Integrating UAS Flocking Operations with Formation Drag Reduction

Craig Reynolds, in the seminal research into simulated flocking, developed a methodology to guide a flock of birds using three rules: collision avoidance, flock centering, and velocity matching. By modifying these rules, a methodology was created so that each aircraft in a flock maintains a precise position relative to the preceding aircraft. By doing so, each aircraft experiences a decrease in induced aerodynamic drag and increase in fuel efficiency. Flocks of semi-autonomous aircraft present the warfighter with a wide array of capabilities for accomplishing missions more effectively. By introducing formation drag reduction, overall fuel consumption is reduced while range and endurance increase, expanding war planners\u27 options. A simulation was constructed to determine the feasibility of the drag reduction flock in a two-dimensional environment using a drag benefit map constructed from existing research. Due to both agent interaction and wind gust variability, the optimal position for drag reduction presented a severe collision hazard, and drag savings were much more sensitive to lateral (wingtip) position than longitudinal (streamwise) position. By increasing longitudinal spacing, the collision hazard was greatly reduced and a 10-aircraft flock demonstrated a 9.7% reduction in total drag and 14.5% increase in endurance over a mock target

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

Effects of Dynamically Weighting Autonomous Rules in a UAS Flocking Model

Within the U.S. military, senior decision-makers and researchers alike have postulated that vast improvements could be made to current Unmanned Aircraft Systems (UAS) Concepts of Operation through inclusion of autonomous flocking. Myriad methods of implementation and desirable mission sets for this technology have been identified in the literature; however, this thesis posits that specific missions and behaviors are best suited for autonomous military flocking implementations. Adding to Craig Reynolds\u27 basic theory that three naturally observed rules can be used as building blocks for simulating flocking behavior, new rules are proposed and defined in the development of an autonomous flocking UAS model. Simulation validates that missions of military utility can be accomplished in this method through incorporation of dynamic event- and time-based rule weights. Additionally, a methodology is proposed and demonstrated that iteratively improves simulated mission effectiveness. Quantitative analysis is presented on data from 570 simulation runs, which verifies the hypothesis that iterative changes to rule parameters and weights demonstrate significant improvement over baseline performance. For a 36 square mile scenario, results show a 100% increase in finding targets, a 40.2% reduction in time to find a target, a 4.5% increase in area coverage, with a 0% attribution rate due to collisions and near misses

Technology challenges of stealth unmanned combat aerial vehicles

The ever-changing battlefield environment, as well as the emergence of global command and control architectures currently used by armed forces around the globe, requires the use of robust and adaptive technologies integrated into a reliable platform. Unmanned Combat Aerial Vehicles (UCAVs) aim to integrate such advanced technologies while also increasing the tactical capabilities of combat aircraft. This paper provides a summary of the technical and operational design challenges specific to UCAVs, focusing on high-performance, and stealth designs. After a brief historical overview, the main technology demonstrator programmes currently under development are presented. The key technologies affecting UCAV design are identified and discussed. Finally, this paper briefly presents the main issues related to airworthiness, navigation, and ethical concerns behind UAV/UCAV operations

Verification of Autonomous Systems: Developmental Test and Evaluation of an Autonomous UAS Swarming Algorithm Combining Simulation, Formulation and Live Flight

This research was driven by the increase of autonomous systems in the current millennium and the challenging nature of testing and evaluating their performance. A review of the current literature revealed proposed methods for verifying autonomous systems, but few implementations. It exposed several gaps in the current verification and validation methods and suggested goals for filling them. Through the use of modeling, software in the loop (SITL), and flight test, this research verified an autonomous swarming algorithm for unmanned aerial systems (UAS) and validated an exemplar of a testing framework. Thirteen sets of three-vehicle swarm data produced over two days of flight testing provided a baseline algorithm analysis. During these tests, vehicle separation distances deviated an average of 5.61 meters from the ideal state, with separation distance violations \u3c 6:39% of the time. The swarm achieved a 0.27 m average deviation and 0.43% violation in the best cases. Average packet loss between vehicles was 4.94% at a 5 Hz update rate, with an optimal communication lag \u3c 0:04 seconds. The multi-faceted empirical analysis created through the pairing of qualitative and quantitative analysis provided a complete understanding of vehicle behavior. This analysis also identified various areas of improvement for the algorithm and testing framework. The outcomes of this research formed a baseline testing continuum that is adaptable to a variety of follow-on investigations into formal verification of autonomous systems

Implementing Cooperative Behavior & Control Using Open Source Technology Across Heterogeneous Vehicles

This thesis describes the research effort into implementing cooperative behavior and control across heterogeneous vehicles using low cost off-the-shelf technologies and open source software. Current cooperative behavior and control methods are explored and improved upon to build analysis models. These analysis models characterize ideal factor settings for implementation and establish limits of performance for these low cost approaches to cooperative behavior and control. The research focused on latency and position accuracy as the two measures of performance. Three different ground control station (GCS) software applications and two types of vehicles, rover ground vehicles and aerial multi-rotors, were used in this research. Using optimum factor settings from Design of Experiments (DOE), the multi-rotor following rover vehicle configuration experienced almost twice the latency of other experiments but also the lowest positional error of 0.8 m. Results show that the achieved update frequency of 0.5 Hz or slower would be far too slow for close-formation flight

DRONE DELIVERY OF CBNRECy – DEW WEAPONS Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD)

Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD) is our sixth textbook in a series covering the world of UASs and UUVs. Our textbook takes on a whole new purview for UAS / CUAS/ UUV (drones) – how they can be used to deploy Weapons of Mass Destruction and Deception against CBRNE and civilian targets of opportunity. We are concerned with the future use of these inexpensive devices and their availability to maleficent actors. Our work suggests that UASs in air and underwater UUVs will be the future of military and civilian terrorist operations. UAS / UUVs can deliver a huge punch for a low investment and minimize human casualties.https://newprairiepress.org/ebooks/1046/thumbnail.jp

