Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) 2019 Annual Report

Prepared for: Dr. Brian Bingham, CRUSER DirectorThe Naval Postgraduate School (NPS) Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) provides a collaborative environment and community of interest for the advancement of unmanned systems (UxS) education and research endeavors across the Navy (USN), Marine Corps (USMC) and Department of Defense (DoD). CRUSER is a Secretary of the Navy (SECNAV) initiative to build an inclusive community of interest on the application of unmanned systems (UxS) in military and naval operations. This 2019 annual report summarizes CRUSER activities in its eighth year of operations and highlights future plans.Deputy Undersecretary of the Navy PPOIOffice of Naval Research (ONR)Approved for public release; distribution is unlimited

Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) 2019 Annual Report

Prepared for: Dr. Brian Bingham, CRUSER DirectorThe Naval Postgraduate School (NPS) Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) provides a collaborative environment and community of interest for the advancement of unmanned systems (UxS) education and research endeavors across the Navy (USN), Marine Corps (USMC) and Department of Defense (DoD). CRUSER is a Secretary of the Navy (SECNAV) initiative to build an inclusive community of interest on the application of unmanned systems (UxS) in military and naval operations. This 2019 annual report summarizes CRUSER activities in its eighth year of operations and highlights future plans.Deputy Undersecretary of the Navy PPOIOffice of Naval Research (ONR)Approved for public release; distribution is unlimited

Motion Planning of UAV Swarm: Recent Challenges and Approaches

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work

Counter Unmanned Aircraft Systems Technologies and Operations

As the quarter-century mark in the 21st Century nears, new aviation-related equipment has come to the forefront, both to help us and to haunt us. (Coutu, 2020) This is particularly the case with unmanned aerial vehicles (UAVs). These vehicles have grown in popularity and accessible to everyone. Of different shapes and sizes, they are widely available for purchase at relatively low prices. They have moved from the backyard recreation status to important tools for the military, intelligence agencies, and corporate organizations. New practical applications such as military equipment and weaponry are announced on a regular basis – globally. (Coutu, 2020) Every country seems to be announcing steps forward in this bludgeoning field. In our successful 2nd edition of Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets (Nichols, et al., 2019), the authors addressed three factors influencing UAS phenomena. First, unmanned aircraft technology has seen an economic explosion in production, sales, testing, specialized designs, and friendly / hostile usages of deployed UAS / UAVs / Drones. There is a huge global growing market and entrepreneurs know it. Second, hostile use of UAS is on the forefront of DoD defense and offensive planners. They are especially concerned with SWARM behavior. Movies like “Angel has Fallen,” where drones in a SWARM use facial recognition technology to kill USSS agents protecting POTUS, have built the lore of UAS and brought the problem forefront to DHS. Third, UAS technology was exploding. UAS and Counter- UAS developments in navigation, weapons, surveillance, data transfer, fuel cells, stealth, weight distribution, tactics, GPS / GNSS elements, SCADA protections, privacy invasions, terrorist uses, specialized software, and security protocols has exploded. (Nichols, et al., 2019) Our team has followed / tracked joint ventures between military and corporate entities and specialized labs to build UAS countermeasures. As authors, we felt compelled to address at least the edge of some of the new C-UAS developments. It was clear that we would be lucky if we could cover a few of – the more interesting and priority technology updates – all in the UNCLASSIFIED and OPEN sphere. Counter Unmanned Aircraft Systems: Technologies and Operations is the companion textbook to our 2nd edition. The civilian market is interesting and entrepreneurial, but the military and intelligence markets are of concern because the US does NOT lead the pack in C-UAS technologies. China does. China continues to execute its UAS proliferation along the New Silk Road Sea / Land routes (NSRL). It has maintained a 7% growth in military spending each year to support its buildup. (Nichols, et al., 2019) [Chapter 21]. They continue to innovate and have recently improved a solution for UAS flight endurance issues with the development of advanced hydrogen fuel cell. (Nichols, et al., 2019) Reed and Trubetskoy presented a terrifying map of countries in the Middle East with armed drones and their manufacturing origin. Guess who? China. (A.B. Tabriski & Justin, 2018, December) Our C-UAS textbook has as its primary mission to educate and train resources who will enter the UAS / C-UAS field and trust it will act as a call to arms for military and DHS planners.https://newprairiepress.org/ebooks/1031/thumbnail.jp

