Atch 3 REVISED Research Topic Area 17.pdf
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This document is a Research Topic Area (RA 17) from the Air Force Research Laboratory (AFRL/RW) University Day Research program for 2025, focusing on Weapon Autonomy and Control Technology Research. The research seeks computationally efficient, intelligent guidance and control algorithms for autonomous systems, targeting MS or PhD-level researchers in fields like Autonomous Systems and Optimal Control Theory.
The research objectives include developing advanced guidance and control algorithms for complex, unpredictable environments, with emphasis on decentralized approaches, multi-agent collaboration, and resilient system performance. Specific requirements include developing novel algorithms that enable context-aware reasoning, support large heterogeneous teams, and demonstrate computational efficiency. The project involves surveying existing autonomous system behaviors, developing theoretical backgrounds for intelligent algorithms, and verifying performance through numerical simulations. The research is part of the Air Force Defense Research Sciences Program (CFDA 12.800) and is intended to support military and public use applications, with a distribution statement allowing public release.
Atch 3 REVISED Research Topic Area 17
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AFRL/RW University Day Research Topic 2025
RA 17: Weapon Autonomy and Control Technology Research
Research Title: Computationally efficient, intelligent guidance and control algorithms
Academic Area/Field Education Level: Autonomous Systems, Optimal Control Theory, (MS or PhD)
Project Description Introduction:
AFRL/RW is interested in developing and demonstrating intelligent guidance and control (G&C) algorithms that enable novel agent behaviors in complex environments. Computationally efficient algorithms are required to support large, multi-agent, heterogeneous teams and enable networked, collaborative, and autonomous system concepts. The limited communication in complex environments will require decentralized approaches that enable context-aware reasoning, agent collaboration, graceful performance degradation, and flexible or reconfigurable operation. Approaches with a focus on optimization theory, machine-learning methods, and/or multi-agent guidance concepts are highly desired. Finally, resilient solutions that allow for disaggregated system performance (capability spread across multiple agents that coordinate to deliver desired performance) are also sought.
Proposed Research:
The overall objectives of the proposed research are to (1) gain the engineering knowledge needed to understand and address the technical challenges associated with autonomous operations in complex and unpredictable environments, (2) develop intelligent G&C algorithms capable of safely operating in these environments, and (3) demonstrating algorithm performance in a military and/or public use application.
AFRL/RW is looking for approaches that have yet to be considered or examined. An example of a task under this effort would involve surveying existing literature on autonomous system behaviors, developing and documenting the theoretical background for a novel intelligent algorithm, and demonstrating/verifying theoretical performance and computational efficiency guarantees in a relevant environment (i.e., 3DoF/6DoF numerical simulation.) The project could then conclude with future recommendations on maturation paths and additional military/public use applications.
DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited. (AFRL-2025-1846)
| 2025 Uniday Swarming_distroA FINALv2 |
| AFRL/RW University Day Research Topic 2025 |
| RA 2: Bioprincipic Sensors, Information Processing & Control |
| Project Introduction/Description |
| References |
| 2025 Uniday Intelligent GC topic_distroA FINALv2 |
| AFRL/RW University Day Research Topic 2025 |
| RA 17: Weapon Autonomy and Control Technology Research |
| Project Description |
| Proposed Research: |
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