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

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Decentralized control for UAV path planning and task allocation

The effort of this research is to move toward enabling Unmanned Air Vehicles to fly in autonomous formations with intelligent mission planning capabilities. In particular, UAVs will be able to autonomously perform path planning and task allocation. During missions, the UAVs must be able to avoid threats and no-fly zones while still reaching their target optimally in time.;A path planning and task allocation approach was first developed that treats the problem as a Multi-dimensional, Multiple-Choice Knapsack Problem. Paths are selected and task assigned while minimizing the UAV team\u27s overall mission cost. Next, a SIMULINK-based centralized simulation environment was created. This simulation uses the path planning and task allocation scheme previously developed, and adds time-varying, dynamic environment aspects. The latter part of the research effort was focused on development of a decentralized simulation environment. This decentralized version includes a vehicle\u27s own decision making capabilities and communication amongst a team of vehicles. (Abstract shortened by UMI.)

Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task

A hierarchic optimization strategy based on the offline path planning process and online trajectory planning process is presented to solve the trajectory optimization problem of multiple quad-rotor unmanned aerial vehicles in the collaborative assembling task. Firstly, the path planning process is solved by a novel parallel intelligent optimization algorithm, the central force optimization-genetic algorithm (CFO-GA), which combines the central force optimization (CFO) algorithm with the genetic algorithm (GA). Because of the immaturity of the CFO, the convergence analysis of the CFO is completed by the stability theory of the linear time-variant discrete-time systems. The results show that the parallel CFO-GA algorithm converges faster than the parallel CFO and the central force optimization-sequential quadratic programming (CFO-SQP) algorithm. Then, the trajectory planning problem is established based on the path planning results. In order to limit the range of the attitude angle and guarantee the flight stability, the optimized object is changed from the ordinary six-degree-of-freedom rigid-body dynamic model to the dynamic model with an inner-loop attitude controller. The results show that the trajectory planning process can be solved by the mature SQP algorithm easily. Finally, the discussion and analysis of the real-time performance of the hierarchic optimization strategy are presented around the group number of the waypoints and the equal interval time

After Action Report

Naval Warfare Studies Institute (NWSI)Warfare Innovation Continuum (WIC)This Naval Warfare Studies Institute (NWSI), Consortium for Robotics and Unmanned Systems Education and Research (CRUSER), and Consortium for Intelligent Systems Education and Research (CISER) sponsored Warfare Innovation Continuum (WIC) workshop was held 21-24 September 2020 on the ‘Virtual Campus’ of the Naval Postgraduate School (NPS). The three-and-a-half-day experience allowed NPS students focused interaction with faculty, staff, fleet officers, and guest engineers from Navy labs, system commands and industry.Sponsored by the Naval Warfare Studies Institute (NWSI), the Consortium for Robotics and Unmanned Systems (CRUSER), and the Consortium for Intelligent Systems Education and Research (CISER

Guidance, Navigation and Control for UAV Close Formation Flight and Airborne Docking

Unmanned aerial vehicle (UAV) capability is currently limited by the amount of energy that can be stored onboard or the small amount that can be gathered from the environment. This has historically lead to large, expensive vehicles with considerable fuel capacity. Airborne docking, for aerial refueling, is a viable solution that has been proven through decades of implementation with manned aircraft, but had not been successfully tested or demonstrated with UAVs. The prohibitive challenge is the highly accurate and reliable relative positioning performance that is required to dock with a small target, in the air, amidst external disturbances. GNSS-based navigation systems are well suited for reliable absolute positioning, but fall short for accurate relative positioning. Direct, relative sensor measurements are precise, but can be unreliable in dynamic environments. This work proposes an experimentally verified guidance, navigation and control solution that enables a UAV to autonomously rendezvous and dock with a drogue that is being towed by another autonomous UAV. A nonlinear estimation framework uses precise air-to-air visual observations to correct onboard sensor measurements and produce an accurate relative state estimate. The state of the drogue is estimated using known geometric and inertial characteristics and air-to-air observations. Setpoint augmentation algorithms compensate for leader turn dynamics during formation flight, and drogue physical constraints during docking. Vision-aided close formation flight has been demonstrated over extended periods; as close as 4 m; in wind speeds in excess of 25 km/h; and at altitudes as low as 15 m. Docking flight tests achieved numerous airborne connections over multiple flights, including five successful docking manoeuvres in seven minutes of a single flight. To the best of our knowledge, these are the closest formation flights performed outdoors and the first UAV airborne docking

Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles

Full version unavailable due to 3rd party copyright restrictions.The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.EOARD (European Office of Aerospace Research & Development), euCognitio

Behavioral representation of military tactics for single-vehicle autonomous rotorcraft via statecharts

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (p. 113-115).Over the past several years, aerospace companies have developed unmanned helicopters suitable for integration into military operations as reconnaissance platforms. These rotorcraft, however, require ground-based human controllers varying in number based on the size and complexity of the system controlled. The automation these platforms have achieved is limited to takeoffs, landings and navigation of pre-programmed waypoints. The possibilities for further development then are vast; with growing sensor and communication capabilities, there exists potential for unmanned rotorcraft to execute the full range of aviation missions normally reserved for manned assets. However, before military planners use autonomous helicopters as robust force multipliers, research must attempt to quantify possible tactics for software architecture implementation. This paper presents a methodology for developing autonomous helicopter tactics through the review of current military doctrine, pilot interviews, and simulation testing. Several tactics suitable for unmanned helicopters are recommended with an attempt to quantify the described behaviors using statecharts. The tactics diagrammed in the statecharts, or visual models that outline transitions between states based on conditions being met or events having occurred, are tested for feasibility in scenarios constructed with a US Army simulation tool, One SemiAutomated Forces (OneSAF) Testbed Baseline 2.0 (OTB 2.0). The ensuing results point to the success of using a thorough methodology to develop autonomous tactics and using statecharts to transfer qualitative behaviors into quantifiable actions.by Mark M. Hickie.S.M

Achieving ship's mission flexibility through designing, printing and operating unmanned systems with additive manufacturing and delayed differentiation

The design, print and operate (DPO) concept of operations (CONOPS) is proposed in this thesis as a new means of equipping ships with the appropriate capabilities. A companion concept of delayed differentiation is also introduced. In coupling the two concepts, additive manufacturing of capabilities in-situ becomes a possibility through the equipping of operational units with three building blocks: additive manufacturing systems and their raw materials, commercial off-the-shelf items and field programmable gate arrays. A concept of operations on uses of additive manufacturing was developed to illustrate the flexibility that the nexus of DPO CONOPS and delayed differentiation can engender. A tactical unmanned aerial vehicle (UAV) was used as an illustration to contextualize the concept of operations to enhance the littoral combat ship's survivability when operating in the littorals. Assessments were then made on the feasibility of DPO CONOPS for shipboard uses. A tactical UAV was used as it was assessed to be operationally relevant and significant. Analytical models that could be iterated to achieve the specific-to-mission requirements were developed to analyze and assess the implementation approach. The models focused on the UAV's reliability in fulfilling the mission as well as the build-time of the UAV.http://archive.org/details/achievingshipsmi1094550484Military Expert 6, Republic of Singapore NavyApproved for public release; distribution is unlimited