Download or read book Methods for Online Predictive Control of Multi rotor Aerial Robots with Perception driven Tasks Subject to Sensing and Actuation Constraints written by Martin Jacquet and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drones have an increasing place in numerous applications already started to take advantage from those, in particular in the fields of photography and video making, or simply for leisure activities. Simultaneously, the picture of autonomous aerial robots widely spread as a mark of innovation, such that many civilian of industrial applications are now envisioned through this aspect. One could cite, for instance, the persistent idea of aerial home delivery of goods, exploited by many companies. Another spread use-case is the deployment of fleets of aerial robots for monitoring activities, in hard-to-access environments, such as high mountains.The aerial robotics research community is active from numerous years, and the state of the art keeps improving, being through the conception of novel, more adaptive control algorithms, or the improvements of the hardware designs, opening new ranges of possibilities.The deployment of such robots in the scope of applications in uncontrolled environments comes with a lot of challenges, in particular regarding the perception of the surroundings. Exteroceptive sensors are indeed mandatory for most of autonomous applications. Among those sensors, cameras hold a peculiar position.It is on the one hand due to the simple onboard integration with their small size and weight,and on the other hand to the design of human-made environments, which are heavily built around visual markers (signs, illuminated signals...) However, maintaining visibility over objects or phenomenon often collide with the motion requirements of the robot, or with the tasks to which it is assigned. This effect is prominent when using underactuated robots, which are the most widely spread types of aerial vehicles, partly because of their higher energy efficiency. This property implies a strong coupling between position and orientation: the robot needs to tilt to move, and corollary moves when it tilts, thus altering the sensor bearing.From this assessment, the robotics community works to produce sensorimotor algorithms, able to produce motions while accounting for perception.This thesis takes place in this context, aiming at proposing such control methods to enforce the visibility over a phenomenon of interest through the onboard sensors. Moreover, to ensure the feasibility of the generated commands, it is required to account for the various actuation limitations of the robots. Finally, this thesis devotes to propose generic formulations, thus avoiding to propose ad hoc solutions, which would be contingent to a specific problem.To tackles these aspects under a common formalism, the proposed solutions are based on optimal and predictive control policies. These are based on numerical optimization, implying the need of accurate models, and thus accounting for the system nonlinearities, which are often disregarded for simplification.The contributions of this these are the aggregation of the various concepts in a common paradigm,and the formalization of the various mathematical functions transcribing the objectives and constraints related to perception. This paradigm is used in the scope of several applications related to usual perception-driven tasks in aerial robotics, namely the tracking of dynamic phenomenon, the improvement of this tracking, or the visual-inertial localization. Finally, the proposed solutions are implemented and tested in simulations and on real aerial robots.The work conducted throughout this thesis led to various publications in international peer-reviewed conferences and journals. All the related software production from these works are published open-source for the robotics community.

