Atch 3 Research Topic Area 2 & 17.pdf

DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited. (AFRL-2025-1846)

AFRL/RW University Day Research Topic 2025

RA 2: Bioprincipic Sensors, Information Processing & Control

Research Title: Collective Swarming Resulting from Emerging Behavior by Autonomous Biological Agents

Project Introduction/Description

The Air Force is interested in increased understanding of biological swarming/collective behavior.

Interest focuses on the sensors used to enable emergent behavior in the agents comprising the dynamic swarm, enabling representative modeling of the exemplar animal (agent) used for the study.

“Algorithms” using the identified/assumed sensor suite on the autonomous agents to control the motion of the agents are of interest; relationships among sensor properties (range, resolution, update rate, etc.)

and the structure of the resulting swarm (size, agent density, etc.) are of interest. The resolution of the sensors and the impact of these resolution parameters on individual agent’s motion and structures of swarm are also of interest.

Example: in some species of flies, the males lek for mating purposes. For the selected species of fly, identify the sensors available for the individual flies and show how the sensor suite drives the structure of the lek.

References

1. Gupta et al., (2024), “Mosquitoes integrate visual and acoustic cues to mediate conspecific interactions in swarms,” Current Biology, 34: 1-13.

2. Dublon et al., (2014), “Flying insect swarms,” Current Biology: 24 (18): R828-R830.

3. Grozinger et al., (2014) “From molecules to societies: mechanisms regulating swarming behavior in honeybees (Apis spp.),” Apidologie 45 (3) 327-346.

4. Islam et al., “Pairwise interactions, feedback rule changes, and deliberative decisions underlie honeybee inflight group coordination,” BioRxiv, (submitted to PNAS).

5. Nakata, T., Simoes, P., Walker, S.M., Russell, I.J., and Bomphrey, R.J. (2022). Auditory Sensory

Range of Male Mosquitoes for the Detection of Female Flight Sound. J. R. Soc. Interface 19, 20220285.

6. Nakata, T., Phillips, N., Simoes, P., Russell, I.J., Cheney, J.A., Walker, S.M., and Bomphrey, R.J.

(2020). Aerodynamic Imaging by Mosquitoes Inspires a Surface Detector for Autonomous Flying Vehicles. Science 368, 634-637.

7. Steinberg, Marc, (2011), “Biologically-inspired approaches for self-organization, adaptation, and collaboration of heterogeneous autonomous systems,” SPIE Defense, Security, and Sensing, 2011, Defense Transformation and Net-Centric Systems 2011.

8. Yuce et al., (2013), “Honeybees inspired optimization method: the Bees Algorithm,” Insects, 4, 646- 662.

9. Zada et al., (2024) “Development of neural circuits for social motion perception in schooling fish,” Current Biology 34: 3380-3391.

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)