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Author: Adam R. Bell Publisher: ISBN: Category : Aerial observation (Military science) Languages : en Pages : 73
Book Description
Uninhabited aerial vehicles, also called UAVs are currently controller by a combination of a human pilot at a remote location, and autopilot systems similar to those found on commercial aircraft. As UAVs transition from remote piloting to fully autonomous operation, control laws must be developed for the tasks to be performed. Flight control and navigation are low-level tasks that must be performed by the UAV in order to complete more useful missions. In the domain of persistent aerial surveillance, in which UAVs are responsible for locating and continually observing points of interest (POIs) in the environment, such a mission can be accomplished much more efficiently by groups of cooperating UAVs. To develop the controller for a UAV, a discrete-time, physics-based simulator was developed in which an initially random neural network controller could be evolved over successive generations to produce the desired output. Because of the inherent complexity of navigating and maintaining stable flight, a novel state space utilizing an approximation of the flight path length between the aircraft and its navigational waypoint is developed and implemented. In choosing the controller output as the net thrust of the aircraft from all control surfaces and impellers, a controller suitable for a wide range of UAV types is reached. To develop a controller for each aircraft to cooperate in the persistent aerial surveillance domain, a behavior-based simulator was developed. Using this simulator, constraints on the flight dynamics are approximated to speed computation. Each UAV agent trains a neural network controller through successive episodes using sensory data about other aircraft and POIs. Testing of each controller was done by simulating in increasingly dynamic environments. The flight controller is shown to be able to successfully maintain heading and altitude and to make turns to ultimately reach a waypoint. The surveillance coordination controller is shown to coordinate UAVs well for both static and mobile POIs, and to scale well from systems of 3 agents to systems of 30 agents. Scaling of the controller to more agents is particularly effective when using a difference reward calculation in training the controllers.
Author: Adam R. Bell Publisher: ISBN: Category : Aerial observation (Military science) Languages : en Pages : 73
Book Description
Uninhabited aerial vehicles, also called UAVs are currently controller by a combination of a human pilot at a remote location, and autopilot systems similar to those found on commercial aircraft. As UAVs transition from remote piloting to fully autonomous operation, control laws must be developed for the tasks to be performed. Flight control and navigation are low-level tasks that must be performed by the UAV in order to complete more useful missions. In the domain of persistent aerial surveillance, in which UAVs are responsible for locating and continually observing points of interest (POIs) in the environment, such a mission can be accomplished much more efficiently by groups of cooperating UAVs. To develop the controller for a UAV, a discrete-time, physics-based simulator was developed in which an initially random neural network controller could be evolved over successive generations to produce the desired output. Because of the inherent complexity of navigating and maintaining stable flight, a novel state space utilizing an approximation of the flight path length between the aircraft and its navigational waypoint is developed and implemented. In choosing the controller output as the net thrust of the aircraft from all control surfaces and impellers, a controller suitable for a wide range of UAV types is reached. To develop a controller for each aircraft to cooperate in the persistent aerial surveillance domain, a behavior-based simulator was developed. Using this simulator, constraints on the flight dynamics are approximated to speed computation. Each UAV agent trains a neural network controller through successive episodes using sensory data about other aircraft and POIs. Testing of each controller was done by simulating in increasingly dynamic environments. The flight controller is shown to be able to successfully maintain heading and altitude and to make turns to ultimately reach a waypoint. The surveillance coordination controller is shown to coordinate UAVs well for both static and mobile POIs, and to scale well from systems of 3 agents to systems of 30 agents. Scaling of the controller to more agents is particularly effective when using a difference reward calculation in training the controllers.
