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• Part I Global Robot Path Planning
• 1.
• Introduction to Mobile Robot Path Planning
• 2.
• Background on Artificial Intelligence Algorithms for Global Path Planning
• 3.
• Design and Evaluation of Intelligent Global Path Planning Algorithms 4.
• Integration of Global Path Planners in ROS
• 5.
• Robot Path Planning using Cloud Computing for Large Grid Maps
• Part II Multi-Robot Task Allocation
• 6.
• General Background on Multi-Robot Task Allocation
• 7.
• Different Approaches to Solve the MRTA Problem
• 8.
• Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot .
• (source: Nielsen Book Data)

This book presents extensive research on two main problems in robotics: the path planning problem and the multi-robot task allocation problem. It is the first book to provide a comprehensive solution for using these techniques in large-scale environments containing randomly scattered obstacles. The research conducted resulted in tangible results both in theory and in practice. For path planning, new algorithms for large-scale problems are devised and implemented and integrated into the Robot Operating System (ROS). The book also discusses the parallelism advantage of cloud computing techniques to solve the path planning problem, and, for multi-robot task allocation, it addresses the task assignment problem and the multiple traveling salesman problem for mobile robots applications. In addition, four new algorithms have been devised to investigate the cooperation issues with extensive simulations and comparative performance evaluation. The algorithms are implemented and simulated in MATLAB and Webots.
(source: Nielsen Book Data)

• micro-ROS- Lab-scale Smart Factory Implementation Using ROS.- High Fidelity IMU and Wheel Encoder Models for ROS based AGV Simulations.- Autonomous Laser-Induced Breakdown Spectroscopy System for Chemical and Mineralogical Designation of Interplanetary.
• (source: Nielsen Book Data)

This book is the seventh volume of the successful book series on Robot Operating System: The Complete Reference, which started in 2016. The book's objective is to provide the reader with comprehensive coverage on the Robot Operating Systems (ROS) and the latest trends and contributed systems. ROS has been considered as the primary development framework for robotics applications. There are seven chapters organized into three parts. Part I presents one chapter dealing with ROS2 and presents a tutorial on using the MediaPipe framework with ROS2. In Part II, three chapters present new contributions of ROS frameworks and applications, including micro-ROS, Autonomous 3D Thermal Mapping of Disaster Environments, and Lab-scale Smart Factory Implementation Using ROS. Part III provides contributions on how to use ROS for cooperative robotics behaviors, particularly in platoon applications, in addition to developing new perception and control algorithms with sensing technologies. This book will be a valuable companion for ROS users and developers to learn more about ROS capabilities and features. .
(source: Nielsen Book Data)

• Development of an Industry 4.0 demonstrator using Sequence Planner and ROS2.- ROS2 for ROS1 users.- Multi-Robot SLAM framework for ROS with Efficient Information Sharing.- Agile experimentation of robot swarms in large scale.- Lessons learned building a self-driving car on top of ROS.- Landing a UAV on a moving platform using a front facing camera.- Integrating the Functional Mock-up Interface with ROS and Gazebo.- An ARVA sensor simulator.- ROS Implementation for Untethered Microrobot Manipulation.- ClegS: A package to develop C-legged robots.- Video frames selection method for 3D Reconstruction depending on ROS-based monocular SLAM.- ROS Rescue: Fault Tolerance System for ROS.
• (source: Nielsen Book Data)

This book is the fifth volume in the successful book series Robot Operating System: The Complete Reference. The objective of the book is to provide the reader with comprehensive coverage on the Robot Operating System (ROS), which is currently considered to be the primary development framework for robotics applications, and the latest trends and contributing systems. The content is divided into six parts. Pat I presents for the first time the emerging ROS 2.0 framework, while Part II focuses on multi-robot systems, namely on SLAM and Swarm coordination. Part III provides two chapters on autonomous systems, namely self-driving cars and unmanned aerial systems. In turn, Part IV addresses the contributions of simulation frameworks for ROS. In Part V, two chapters explore robotic manipulators and legged robots. Finally, Part VI presents emerging topics in monocular SLAM and a chapter on fault tolerance systems for ROS. Given its scope, the book will offer a valuable companion for ROS users and developers, helping them deepen their knowledge of ROS capabilities and features. .
(source: Nielsen Book Data)

