Unmanned Aerial Systems: Theoretical Foundation and Applications presents some of the latest innovative approaches to drones from the point-of-view of dynamic modeling, system analysis, optimization, control, communications, 3D-mapping, search and rescue, surveillance, farmland and construction monitoring, and more. With the emergence of low-cost UAS, a vast array of research works in academia and products in the industrial sectors have evolved. The book covers the safe operation of UAS, including, but not limited to, fundamental design, mission and path planning, control theory, computer vision, artificial intelligence, applications requirements, and more.

This book provides a unique reference of the state-of-the-art research and development of unmanned aerial systems, making it an essential resource for researchers, instructors and practitioners.

This book is the fifth volume in the successful book series Robot Operating System: The Complete Reference.

This book is the sixth volume of the successful book series on Robot Operating System: The Complete Reference. The objective of the book is to provide the reader with comprehensive coverage of the Robot Operating Systems (ROS) and the latest trends and contributed systems. ROS is currently considered as the primary development framework for robotics applications.

There are seven chapters organized into three parts.

Part I presents two chapters on the emerging ROS 2.0 framework; in particular, ROS 2.0 is become increasingly mature to be integrated into the industry. The first chapter from Amazon AWS deals with the challenges that ROS 2 developers will face as they transition their system to be commercial-grade. The second chapter deals with reactive programming for both ROS1 and ROS. In Part II, two chapters deal with advanced robotics, namely on the usage of robots in farms, and the second deals with platooning systems. Part III provides three chapters on ROS navigation. The first chapter deals with the use of deep learning for ROS navigation. The second chapter presents a detailed tuning guide on ROS navigation and the last chapter discusses SLAM for ROS applications. I believe that this book is a valuable companion for ROS users and developers to learn more ROS capabilities and features.

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.

In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). 

The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science.

This book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical for intelligent robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on. Humans are distinguished from animals by capacities such as receiving visual information, adjusting to uncertain circumstances, and making decisions to take action in a complex system. Significant progress has been made in robotics toward human-like intelligence; yet, there are still numerous unresolved issues. Deep learning, reinforcement learning, real-time learning, swarm intelligence, and other developing approaches such as tiny-ML have been developed in recent decades and used in robotics.

Artificial intelligence is being integrated into robots in order to develop advanced robotics capable of performing multiple tasks and learning new things with a better perception of the environment, allowing robots to perform critical tasks with human-like vision to detect or recognize various objects. Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome.

We believe that the increasing demands and challenges offered by real-world robotic applications encourage academic research in both artificial intelligence and robotics. The goal of this book is to bring together scientists, specialists, and engineers from around the world to present and share their most recent research findings and new ideas on artificial intelligence in robotics. 

This is a book that covers different aspects of UAV technology, including design and development, applications, security and communication, and legal and regulatory challenges.

The book serves as a valuable resource for researchers, practitioners, and students in the field of unmanned aerial vehicles, providing a comprehensive understanding of UAV technology and its applications.

Unmanned Aerial Systems: Theoretical Foundation and Applications presents some of the latest innovative approaches to drones from the point-of-view of dynamic modeling, system analysis, optimization, control, communications, 3D-mapping, search and rescue, surveillance, farmland and construction monitoring, and more. With the emergence of low-cost UAS, a vast array of research works in academia and products in the industrial sectors have evolved. The book covers the safe operation of UAS, including, but not limited to, fundamental design, mission and path planning, control theory, computer vision, artificial intelligence, applications requirements, and more.

This book provides a unique reference of the state-of-the-art research and development of unmanned aerial systems, making it an essential resource for researchers, instructors and practitioners.

This book is the fifth volume in the successful book series Robot Operating System: The Complete Reference.

This book is the sixth volume of the successful book series on Robot Operating System: The Complete Reference. The objective of the book is to provide the reader with comprehensive coverage of the Robot Operating Systems (ROS) and the latest trends and contributed systems. ROS is currently considered as the primary development framework for robotics applications.

There are seven chapters organized into three parts.

Part I presents two chapters on the emerging ROS 2.0 framework; in particular, ROS 2.0 is become increasingly mature to be integrated into the industry. The first chapter from Amazon AWS deals with the challenges that ROS 2 developers will face as they transition their system to be commercial-grade. The second chapter deals with reactive programming for both ROS1 and ROS. In Part II, two chapters deal with advanced robotics, namely on the usage of robots in farms, and the second deals with platooning systems. Part III provides three chapters on ROS navigation. The first chapter deals with the use of deep learning for ROS navigation. The second chapter presents a detailed tuning guide on ROS navigation and the last chapter discusses SLAM for ROS applications. I believe that this book is a valuable companion for ROS users and developers to learn more ROS capabilities and features.

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.

In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). 

The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science.

This book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical for intelligent robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on. Humans are distinguished from animals by capacities such as receiving visual information, adjusting to uncertain circumstances, and making decisions to take action in a complex system. Significant progress has been made in robotics toward human-like intelligence; yet, there are still numerous unresolved issues. Deep learning, reinforcement learning, real-time learning, swarm intelligence, and other developing approaches such as tiny-ML have been developed in recent decades and used in robotics.

Artificial intelligence is being integrated into robots in order to develop advanced robotics capable of performing multiple tasks and learning new things with a better perception of the environment, allowing robots to perform critical tasks with human-like vision to detect or recognize various objects. Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome.

We believe that the increasing demands and challenges offered by real-world robotic applications encourage academic research in both artificial intelligence and robotics. The goal of this book is to bring together scientists, specialists, and engineers from around the world to present and share their most recent research findings and new ideas on artificial intelligence in robotics. 

This is a book that covers different aspects of UAV technology, including design and development, applications, security and communication, and legal and regulatory challenges.

The book serves as a valuable resource for researchers, practitioners, and students in the field of unmanned aerial vehicles, providing a comprehensive understanding of UAV technology and its applications.