KEYWORDS: Systems modeling, Modeling and simulation, Artificial intelligence, Machine learning, Intelligence systems, System identification, New and emerging technologies, Modeling, Computing systems, Clouds
Today’s battlefield increasingly incorporates emerging technologies using artificial intelligence. These systems not only provide unparalleled speed and accuracy, but also allow for digital models to be developed and tested in simulation prior to deployment, reducing the time and cost of acquisition. This holds additional promise for wargaming modeling and simulation for understanding the impact of complex, multi-domain operations on future force efficacy and structure. However, current modeling and simulation environments are not designed for simulating decentralized, intelligent systems at scale. Cloud computing has revolutionized how we scale computational capability, but was not designed for complex, low latency interactions between independently reasoning entities. This motivates new methods for characterizing and mitigating complexity to meet operational and mission requirements. We outline the challenges and opportunities for modeling and simulating large-scale multi-agent systems and identify future research areas that should address these challenges. We recommend that investment be placed in holistically understanding scalability from a cost-benefit perspective, measuring the impact on requirements, developing improved tools for understanding the dimensions of scalability, and formalizing specifications of the scalability requirements met (or not met) by available systems. We propose that a framework for reasoning over and adjusting the fidelity of various models within a system of systems is needed to meet development and testing requirements. Formal methods can be used to understand the limits on scalability as a function of objectives (e.g. speed, convergence, performance) and constraints (e.g. cost, compute, and time), optimizing resources to develop and test interacting artificial intelligence systems at scale.
Swarms of inexpensive, robotic sensors have the potential for revolutionizing intelligence gathering. They self-organize to provide wide apertures, redundancy, attritability, with low probability of detect over a wide area. Coordinating swarm behaviors to provide the necessary apertures and spatial configurations requires novel methods of distributed control that can maintain the positioning accuracy in the face of arbitrary threats and obstacles. In this paper we describe the algorithms to control a swarm of air vehicles with radio frequency receivers that cooperatively search an urban area for radio frequency emitters, self-organize into teams to localize each emitter, and perform coordinated maneuvers to maximize the information gain during the localization operation. The swarm is able to adapt to attrition, performs collision avoidance, and adjusts its trajectories based on the urban terrain. These behaviors were implemented in a ROS-based swarm deployment environment suitable for execution on a small drone and simulated in a 3D model of a small urban area. This paper describes the search and localization tactics employed, the algorithms for implementing those tactics in the swarm, and experimental results. Our companion paper describes the algorithms used for localization.
Swarm technology provides a new opportunity for sensor systems in which the movement of the agents can be leveraged to enhance the joint capacity of the sensors and subsequent signal processing algorithms. In the Localizing Urban Swarm Technology (LocUST) project for DARPA's OFFensive Swarm-Enabled Tactics (OFFSET) program, the authors developed a complex fading model for the virtual radio frequency (RF) environment, decentralized search and localization tasking, and movement-enhanced time difference of arrival (TDOA) localization. The system was also implemented in hardware using Bluetooth 5.0 modules. This paper reports on the fading model, localization algorithm, and hardware testing results, with the companion paper reporting on the swarm coordination, localization-enhancing movement, and associated experimentation.
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