WATER-BASED MITIGATION TECHNIQUES AND NETWORK INTEGRATION TO COUNTER DRONE SWARMS

Potential and current U.S. adversaries are purchasing and deploying commercial small Unmanned Aircraft Systems (sUAS) in networked swarms. These swarms can be used for intelligence collection and reconnaissance, and have the potential to be weaponized as well. Additionally, the unlawful, but probably not malicious, activity of civilian UAS (drone) operators is of increasing concern. More specifically, there is increased risk to naval assets while in constrained environments, such as harbor transit, where both navigation and weaponized responses are serious concerns. This thesis uses the scenario of protecting a U.S. Navy destroyer entering and exiting a harbor to develop a sUAS mitigation procedure based on existing firefighting and counter-piracy technologies. The proposed procedure includes a communications plan and can be implemented almost immediately using existing civilian and military assets. Additional recommendations to improve the performance of such procedures are provided.CRUSARRRTOLieutenant, United States NavyApproved for public release. Distribution is unlimited

Unmanned Vehicle Systems & Operations on Air, Sea, Land

Unmanned Vehicle Systems & Operations On Air, Sea, Land is our fourth textbook in a series covering the world of Unmanned Aircraft Systems (UAS) and Counter Unmanned Aircraft Systems (CUAS). (Nichols R. K., 2018) (Nichols R. K., et al., 2019) (Nichols R. , et al., 2020)The authors have expanded their purview beyond UAS / CUAS systems. Our title shows our concern for growth and unique cyber security unmanned vehicle technology and operations for unmanned vehicles in all theaters: Air, Sea and Land – especially maritime cybersecurity and China proliferation issues. Topics include: Information Advances, Remote ID, and Extreme Persistence ISR; Unmanned Aerial Vehicles & How They Can Augment Mesonet Weather Tower Data Collection; Tour de Drones for the Discerning Palate; Underwater Autonomous Navigation & other UUV Advances; Autonomous Maritime Asymmetric Systems; UUV Integrated Autonomous Missions & Drone Management; Principles of Naval Architecture Applied to UUV’s; Unmanned Logistics Operating Safely and Efficiently Across Multiple Domains; Chinese Advances in Stealth UAV Penetration Path Planning in Combat Environment; UAS, the Fourth Amendment and Privacy; UV & Disinformation / Misinformation Channels; Chinese UAS Proliferation along New Silk Road Sea / Land Routes; Automaton, AI, Law, Ethics, Crossing the Machine – Human Barrier and Maritime Cybersecurity.Unmanned Vehicle Systems are an integral part of the US national critical infrastructure The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. Unmanned Vehicle (UV) Systems & Operations On Air, Sea, Land discusses state-of-the-art technology / issues facing U.S. UV system researchers / designers / manufacturers / testers. We trust our newest look at Unmanned Vehicles in Air, Sea, and Land will enrich our students and readers understanding of the purview of this wonderful technology we call UV.https://newprairiepress.org/ebooks/1035/thumbnail.jp

Intelligent Guidance, Navigation and Control of Multi-Agent UASs with Validation and Verification

Following the exponential growth in the usage of unmanned aerial systems (UASs) across the Aerospace Industry, more intelligent and robust guidance, navigation, and control algorithms are vital to cope with increasing levels of mission complexity. Additionally, many unmanned aerial operations require large payloads and long endurance such as extended reconnaissance, large-scale search and rescue and fine resolution terrain mapping. However, the stringent payload of a single agent or small UASs reduces their overall practicality and effectiveness. My research aims to address these inherent limitations of small UASs with a swarm by holding the required formation in order to distribute tasks and payload among multiple UASs. The goal of this research is to overcome the challenges of operating multi-agent systems by developing phasic navigation and guidance algorithms. Aircraft dynamics and their interactions with surrounding agents are highly nonlinear, which makes autonomous formation flight very sensitive to aircraft initial conditions. The phasic navigation algorithms are proposed and consist of hybrid mathematical approaches: Frenet-Serret curvature control, Hungarian algorithm and moving mesh methods. At the first phase, the curvature control allieviates the sensitivity to initial conditions of multi-agent UASs in unstructured environments by matching agents’ heading angle to the united direction. A variation of Hungarian algorithm is implemented with a moving virtual terminal to assign each agent to the formation position. In the second phase of navigation, the moving mesh methods are applied for holding the formation by defining the outer agents’ position for the boundary condition. The significance of the moving mesh methods is a scalability and a inherent intercollision avoidance. Due to the profound difference between the longitudinal and lateral-directional motion of a fixed-wing aircraft, a multi-scale moving point guidance algorithm has been designed to create the separate virtual reference points in the longitudinal and lateral-direction planes for the first time. This method has been shown to greatly reduce tracking oscillations and improve the overall tracking quality and coherency of the formation. Monte Carlo simulations are performed to ensure the stability and robustness of implementing proposed algorithms through an essentially exhaustive search. A broad range of random initial conditions have been used to validate the effectiveness of guidance, navigation, and control algorithms. Another unique contribution of this work is the validation and verification of proposed algorithms by the hardware-in-the-loop testbed and the numerous flight tests. The hardware-in-the-loop testbed is designed to test the avionics and communication before the flight test by simulating onboard 6-degrees of freedom nonlinear equations of motion. Over one hundred flight tests have been conducted using three distinct aircraft platforms between 2016 and 2018 to validate the fundamental building blocks of this architecture. In summary, this dissertation provides a conceptual and practical foundation for guidance, navigation, and control of multi-agent cooperative/collaborative UASs by unique approaches