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Author: Yue Wang
Publisher: Springer Science & Business Media
ISBN: 1447129563
Category : Technology & Engineering
Languages : en
Pages : 167
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Book Description
Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-making strategies and sensor-management schemes are created in sequence. Modeling and analysis include rigorous mathematical proofs of the proposed theorems and the practical consideration of limited sensing resources and observation costs. A survey of the well-developed coverage control problem is also provided as a foundation of search algorithms within the overall decision-making strategies. Applications in both underwater sampling and space-situational awareness are investigated in detail. The control strategies proposed in each chapter are followed by illustrative simulation results and analysis. Academic researchers and graduate students from aerospace, robotics, mechanical or electrical engineering backgrounds interested in multi-agent coordination and control, in detection and estimation or in Bayes filtration will find this text of interest.
Author: Yue Wang
Publisher: Springer Science & Business Media
ISBN: 1447129563
Category : Technology & Engineering
Languages : en
Pages : 167
Get Book Here
Book Description
Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-making strategies and sensor-management schemes are created in sequence. Modeling and analysis include rigorous mathematical proofs of the proposed theorems and the practical consideration of limited sensing resources and observation costs. A survey of the well-developed coverage control problem is also provided as a foundation of search algorithms within the overall decision-making strategies. Applications in both underwater sampling and space-situational awareness are investigated in detail. The control strategies proposed in each chapter are followed by illustrative simulation results and analysis. Academic researchers and graduate students from aerospace, robotics, mechanical or electrical engineering backgrounds interested in multi-agent coordination and control, in detection and estimation or in Bayes filtration will find this text of interest.
Author: Yue Wang
Publisher: John Wiley & Sons
ISBN: 1119266211
Category : Technology & Engineering
Languages : en
Pages : 335
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Book Description
A comprehensive review of the state of the art in the control of multi-agent systems theory and applications The superiority of multi-agent systems over single agents for the control of unmanned air, water and ground vehicles has been clearly demonstrated in a wide range of application areas. Their large-scale spatial distribution, robustness, high scalability and low cost enable multi-agent systems to achieve tasks that could not successfully be performed by even the most sophisticated single agent systems. Cooperative Control of Multi-Agent Systems: Theory and Applications provides a wide-ranging review of the latest developments in the cooperative control of multi-agent systems theory and applications. The applications described are mainly in the areas of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). Throughout, the authors link basic theory to multi-agent cooperative control practice — illustrated within the context of highly-realistic scenarios of high-level missions — without losing site of the mathematical background needed to provide performance guarantees under general working conditions. Many of the problems and solutions considered involve combinations of both types of vehicles. Topics explored include target assignment, target tracking, consensus, stochastic game theory-based framework, event-triggered control, topology design and identification, coordination under uncertainty and coverage control. Establishes a bridge between fundamental cooperative control theory and specific problems of interest in a wide range of applications areas Includes example applications from the fields of space exploration, radiation shielding, site clearance, tracking/classification, surveillance, search-and-rescue and more Features detailed presentations of specific algorithms and application frameworks with relevant commercial and military applications Provides a comprehensive look at the latest developments in this rapidly evolving field, while offering informed speculation on future directions for collective control systems The use of multi-agent system technologies in both everyday commercial use and national defense is certain to increase tremendously in the years ahead, making this book a valuable resource for researchers, engineers, and applied mathematicians working in systems and controls, as well as advanced undergraduates and graduate students interested in those areas.
Author: Yue Wang
Publisher: Springer
ISBN: 1447129571
Category : Technology & Engineering
Languages : en
Pages : 167
Get Book Here
Book Description
Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-making strategies and sensor-management schemes are created in sequence. Modeling and analysis include rigorous mathematical proofs of the proposed theorems and the practical consideration of limited sensing resources and observation costs. A survey of the well-developed coverage control problem is also provided as a foundation of search algorithms within the overall decision-making strategies. Applications in both underwater sampling and space-situational awareness are investigated in detail. The control strategies proposed in each chapter are followed by illustrative simulation results and analysis. Academic researchers and graduate students from aerospace, robotics, mechanical or electrical engineering backgrounds interested in multi-agent coordination and control, in detection and estimation or in Bayes filtration will find this text of interest.
Author: Shaoshan Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681731673
Category : Computers
Languages : en
Pages : 285
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Book Description
This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.
Author: Maryam Zehtabchi
Publisher: WIPO
ISBN:
Category : Law
Languages : en
Pages : 36
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Book Description
Automotive industry is going through a technological shock. Multiple intertwined technological advances (autonomous vehicle, connect vehicles and mobility-as-a-Service) are creating new rules for an industry that had not changed its way of doing business for almost a century. Key players from the tech and traditional automobile sectors – although with different incentives – are pooling resources to realize the goal of self-driving cars. AV innovation by auto and tech companies’ innovation is still largely home based, however, there is some shifting geography at the margin. AV and other related technologies are broadening the automotive innovation landscape, with several IT-focused hotspots – which traditionally were not at the center of automotive innovation – gaining prominence.
Author: Eklas Hossain
Publisher: Springer Nature
ISBN: 3031469909
Category : Computers
Languages : en
Pages : 465
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Book Description
Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly.
Author: Mo, Hongwei
Publisher: IGI Global
ISBN: 1605663115
Category : Computers
Languages : en
Pages : 633
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Book Description
"This book offers new ideas and recent developments in Natural Computing, especially on artificial immune systems"--Provided by publisher.
Author: Sergiy Butenko
Publisher: Springer Science & Business Media
ISBN: 1461302196
Category : Mathematics
Languages : en
Pages : 461
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Book Description
Over the past several years, cooperative control and optimization has un questionably been established as one of the most important areas of research in the military sciences. Even so, cooperative control and optimization tran scends the military in its scope -having become quite relevant to a broad class of systems with many exciting, commercial, applications. One reason for all the excitement is that research has been so incredibly diverse -spanning many scientific and engineering disciplines. This latest volume in the Cooperative Systems book series clearly illustrates this trend towards diversity and creative thought. And no wonder, cooperative systems are among the hardest systems control science has endeavored to study, hence creative approaches to model ing, analysis, and synthesis are a must! The definition of cooperation itself is a slippery issue. As you will see in this and previous volumes, cooperation has been cast into many different roles and therefore has assumed many diverse meanings. Perhaps the most we can say which unites these disparate concepts is that cooperation (1) requires more than one entity, (2) the entities must have some dynamic behavior that influences the decision space, (3) the entities share at least one common objective, and (4) entities are able to share information about themselves and their environment. Optimization and control have long been active fields of research in engi neering.
Author:
Publisher: Jeffrey Frank Jones
ISBN:
Category :
Languages : en
Pages : 3840
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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: Valentina Emilia Balas
Publisher: Springer
ISBN: 3030114791
Category : Technology & Engineering
Languages : en
Pages : 380
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Book Description
This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
