An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to this day, most swarm development efforts have gone untested and thus unimplemented. Cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools are some of the major inhibitors to successful swarm implementation. This thesis examines how the OpenEaagles simulation framework can be extended to bridge this gap. This research aims to utilize Hardware-in-the-Loop (HIL) simulation to provide developers a functional capability to develop and test the behaviors of scalable and modular swarms of autonomous UAVs in simulation with high confidence that these behaviors will prop- agate to real/live ight tests. Demonstrations show the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, pro- vides realistic aircraft modeling, and is capable of simultaneously accommodating four hardware-piloted swarming UAVs during HIL simulation or 64 swarming UAVs during pure simulation

Defensive swarm: an agent-based modeling analysis

Security at remote military bases is a difficult, yet critical, mission. Remote locations are generally closer to enemy combatants and farther from supporting forces; the individuals charged with defending the bases do so with less equipment. These locations are also usually reliant on air-resupply missions to maintain mission readiness and effectiveness. This thesis analyzes how swarms of small autonomous unmanned aerial vehicles (UAVs) could assist in defensive operations. To accomplish this, I created an agent-based computer simulation model, which creates a tactical problem (enemies attempting to attack or infiltrate a notional base) that a swarm of UAVs attempts to defend against. Results indicate that a swarm can effectively deter 95% of attackers if each UAV is responsible for covering no more than 0.18 square miles and at least 40% of the UAVs are armed. I conclude that UAVs are an excellent addition to base defense and are particularly helpful at remote outposts with less organic capability (limited field of view, defensive assets, etc.). While this research deals specifically with countering a threat to a central base, the algorithms for swarm dynamics could be applied to future problems in mobile convoy or aircraft defense, and even peacetime applications like search and rescue.http://archive.org/details/defensiveswarmng1094556777Major, United States Air ForceApproved for public release; distribution is unlimited

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

Virtual environment UAV swarm management using GPU calculated digital pheromones

Our future military force will be complex: a highly integrated mix of manned and unmanned units. These unmanned units could function individually or within a swarm. The readiness of future warfighters to work alongside and utilize these new forces depends on the creation of usable interfaces and training simulators. The difficulty is that current unmanned aerial vehicle (UAV) control interfaces require too much operator attention and common swarm control methods require expensive computational power. This dissertation discusses how to improve upon current user interfaces and how to improve the performance of a common swarm control method, the digital pheromone field. This method uses digital pheromones to bias the movements of individual units within a swarm toward areas that are attractive and away from areas that are dangerous or unattractive. A more efficient method for performing pheromone field calculations is introduced, one that harnesses the power of the GPU (graphics processing unit) in today\u27s graphics cards by reshaping the ADAPTIV swarm control algorithm into a form acceptable to the GPU\u27s pipeline. The GPU ADAPTIV implementation is tested in scenarios that involve up to 50,000 virtual UAVs. When compared to its counterpart CPU implementation, the GPU version performed over 30 times faster than the CPU version. This gain translates directly into lower costs for training the future warfighter today and fielding the swarms of tomorrow. Finally, this dissertation presents a vision for combining these new interface ideas and performance enhancements into an effective swarm control interface and training simulator

Swarm Based Implementation of a Virtual Distributed Database System in a Sensor Network

The deployment of unmanned aerial vehicles (UAVs) in recent military operations has had success in carrying out surveillance and combat missions in sensitive areas. An area of intense research on UAVs has been on controlling a group of small-sized UAVs to carry out reconnaissance missions normally undertaken by large UAVs such as Predator or Global Hawk. A control strategy for coordinating the UAV movements of such a group of UAVs adopts the bio-inspired swarm model to produce autonomous group behavior. This research proposes establishing a distributed database system on a group of swarming UAVs, providing for data storage during a reconnaissance mission. A distributed database system model is simulated treating each UAV as a distributed database site connected by a wireless network. In this model, each UAV carries a sensor and communicates to a command center when queried. Drawing equivalence to a sensor network, the network of UAVs poses as a dynamic ad-hoc sensor network. The distributed database system based on a swarm of UAVs is tested against a set of reconnaissance test suites with respect to evaluating system performance. The design of experiments focuses on the effects of varying the query input and types of swarming UAVs on overall system performance. The results show that the topology of the UAVs has a distinct impact on the output of the sensor database. The experiments measuring system delays also confirm the expectation that in a distributed system, inter-node communication costs outweigh processing costs

Architectural Considerations for Single Operator Management of Multiple Unmanned Aerial Vehicles

Recently, small Unmanned Aircraft Systems (UAS) have become ubiquitous in military battlefield operations due to their intelligence collection capabilities. However, these unmanned systems consistently demonstrate limitations and shortfalls with respect to size, weight, range, line of sight and information management. The United States Air Force Unmanned Aircraft Systems Flight Plan 2009-2047 describes an action plan for improved UAS employment which calls out single operator, multi-vehicle mission configurations. This thesis analyzes the information architecture using future concepts of operations, such as biologically-inspired flocking mechanisms. The analysis and empirical results present insight into the engineering of single-operator multiple-vehicle architectures

A Model for Geographically Distributed Combat Interactions of Swarming

17 USC 105 interim-entered record; under review.This article describes the Distributed Interaction Campaign Model (DICM), an exploratory campaign analysis tool and asset allocation decision-aid for managing geographically distributed and swarming naval and air forces. The model is capable of fast operation, while accounting for uncertainty in an opponent’s plan. It is intended for use by commanders and analysts who have limited time for model runs, or a finite budget. The model is purpose-built for the Pentagon’s Office of Net Assessment, and supports analysis of the following questions: What happens when swarms of geographically distributed naval and air forces engage each other and what are the key elements of the opponents’ force to attack? Are there changes to force structure that make a force more effective, and what impacts will disruptions in enemy command and control and wide-area surveillance have? Which insights are to be gained by fast exploratory mathematical/computational campaign analysis to augment and replace expensive and time-consuming simulations? An illustrative example of model use is described in a simple test scenario.Identified in text as U.S. Government work

Design and Test of a UAV Swarm Architecture over a Mesh Ad-Hoc Network

The purpose of this research was to develop a testable swarm architecture such that the swarm of UAVs collaborate as a team rather than acting as several independent vehicles. Commercial-off-the-shelf (COTS) components were used as they were low-cost, readily available, and previously proven to work with at least two networked UAVs. Initial testing was performed via software-in-the-loop (SITL) demonstrating swarming of three simulated multirotor aircraft, then transitioned to real hardware. The architecture was then tested in an outdoor nylon netting enclosure. Command and control (C2) was provided by software implementing an enhanced version of Reynolds’ flocking rules via an onboard companion computer, and UDP multicast messages over a W-Fi mesh ad-hoc network. Experimental results indicate a standard deviation between vehicles of two meters or less, at airspeeds up to two meters per second. This aligns with navigation instrumentation error, permitting safe operation of multiple vehicles within five meters of each other. Qualitative observations indicate this architecture is robust enough to handle more aircraft, pass additional sensor data, and incorporate different swarming algorithms and missions