The United States Air Force (USAF) April 2019 Science and Technology Strategy report identifies the need for the USAF to “develop and deliver transformational capabilities” while “maintaining the ability to dominate time, space, and complexity in future conflict across all operating domains to project power and defend the homeland.” To achieve this dominance, the report emphasizes operational strategies that increase the speed and complexity of the battle-space by transforming the “current force structure, which emphasizes relatively low numbers of high-value assets,” into one that overwhelms hostile forces by augmenting high-end platforms with larger numbers of inexpensive, low-end systems capable of rapid and effective decision-making. The primary objective of this project is to develop novel solutions to multi-agent learning and control problems by formulating them as output-feedback adaptive optimal control problems. Applications of the proposed learning framework to several problems relevant to the USAF will be pursued. In particular, we will develop novel solutions for decentralized estimation of scalar and vector fields using a team of agents while minimizing exposure to the effects of these fields. We will also utilize the developed framework to optimize agent trajectories to maximize information gain from onboard sensors while managing constraints related to illumination and field of view.

Publications:

Get in touch

rkamalapurkar@ufl.edu
(352) 392-0961