DEEP LEARNING METHODS FOR DECENTRALIZED DECISION-MAKING IN COUNTERSWARM ENGAGEMENTS
Loading...
Authors
Sharif, Kurdo A.
Subjects
swarm
counter-swarm
drone
robotic systems
autonomous
machine learning
neural network
deep learning
counter-swarm
drone
robotic systems
autonomous
machine learning
neural network
deep learning
Advisors
Kaminer, Isaac I.
Clark, Abram H., IV
Date of Issue
2024-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The adoption of unmanned technologies has spurred multidisciplinary research into robotic swarm systems, especially in military contexts. Inspired by biological swarms' problem-solving abilities, these systems offer the advantage of global behavior emerging from local interactions, reducing reliance on centralized control. Traditional approaches to creating emergent behavior in robotic swarms require predictable and controllable swarms with well-defined local rules and complete knowledge of all agents. In counter-swarm engagements, swarm systems need a global strategy that is robust and adaptable to dynamic environments with minimal reliance on complete knowledge. This research investigates an inverse problem: designing local rules to approximate emergent behavior typically based on perfect knowledge and communication by each drone. The objective is to create decentralized regions where a defender drone utilizes a neural network model trained extensively on simulation data. The data, extracted from engagements involving three attackers and one defender, was organized into various input sets representing different features. Post-training regression analysis identified the feature set that generated optimal defender heading angles compared to an oracle algorithm. The results demonstrated that the neural network model optimizes for shorter engagements more effectively than the oracle, validating the feasibility of employing trained networks in lieu of traditional algorithms.
Type
Thesis
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Distribution Statement A. Approved for public release: Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.