, Nisar Ahmed, Brandon Luders, Eric Sample, Tauhira Hoossainy, Danelle Shah, Mark Campbell, and Jonathan How. "Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions" AIAA Guidance, Navigation, and Control Conference. August.
Version
Author's final manuscript
Abstract
This paper introduces a novel planning and estimation framework for maximizing infor-
mation collection in missions involving cooperative teams of multiple autonomous vehicles
and human agents, such as those used for multi-target search and tracking. The main
contribution of this work is the scalable uni cation of e ective algorithms for distributed
high-level task planning, decentralized information-based trajectory planning, and hybrid
Bayesian information fusion through a common Gaussian mixture uncertainty representa-
tion, which can accommodate multiple mission objectives and constraints as well as het-
erogeneous human/robot information sources. The proposed framework is validated with
promising results on real hardware through a set of experiments involving a human-robot
team performing a multi-target search mission.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/6.2011-6238
