Summaries - Research
Back Hybrid Centralized and Decentralized Algorithms for Resource Optimization in Stochastic Environments
|Division||Graduate School of Engineering & Applied Science|
|Investigator(s)||Chung, Timothy H.|
|Sponsor||Office of Naval Research (Navy)|
This joint Johns Hopkins University (APL and JHU Department of Applied Mathematics and Statistics) and Naval Postgraduate School (NPS) response focuses on thrust area (1), Resource Optimization, as described in Special Notice 12-SN-0009. The aim of the team is the development, analysis, and application of rigorous methods in deterministic and stochastic optimization that will provide optimal or provably near-optimal solutions to resource-allocation problems of interest to the U. S. Navy. While the focus is on the theme, sensor management and allocation, within thrust area (1), it is expected that at least some of the algorithms and theory being developed will be applicable to other areas of naval interest, including the other theme, maritime mission planning, within thrust area (1), and perhaps to other thrust areas. This is a continuation of RSGB3 (FY13) and RSGVP (FY14).
The development effort proposed herein seeks to advance the design, integration, and live-fly fielding of existing or near-term technologies developed at the Naval Postgraduate School that enable swarm UAS capabilities, specifically in the areas of swarm autonomy and swarm operations. This work is intended to directly inform the systems integration effort for the ONR APIS-U initiative for at-sea demonstration of swarm UAS technologies.
|Publications||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|
|Data||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|