TY - GEN
T1 - Application of bio-inspired sensing, perception and control technology to uxv autonomous missions
AU - Jackson, Joseph A.
AU - Diel, David D.
AU - Boskovic, Jovan D.
AU - Pratt, Stephen C.
AU - Charbonneau, Daniel
N1 - Funding Information:
This work was supported by DARPA under Contract Number W31P4Q-18-C-0035
Publisher Copyright:
© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - Biological swarms utilize simple behaviors spread over many agents to protect and promote the health of the colony. Recent interest in distributing tasks across swarms of autonomous agents has exposed the complexity of communications, task assignment, distributed sensing, and mission replanning for large teams of robotic agents. In this paper, we describe the approach we have taken to characterize and model the individual and collaborative behaviors used by Temnothorax rugatulus ants amid competitive nest selection scenarios. This species of ants exhibit favorable characteristics self-organizing without need for centralized planning, control, or communication. This work focuses on learning behavior rules from these ants to help design responses to swarm-based combat operations. This research effort consisted of three main parts: data generation, behavior characterization, and machine learning for UAV applications.
AB - Biological swarms utilize simple behaviors spread over many agents to protect and promote the health of the colony. Recent interest in distributing tasks across swarms of autonomous agents has exposed the complexity of communications, task assignment, distributed sensing, and mission replanning for large teams of robotic agents. In this paper, we describe the approach we have taken to characterize and model the individual and collaborative behaviors used by Temnothorax rugatulus ants amid competitive nest selection scenarios. This species of ants exhibit favorable characteristics self-organizing without need for centralized planning, control, or communication. This work focuses on learning behavior rules from these ants to help design responses to swarm-based combat operations. This research effort consisted of three main parts: data generation, behavior characterization, and machine learning for UAV applications.
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M3 - Conference contribution
AN - SCOPUS:85100306419
SN - 9781624106095
T3 - AIAA Scitech 2021 Forum
SP - 1
EP - 30
BT - AIAA Scitech 2021 Forum
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
Y2 - 11 January 2021 through 15 January 2021
ER -