• Part I. ROS 2
• .0: Developing Production-Grade Applications with ROS 2.- Reactive Programming of Robots with RxROS.- Part II. Advanced Robotic Systems.- ROS Integration of an Instrumented Bobcat T190 for the SEMFIRE project.- CopaDrive: an integrated ROS cooperative driving test and validation framework.- Part III. ROS Navigation.- Autonomous Navigation with Mobile Robots using Deep Learning and the Robot Operating System.- ROS Navigation Tuning Guide.- Mobile Robotics, SLAM, Bayesian Filter, Keyframe Bundle Adjustment and ROS Applications.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Part I: Navigation
• A Guide for 3D Mapping with Low-Cost Sensors using ROS
• Path Planning and Following for an Autonomous Model Car Using an "Eye in the Sky"
• Part II: Quadcopters
• Parametric Optimization for Nonlinear Quadcopter Control using Stochastic Test Signals
• CrazyS: a software-in-the-loop Simulation Platform for the Crazyflie 2.0 nano-quadcopter
• Part III: Applications
• Cloud Robotics with ROS
• Video Stabilization of the NAO Robot Using IMU Data
• Part IV: ROS Tools
• Roslaunch2: Versatile, Flexible and Dynamic Launch Configurations for the Robot Operating System
• Penetration testing ROS.

This is the fourth volume of the successful series Robot Operating Systems: The Complete Reference, providing a comprehensive overview of robot operating systems (ROS), which is currently the main development framework for robotics applications, as well as the latest trends and contributed systems. The book is divided into four parts: Part 1 features two papers on navigation, discussing SLAM and path planning. Part 2 focuses on the integration of ROS into quadcopters and their control. Part 3 then discusses two emerging applications for robotics: cloud robotics, and video stabilization. Part 4 presents tools developed for ROS; the first is a practical alternative to the roslaunch system, and the second is related to penetration testing. This book is a valuable resource for ROS users and wanting to learn more about ROS capabilities and features.

• A ROS-based framework for simulation and benchmarking of multi-robot patrolling algorithms.- ROS: Developing a new generation of operator interfaces.- Autonomous Exploration and Inspection Path Planning for Aerial Robots using the Robot Operating System.- A generic ROS-based System for Rapid Development and Testing of Algorithms for Autonomous Ground and Aerial Vehicles.- A generic ROS-based System for Rapid Development and Testing of Algorithms for Autonomous Ground and Aerial Vehicles.- ROS-based Approach for Unmanned Vehicles in Civil Applications.- A quadcopter and mobile robot cooperative task using Augmented Reality Tags.- An Extensible Optimization Toolset for Motion Planning and Control Prototyping and Benchmarking.- Online Trajectory Optimization and Navigation for Dynamic Environments in ROS.- A Backstepping Non-smooth Controller for ROS-based Differential-Drive Mobile Robots.- University Rover Challenge: Tutorials and Control System Survey.- SROS: Using and Developing Secure ROS Systems.- GPU and ROS - The use of general parallel processing architecture for robot perception.- Connecting ROS and FIWARE: concepts and tutorial.
• (source: Nielsen Book Data)

Building on the successful first and second volumes, this book is the third volume of the Springer book on the Robot Operating System (ROS): The Complete Reference. The Robot Operating System is evolving from year to year with a wealth of new contributed packages and enhanced capabilities. Further, the ROS is being integrated into various robots and systems and is becoming an embedded technology in emerging robotics platforms. The objective of this third volume is to provide readers with additional and comprehensive coverage of the ROS and an overview of the latest achievements, trends and packages developed with and for it. Combining tutorials, case studies, and research papers, the book consists of sixteen chapters and is divided into five parts. Part 1 presents multi-robot systems with the ROS. In Part 2, four chapters deal with the development of unmanned aerial systems and their applications. In turn, Part 3 highlights recent work related to navigation, motion planning and control. Part 4 discusses recently contributed ROS packages for security, ROS2, GPU usage, and real-time processing. Lastly, Part 5 deals with new interfaces allowing users to interact with robots. Taken together, the three volumes of this book offer a valuable reference guide for ROS users, researchers, learners and developers alike. Its breadth of coverage makes it a unique resource.
(source: Nielsen Book Data)

• Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using ROS.- Design of Fuzzy Logic Controllers to ROS-based UAVs.- Flying Multiple UAVs Using ROS.- SkiROS
• A skill-based robot control architecture on top of ROS.- Control of Mobile Robots using ActionLib.- Parametric Identification of the Dynamics of Mobile Robots and Its Application for the Tuning of Controllers in ROS.- ROSLink: Bridging ROS with the Internet-of-Things for Cloud Robotics.- A ROS Package for Dynamic Bandwidth Management in Multi-Robot Systems.- An autonomous companion UAV for the SpaceBot Cup competition 2015. .
• (source: Nielsen Book Data)

