Modeling and Control of Large-Scale Adversarial Swarm Engagements

We theoretically and numerically study the problem of optimal control of large-scale autonomous systems under explicitly adversarial conditions, including probabilistic destruction of agents during the simulation. Large-scale autonomous systems often include an adver sarial component, where different agents or groups of agents explicitly compete with one another. An important component of these systems that is not included in current theory or modeling frameworks is random destruction of agents in time. In this case, the modeling and optimal control framework should consider the attrition of agents as well as their position. We propose and test three numerical modeling schemes, where survival probabilities of all agents are smoothly and continuously decreased in time, based on the relative positions of all agents during the simulation. In particular, we apply these schemes to the case of agents defending a high-value unit from an attacking swarm. We show that these models can be successfully used to model this situation, provided that attrition and spatial dynamics are coupled. Our results have relevance to an entire class of adversarial autonomy situations, where the positions of agents and their survival probabilities are both important.ONR SoA programNPS CRUSER progra

COMPUTATIONALLY EFFICIENT ALGORITHMS FOR OPTIMAL MOTION PLANNING AGAINST MULTI-DOMAIN SUPER SWARMS

This thesis develops robust tactics for countering multi-domain super swarms. Previous research has provided tools for assessing adversarial swarms’ internal cooperating strategies, quantifying risk based on swarm and weapons models, and generating optimal defender trajectories. In this research, we develop a simulation-based testbed for experimental validation of these strategies and a database of adversarial swarming models against which to test. In this research, the aforementioned simulation-based testbed is examined from the perspective of computational efficiency. A significant computational advantage is obtained by replacing the Runge Kutta with the Verlet integration scheme frequently used in the molecular dynamics community. This almost equally numerically stable framework offers an impressive performance advantage. Additionally, this research seeks to find the ideal balance between the deterministic nature of the dynamics of adversarial swarms and the requirement for a probabilistic approach in order to model the mutual attrition between opposing agents during combat situations. To achieve this end, several models of dynamics and attrition are introduced for optimal motion planning. Their outcomes are compared with a Monte Carlo simulation model, in which survivability is partially influenced by random number generation that aims to simulate the unpredictability exhibited in the real world.Outstanding ThesisLieutenant, Hellenic NavyApproved for public release. distribution is unlimite

Defense against Adversarial Swarms with Parameter Uncertainty

This paper addresses the problem of optimal defense of a high-value unit (HVU) against a large-scale swarm attack. We discuss multiple models for intra-swarm cooperation strategies and provide a framework for combining these cooperative models with HVU tracking and adversarial interaction forces. We show that the problem of defending against a swarm attack can be cast in the framework of uncertain parameter optimal control. We discuss numerical solution methods, then derive a consistency result for the dual problem of this framework, providing a tool for verifying computational results. We also show that the dual conditions can be computed numerically, providing further computational utility. Finally, we apply these numerical results to derive optimal defender strategies against a 100-agent swarm attack