Author: Abdelkader Abdessameud Publisher: Springer Science & Business Media ISBN: 1447150945 Category : Technology & Engineering Languages : en Pages : 192
Book Description
Motion Coordination for VTOL Unmanned Aerial Vehicles develops new control design techniques for the distributed coordination of a team of autonomous unmanned aerial vehicles. In particular, it provides new control design approaches for the attitude synchronization of a formation of rigid body systems. In addition, by integrating new control design techniques with some concepts from nonlinear control theory and multi-agent systems, it presents a new theoretical framework for the formation control of a class of under-actuated aerial vehicles capable of vertical take-off and landing. Several practical problems related to the systems’ inputs, states measurements, and restrictions on the interconnection topology between the aerial vehicles in the team are addressed. Worked examples with sufficient details and simulation results are provided to illustrate the applicability and effectiveness of the theoretical results discussed in the book. The material presented is primarily intended for researchers and industrial engineers from robotics, control engineering and aerospace communities. It also serves as a complementary reading for graduate students involved in research related to flying robotics, aerospace, control of under-actuated systems, and nonlinear control theory
Author: Isaac Kaminer Publisher: Butterworth-Heinemann ISBN: 012809947X Category : Technology & Engineering Languages : en Pages : 272
Book Description
Time-Critical Cooperative Control of Autonomous Air Vehicles presents, in an easy-to-read style, the latest research conducted in the industry, while also introducing a set of novel ideas that illuminate a new approach to problem-solving. The book is virtually self-contained, giving the reader a complete, integrated presentation of the different concepts, mathematical tools, and control solutions needed to tackle and solve a number of problems concerning time-critical cooperative control of UAVs. By including case studies of fixed-wing and multirotor UAVs, the book effectively broadens the scope of application of the methodologies developed. This theoretical presentation is complemented with the results of flight tests with real UAVs, and is an ideal reference for researchers and practitioners from academia, research labs, commercial companies, government workers, and those in the international aerospace industry. - Addresses important topics related to time-critical cooperative control of UAVs - Describes solutions to the problems rooted in solid dynamical systems theory - Applies the solutions developed to fixed-wing and multirotor UAVs - Includes the results of field tests with both classes of UAVs
Author: Wenxing Fu Publisher: Springer Nature ISBN: 981990479X Category : Technology & Engineering Languages : en Pages : 3985
Book Description
This book includes original, peer-reviewed research papers from the ICAUS 2022, which offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their most recent research and innovative ideas. The aim of the ICAUS 2022 is to stimulate researchers active in the areas pertinent to intelligent unmanned systems. The topics covered include but are not limited to Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and Its Application in Unmanned Systems. The papers showcased here share the latest findings on Unmanned Systems, Robotics, Automation, Intelligent Systems, Control Systems, Integrated Networks, Modeling and Simulation. It makes the book a valuable asset for researchers, engineers, and university students alike.
Author: Michail G. Michailidis Publisher: Springer Nature ISBN: 3030407160 Category : Technology & Engineering Languages : en Pages : 119
Book Description
This book introduces a comprehensive and mathematically rigorous controller design for families of nonlinear systems with time-varying parameters and unstructured uncertainties. Although the presented methodology is general, the specific family of systems considered is the latest, NextGen, unconventional fixed-wing unmanned aircraft with circulation control or morphing wings, or a combination of both. The approach considers various sources of model and parameter uncertainty, while the controller design depends not on a nominal plant model, but instead on a family of admissible plants. In contrast to existing controller designs that consider multiple models and multiple controllers, the proposed approach is based on the ‘one controller fits all models’ within the unstructured uncertainty interval. The book presents a modeling-based analysis and synthesis approach with additive uncertainty weighting functions for accurate realization of the candidate systems. This differs significantly from existing designs in that it is capable of handling time-varying characteristics. This research monograph is suitable for scientists, engineers, researchers and graduate students with a background in control system theory who are interested in complex engineering nonlinear systems.
Author: Mahsoo Salimi Publisher: ISBN: Category : Languages : en Pages : 215
Book Description
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation.
Author: Tal Shima Publisher: SIAM ISBN: 0898718589 Category : Mathematics Languages : en Pages : 180
Book Description
Unmanned aerial vehicles (UAVs) are increasingly used in military missions because they have the advantages of not placing human life at risk and of lowering operation costs via decreased vehicle weight. These benefits can be fully realized only if UAVs work cooperatively in groups with an efficient exchange of information. This book provides an authoritative reference on cooperative decision and control of UAVs and the means available to solve problems involving them.