This second volume is a continuation of the successful first volume of this Springer book, and as well as addressing broader topics it puts a particular focus on unmanned aerial vehicles (UAVs) with Robot Operating System (ROS). Consisting of three types of chapters: tutorials, cases studies, and research papers, it provides comprehensive additional material on ROS and the aspects of developing robotics systems, algorithms, frameworks, and applications with ROS. ROS is being increasingly integrated in almost all kinds of robots and is becoming the de-facto standard for developing applications and systems for robotics. Although the research community is actively developing applications with ROS and extending its features, amount of literature references is not representative of the huge amount of work being done. The book includes 19 chapters organized into six parts: Part 1 presents the control of UAVs with ROS, while in Part 2, three chapters deal with control of mobile robots. Part 3 provides recent work toward integrating ROS with Internet, cloud and distributed systems. Part 4 offers five case studies of service robots and field experiments. Part 5 presents signal-processing tools for perception and sensing, and lastly, Part 6 introduces advanced simulation frameworks. The diversity of topics in the book makes it a unique and valuable reference resource for ROS users, researchers, learners and developers. .
(source: Nielsen Book Data)

The objective of this book is to provide the reader with a comprehensive coverage on the Robot Operating Systems (ROS) and latest related systems, which is currently considered as the main development framework for robotics applications. The book includes twenty-seven chapters organized into eight parts. Part 1 presents the basics and foundations of ROS. In Part 2, four chapters deal with navigation, motion and planning. Part 3 provides four examples of service and experimental robots. Part 4 deals with real-world deployment of applications. Part 5 presents signal-processing tools for perception and sensing. Part 6 provides software engineering methodologies to design complex software with ROS. Simulations frameworks are presented in Part 7. Finally, Part 8 presents advanced tools and frameworks for ROS including multi-master extension, network introspection, controllers and cognitive systems. This book will be a valuable companion for ROS users and developers to learn more ROS capabilities and features.

• Part I Cloud Robotics.- Part II Cloud for the IoT.
• (source: Nielsen Book Data)

This book comprises four chapters that address some of the latest research in clouds robotics and sensor clouds. The first part of the book includes two chapters on cloud robotics. The first chapter introduces a novel resource allocation framework for cloud robotics and proposes a Stackelberg game model and the corresponding task oriented pricing mechanism for resource allocation. In the second chapter, the authors apply Cloud Computing for building a Cloud-Based 3D Point Cloud extractor for stereo images. Their objective is to have a dynamically scalable and applicable to near real-time scenarios.
(source: Nielsen Book Data)

• Robots and Sensor Networks Localization
• Cooperative Robots and Sensor Networks Applications
• System Engineering
• Mobility Management.

This book is the second volume on Cooperative Robots and Sensor Networks. The primary objective of this book is to provide an up-to-date reference for cutting-edge studies and research trends related to mobile robots and wireless sensor networks, and in particular for the coupling between them. Indeed, mobile robots and wireless sensor networks have enabled great potentials and a large space for ubiquitous and pervasive applications. Robotics and wireless sensor networks have mostly been considered as separate research fields and little work has investigated the marriage between these two technologies. However, these two technologies share several features, enable common cyber-physical applications and provide complementary support to each other. The book consists of ten chapters, organized into four parts. The first part of the book presents three chapters related to localization of mobile robots using wireless sensor networks. Two chapters presented new solutions based Extended Kalman Filter and Particle Filter for localizing the robots using range measurements with the sensor network. The third chapter presents a survey on mobility-assisted localization techniques in wireless sensor networks. The second part of the book deals with cooperative robots and sensor networks applications. One chapter presents a comprehensive overview of major applications coupling between robots and sensor networks and provides real-world examples of their cooperation. Two other chapters present applications for underwater robots and sensor networks.

• Genetic Machine Learning Approach for Link Quality Prediction in Mobile Wireless Sensor Networks / Gustavo Medeiros de Araújo, A.R. Pinto, Jörg Kaiser and Leandro Buss Becker
• Generation of Trajectories Using Predictive Control for Tracking Consensus with Sensing and Connectivity Constraint / Bernardo Ordoñez, Ubirajara F. Moreno, Jés Cerqueira and Luis Almeida
• Localization, Route Planning, and Smartphone Interface for Indoor Navigation / Balajee Kannan, Nisarg Kothari, Chet Gnegy, Hend Gedaway, M. Freddie Dias and M. Bernadine Dias
• Real-Time Modeling of Ocean Currents for Navigating Underwater Glider Sensing Networks / Dongsik Chang, Xiaolin Liang, Wencen Wu, Catherine R. Edwards and Fumin Zhang
• EasyLoc: Plug-and-Play RSS-Based Localization in Wireless Sensor Networks / Maissa Ben Jamâa, Anis Koubâa, Nouha Baccour, Yasir Kayani, Khaled Al-Shalfan and Mohamed Jmaiel.

