Defense against Adversarial Swarms with Parameter Uncertainty.

Area of Science:

• Control Theory
• Robotics
• Artificial Intelligence

Background:

• Large-scale swarm attacks pose a significant threat to high-value units (HVUs).
• Understanding and modeling intra-swarm cooperation is crucial for effective defense.
• Existing defense strategies may not adequately address the complexities of coordinated swarm behavior.

Purpose of the Study:

• To develop a framework for optimal defense against large-scale swarm attacks.
• To integrate swarm cooperation models with high-value unit tracking and adversarial dynamics.
• To derive and verify computational methods for determining optimal defender strategies.

Main Methods:

• Modeling intra-swarm cooperation strategies.
• Formulating the defense problem within uncertain parameter optimal control.
• Developing and applying numerical solution methods for the primal and dual problems.
• Deriving consistency results for the dual problem to aid in verification.

Main Results:

• The swarm attack defense problem is successfully cast within uncertain parameter optimal control.
• Numerical methods for solving the dual problem are established, enhancing computational utility and verification.
• Optimal defender strategies were derived for a simulated 100-agent swarm attack.

Conclusions:

• The proposed framework provides a robust approach to defending HVUs against coordinated swarm attacks.
• Numerical solutions for the dual problem offer valuable tools for validating computational results in optimal control.
• The derived strategies demonstrate the potential for effective defense against complex, large-scale adversarial swarms.