Author: Publisher: Jeffrey Frank Jones ISBN: Category : Languages : en Pages : 3840
Book Description
Over 3,800 total pages ... Just a sample of the studies / publications included: Drone Swarms Terrorist and Insurgent Unmanned Aerial Vehicles: Use, Potentials, and Military Implications Countering A2/AD with Swarming Stunning Swarms: An Airpower Alternative to Collateral Damage Ideal Directed-Energy System To Defeat Small Unmanned Aircraft System Swarms Break the Kill Chain, not the Budget: How to Avoid U.S. Strategic Retrenchment Gyges Effect: An Ethical Critique of Lethal Remotely Piloted Aircraft Human Robotic Swarm Interaction Using an Artificial Physics Approach Swarming UAS II Swarming Unmanned Aircraft Systems Communication Free Robot Swarming UAV Swarm Attack: Protection System Alternatives for Destroyers Confidential and Authenticated Communications in a Large Fixed-Wing UAV Swarm UAV Swarm Behavior Modeling for Early Exposure of Failure Modes Optimized Landing of Autonomous Unmanned Aerial Vehicle Swarms Mini, Micro, and Swarming Unmanned Aerial Vehicles: A Baseline Study UAV Swarm Operational Risk Assessment System SmartSwarms: Distributed UAVs that Think Command and Control Autonomous UxV's UAV Swarm Tactics: An Agent-Based Simulation and Markov Process Analysis A Novel Communications Protocol Using Geographic Routing for Swarming UAVs Performing a Search Mission Accelerating the Kill Chain via Future Unmanned Aircraft Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles AFIT UAV Swarm Mission Planning and Simulation System A Genetic Algorithm for UAV Routing Integrated with a Parallel Swarm Simulation Applying Cooperative Localization to Swarm UAVS Using an Extended Kalman Filter A Secure Group Communication Architecture for a Swarm of Autonomous Unmanned Aerial Vehicles Braving the Swarm: Lowering Anticipated Group Bias in Integrated Fire/Police Units Facing Paramilitary Terrorism Distributed Beamforming in a Swarm UAV Network Integrating UAS Flocking Operations with Formation Drag Reduction Tracking with a Cooperatively Controlled Swarm of GMTI Equipped UAVS Using Agent-Based Modeling to Evaluate UAS Behaviors in a Target-Rich Environment Experimental Analysis of Integration of Tactical Unmanned Aerial Vehicles and Naval Special Warfare Operations Forces Target Acquisition Involving Multiple Unmanned Air Vehicles: Interfaces for Small Unmanned Air Systems (ISUS) Program Tools for the Conceptual Design and Engineering Analysis of Micro Air Vehicles Architectural Considerations for Single Operator Management of Multiple Unmanned Aerial Vehicles
Author: Andrea L'Afflitto Publisher: Springer Nature ISBN: 3031397673 Category : Technology & Engineering Languages : en Pages : 363
Book Description
Control of Autonomous Aerial Vehicles is an edited book that provides a single-volume snapshot on the state of the art in the field of control theory applied to the design of autonomous unmanned aerial vehicles (UAVs), aka “drones”, employed in a variety of applications. The homogeneous structure allows the reader to transition seamlessly through results in guidance, navigation, and control of UAVs, according to the canonical classification of the main components of a UAV’s autopilot. Each chapter has been written to assist graduate students and practitioners in the fields of aerospace engineering and control theory. The contributing authors duly present detailed literature reviews, conveying their arguments in a systematic way with the help of diagrams, plots, and algorithms. They showcase the applicability of their results by means of flight tests and numerical simulations, the results of which are discussed in detail. Control of Autonomous Aerial Vehicles will interest readers who are researchers, practitioners or graduate students in control theory, autonomous systems or robotics, or in aerospace, mechanical or electrical engineering.
Author: Hassan, Jahan Publisher: IGI Global ISBN: Category : Computers Languages : en Pages : 425
Book Description
Applications of Machine Learning in UAV Networks presents a pioneering exploration into the symbiotic relationship between machine learning techniques and UAVs. In an age where UAVs are revolutionizing sectors as diverse as agriculture, environmental preservation, security, and disaster response, this meticulously crafted volume offers an analysis of the manifold ways machine learning drives advancements in UAV network efficiency and efficacy. This book navigates through an expansive array of domains, each demarcating a pivotal application of machine learning in UAV networks. From the precision realm of agriculture and its dynamic role in yield prediction to the ecological sensitivity of biodiversity monitoring and habitat restoration, the contours of each domain are vividly etched. These explorations are not limited to the terrestrial sphere; rather, they extend to the pivotal aerial missions of wildlife conservation, forest fire monitoring, and security enhancement, where UAVs adorned with machine learning algorithms wield an instrumental role. Scholars and practitioners from fields as diverse as machine learning, UAV technology, robotics, and IoT networks will find themselves immersed in a confluence of interdisciplinary expertise. The book's pages cater equally to professionals entrenched in agriculture, environmental studies, disaster management, and beyond.
Author: Adam R. Bell
Author: Adam R. Bell
Author: Abdelkader Abdessameud
Author: Isaac Kaminer
Author: Wenxing Fu
Author: Michail G. Michailidis
Author: Mahsoo Salimi
Author: Tal Shima
Author: 
Author: Andrea L'Afflitto
Author: Hassan, Jahan