Mobile robots and Wireless Sensor Networks (WSNs) have enabled great potentials and a large space for ubiquitous and pervasive applications. Robotics and WSNs have mostly been considered as separate research fields and little work has investigated the marriage between these two technologies. However, these two technologies share several features, enable common cyber-physical applications and provide complementary support to each other. The primary objective of book is to provide a reference for cutting-edge studies and research trends pertaining to robotics and sensor networks, and in particular for the coupling between them. The book consists of five chapters. The first chapter presents a cooperation strategy for teams of multiple autonomous vehicles to solve the rendezvous problem. The second chapter is motivated by the need to improve existing solutions that deal with connectivity prediction, and proposed a genetic machine learning approach for link-quality prediction. The third chapter presents an architecture for indoor navigation using an Android smartphone for guiding a variety of users, from sighted to the visually impaired, to their intended destination. In chapter four, the authors deal with accurate prediction modeling of ocean currents for underwater glider navigation. In chapter five, the authors discuss the challenges and limitations of RSS-based localization mechanisms and propose, EasyLoc, an autonomous and practical RSS-based localization technique that satisfies ease of deployment and implementation.

• Efficient Machine Learning of Mobile Robotic Systems based on Convolutional Neural Networks
• UAV Path Planning Based on Deep Reinforcement Learning
• Drone Shadow Cloud: A New Concept to Protect Individuals from Danger Sun Exposure in GCC Countries
• Accurate Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method for High-speed Target Interception
• Robotics and Artificial Intelligence in the Nuclear Industry: from Tele-operation to Cyber-physical Systems
• Deep Learning and Robotics, Surgical Robot Applications
• Deep Reinforcement Learning for Autonomous Mobile Robot Navigation
• Event Vision for Autonomous Off-road Navigation
• Multi-armed Bandit Approach for Task Scheduling of a Fixed-Base Robot in the Warehouse
• Machine Learning and Deep Learning for Robotics Applications
• A Review on Deep Learning on UAV Monitoring Systems for Agricultural Applications
• Navigation and Path Planning Techniques for UAV Swarm
• Intelligent Control System for Hybrid Electric Vehicle with Autonomous Charging
• Advanced Sensor Systems for Robotics and Autonomous Vehicles
• Four Wheeled Humanoid Second-Order Cascade Control of Holonomic Trajectories.

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. .

• Deep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review.- Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment.- Reactive Obstacle Avoidance Method for a UAV.- Guaranteed Performances for Learning-Based Control Systems using Robust Control Theory.- A cascaded deep Neural Network for Position Estimation of Industrial Robots.- Managing Deep Learning Uncertainty for Autonomous Systems.- Uncertainty-Aware Autonomous Mobile Robot Navigation with Deep Reinforcement Learning.- Deep Reinforcement Learning for Autonomous Mobile Networks in Micro-Grids.- Reinforcement learning for Autonomous Morphing Control and Cooperative Operations of UAV Cluster.- Image-Based Identification of Animal Breeds Using Deep Learning.
• (source: Nielsen Book Data)

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. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.
(source: Nielsen Book Data)

• 1. UAS System Design
• 2. UAS Control systems
• 3. Hybrid control of UAS
• 4. Obstacle and collision avoidance of UAS
• 5. UAV onboard data storage, transmission and retrieval
• 6. Kalman and Particle filtering and other advanced techniques for motion sensor data fusion
• 7. Simultaneous Localization and Mapping (SLAM)
• 8. Single/multiple IMU-Vision-based navigation and orientation
• 9. Autopilots and navigation: standard and advanced solutions for navigation integrity
• 10. Integration of UAS into the Internet
• 11. IoT applications using UAS
• 12. Safety issues of UAS
• 13. Ultra-Wide Band (UWB) localization
• 14. Security threats of UAS
• 15. UAS public deployment challenges
• 16. UAS for cloud robotics
• 17. Deep neural networks (DNN) for field aerial robot perception (e.g., object detection, or semantic classification for navigation)
• 18. Recurrent networks for state estimation and dynamic identification of aerial vehicles
• 19. Deep-reinforcement learning for aerial robots (discrete-, or continuous-control) in dynamic environments
• 20. Learning-based aerial manipulation in cluttered environments
• 21. Decision making or task planning using machine learning for field aerial robots
• 22. Long-term ecological monitoring based on UAVs
• 23. Ecological Integrity parameters mapping
• 24. Rapid risk and disturbance assessment using drones
• 25. Ecosystem structure and processes assessment by using UAVs.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Part I. Multi-Robots Systems
• Part II. Data Fusion, Localization and Mapping
• Part III. Security and Dependability
• Part IV. Mobility.

This book compiles some of the latest research in cooperation between robots and sensor networks. Structured in twelve chapters, this book addresses fundamental, theoretical, implementation and experimentation issues. The chapters are organized into four parts namely multi-robots systems, data fusion and localization, security and dependability, and mobility.