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.
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.
KEYWORDS: Unmanned aerial vehicles, Data modeling, Sensors, Detection and tracking algorithms, Systems modeling, Robotics, Optimization (mathematics), Performance modeling, Unmanned systems
Advances in unmanned systems enable smaller, less expensive platforms that can be deployed in high numbers or swarms providing superior intelligence and overwhelming effects over a widely dispersed battlefield greatly multiplying their effectiveness. But swarming system remain a laboratory curiosity with only a few live demonstrations of limited scope. Swarms are complex and their behavior is difficult to predict requiring skilled engineers and hand-tuning for each mission. Based on 20 years of experience designing swarms for military missions, the Design of Self-Organizing Adaptive Robotic Swarms (DSOARS) is an engineering environment which addresses the three core challenges of swarm design: (1) decomposing mission tasks into the behaviors of the swarm entities, (2) configuring the size of the swarm to a specific mission, and (3) verifying that the resulting swarm behavior consistently achieves the mission goals with a high level of confidence. DSOARS addresses these challenges through two primary innovations: (1) a means to create verified swarm design patterns that decompose high level mission tasks into individual behaviors and (2) a constructive test environment that simultaneously optimizes and characterizes the swarm performance against a range of possible mission conditions. Users with no swarm expertise can specify the requirements and constraints of their mission and DSOARS will configure a swarm that can meet those objectives with performance guarantees. This paper describes the approach and reports experimental results building and configuring a suite of swarm tactics for an urban mission.
Military planners envision a future of unmanned vehicle swarms that can self-organize to provide superior intelligence and overwhelming effects over a widely dispersed battlefield greatly multiplying the effectiveness of the manned forces. Deploying swarms requires new tactics to take advantage of this capability. Working with military experts we developed a suite of swarm tactics for unmanned air and ground vehicles supporting a full Company with a mission to secure an objective in an urban area. The air vehicles create and maintain perimeter security around the objective. They map the area and maintain tracks on all vehicles and people in the perimeter. They also provide persistent, stealthy communication relay support to all the ground forces. The ground vehicles surveil the key intersections, provide scout and rear security to the squads and scout out alternative routes through the city. The unmanned vehicles execute decoy operations and their behaviors are designed to mask the actual task they are performing through seemingly random movement and regular swapping of tasks. These tactics were implemented in software and evaluated in a 3D model of an urban area taken from a city in the Midwest and implemented in Unity. This paper describes the tactics, algorithms, and experimental setup and reports the results.
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