MISSION ENGINEERING FOR HYBRID FORCE 2025

This report focuses on the mission engineering process for a hybrid force in 2025. Updated tasking from OPNAV N9I emphasized the necessity of focusing on the benefits of using cost-conservative unmanned systems. Specifically, the focus was placed on the near-peer competitor China and the problems that could be expected in an anti-access/area denial (A2/AD) situation in the South China Sea. The Naval Surface Warfare Center mission engineering approach was used to identify specific vignettes for proposed alternative fleet architectures and then analyzed using combat simulation and optimization models. Research on performance characteristics and cost were compiled on current unmanned systems, specifically those in development at a high technology readiness level. Proposed unmanned systems architectures were developed as solutions to the A2/AD problem and proposed vignettes. The unmanned systems architectures were then run through an optimization model to maximize system performance while minimizing cost. The results of the architecture optimization were then input into modeling and simulation. The overall effectiveness of each architecture in each vignette were then compared to find the most effective solution. An analysis of the results was performed to show the expected mission effectiveness and proposed cost of utilizing the proposed solution unmanned architectures. The most effective architectures included search, counter swarm, delivery, and attack systems.Lieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyMajor, Republic of Singapore NavyMajor, Singapore ArmyLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyCommander, United States NavyApproved for public release. Distribution is unlimited

Planning the future of smart cities with swarms of fully autonomous unmanned aerial vehicles using a novel framework

The autonomy of unmanned aerial vehicles (UAVs) - self-governing in the aerospace discipline has been a remarkable research area with the development of the advanced bespoke microcontrollers embedded with advanced AI techniques for the last several decades. The road forward about the operational environment is certain about the swarms of fully automated UAVs (FAUAVs), that is, urban areas. Therefore, the planning the future of cities with swarms of fully autonomous unmanned aerial vehicles is explored in this paper to optimise the use of this type of autonomy with a diverse range of applications and a contemporary methodology is proposed using a synergistic holistic framework equipped with various effective and efficient techniques along with a novel FAUAV routing approach customisable to the constraints of FAUAVs and urban areas. The framework consists of a decentralized agent-based control architecture that monitors and controls the swarms of resource-constraint FAUAVs for their real-time requirements in optimising their urban uses. The results demonstrate that the constraints of FAUAVs can be mitigated significantly in urban areas and their use in realising their diverse range of missions can be optimised using the proposed methodology

Multi-UAV wireless positioning using adaptive multidimensional scaling and extended Kalman filter

Global Navigation Satellite System (GNSS) signal can be blocked when flight vehicles operate in challenging environments such as indoor or adversarial environments. While multi-UAVs are teamed during flight, cooperative localization becomes available to tackle this challenge. Multidimensional Scaling (MDS) method has been well studied for cooperative localization of Wireless Sensor Network (WSN) based on radio frequency (RF) measurement. When noise RF measurement model is lacking, conventional weighted MDS method represents confidence with the measurements by assigning weights relying on distance information between each pair of nodes. In order to process non-distance RF measurements, we present an improved weighted MDS method which applies a novel weighting scheme. In this article, the proposed method conducts velocity estimation for multi-UAV system based on odometry and Frequency Difference of Arrival (FDOA) measurements. Furthermore, an extended Kalman Filter (EKF) algorithm is applied to refine the initial estimation of the MDS method and derive position estimation. Finally, numerical experiments demonstrate the robustness and accuracy of the adaptive MDS-EKF refinement framework for multi-UAV system localization in an unknown dynamic environment lacking measurement noise information.UK Government Foreign, Commonwealth and Development Office: Chevening Scholarship. European Union funding: 778305

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