Download or read book Perception for Control and Control for Perception of Vision based Autonomous Aerial Robots written by Eric Cristofalo and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The mission of this thesis is to develop visual perception and feedback control algorithms for autonomous aerial robots that are equipped with an onboard camera. We introduce light-weight algorithms that parse images from the robot's camera directly into feedback signals for control laws that improve perception quality. We emphasize the co-design, analysis, and implementation of the perception, planning, and control tasks to ensure that the entire autonomy pipeline is suitable for aerial robots with real-world constraints. The methods presented in this thesis further leverage perception for control and control for perception: the former uses perception to inform the robot how to act while the later uses robotic control to improve the robot's perception of the world. Perception in this work refers to the processing of raw sensor measurements and the estimation of state values while control refers to the planning of useful robot motions and control inputs based on these state estimates. The major capability that we enable is a robot's ability to sense this unmeasured scene geometry as well as the three-dimensional (3D) robot pose from images acquired by its onboard camera. Our algorithms specifically enable a UAV with an onboard camera to use control to reconstruct the 3D geometry of its environment in a both sparse sense and a dense sense, estimate its own global pose with respect to the environment, and estimate the relative poses of other UAVs and dynamic objects of interest in the scene. All methods are implemented on real robots with real-world sensory, power, communication, and computation constraints to demonstrate the need for tightly-coupled, fast perception and control in robot autonomy. Depth estimation at specific pixel locations is often considered to be a perception-specific task for a single robot. We instead control the robot to steer a sensor to improve this depth estimation. First, we develop an active perception controller that maneuvers a quadrotor with a downward facing camera according to the gradient of maximum uncertainty reduction for a sparse subset of image features. This allows us to actively build a 3D point cloud representation of the scene quickly and thus enabling fast situational awareness for the aerial robot. Our method reduces uncertainty more quickly than state-of-the-art approaches for approximately an order of magnitude less computation time. Second, we autonomously control the focus mechanism on a camera lens to build metric-scale, dense depth maps that are suitable for robotic localization and navigation. Compared to the depth data from an off-the-shelf RGB-D sensor (Microsoft Kinect), our Depth-from-Focus method recovers the depth for 88% of the pixels with no RGB-D measurements in near-field regime (0.0 - 0.5 meters), making it a suitable complimentary sensor for RGB-D. We demonstrate dense sensing on a ground robot localization application and with AirSim, an advanced aerial robot simulator. We then consider applications where groups of aerial robots with monocular cameras seek to estimate their pose, or position and orientation, in the environment. Examples include formation control, target tracking, drone racing, and pose graph optimization. Here, we employ ideas from control theory to perform the pose estimation. We first propose the tight-coupling of pairwise relative pose estimation with cooperative control methods for distributed formation control using quadrotors with downward facing cameras, target tracking in a heterogenous robot system, and relative pose estimation for competitive drone racing. We experimentally validate all methods with real-time perception and control implementations. Finally, we develop a distributed pose graph optimization method for networks of robots with noisy relative pose measurements. Unlike existing pose graph optimization methods, our method is inspired by control theoretic approaches to distributed formation control. We leverage tools from Lyapunov theory and multi-agent consensus to derive a relative pose estimation algorithm with provable performance guarantees. Our method also reaches consensus 13x faster than a state-of-the-art centralized strategy and reaches solutions that are approximately 6x more accurate than decentralized pose estimation methods. While the computation times between our method and the benchmarch distributed method are similar for small networks, ours outperforms the benchmark by a factor of 100 on networks with large numbers of robots (> 1000). Our approach is easy to implement and fast, making it suitable for a distributed backend in a SLAM application. Our methods will ultimately allow micro aerial vehicles to perform more complicated tasks. Our focus on tightly-coupled perception and control leads to algorithms that are streamlined for real aerial robots with real constraints. These robots will be more flexible for applications including infrastructure inspection, automated farming, and cinematography. Our methods will also enable more robot-to-robot collaboration since we present effective ways to estimate the relative pose between them. Multi-robot systems will be an important part of the robotic future as they are robust to the failure of individual robots and allow complex computation to be distributed amongst the agents. Most of all, our methods allow robots to be more self sufficient by utilizing their onboard camera and by accurately estimating the world's structure. We believe these methods will enable aerial robots to better understand our 3D world.

Download or read book Modeling Control State Estimation and Path Planning Methods for Autonomous Multirotor Aerial Robots written by Christos Papachristos and published by . This book was released on 2018 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: This review paper aims to provide an overview of core modeling, control, estimation, and planning concepts and approaches for micro aerial robots of the rotorcraft class. A comprehensive description of a set of methods that enable automated flight control, state estimation in GPS–denied environments, as well as path planning techniques for autonomous exploration is provided, and serves as a holistic point of reference for those interested in the field of unmanned aerial systems. Further discussion for other applications of aerial robots concludes this manuscript.

Download or read book Aerial Manipulation written by Matko Orsag and published by Springer. This book was released on 2017-09-19 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is a thorough treatment of the rapidly growing area of aerial manipulation. It details all the design steps required for the modeling and control of unmanned aerial vehicles (UAV) equipped with robotic manipulators. Starting with the physical basics of rigid-body kinematics, the book gives an in-depth presentation of local and global coordinates, together with the representation of orientation and motion in fixed- and moving-coordinate systems. Coverage of the kinematics and dynamics of unmanned aerial vehicles is developed in a succession of popular UAV configurations for multirotor systems. Such an arrangement, supported by frequent examples and end-of-chapter exercises, leads the reader from simple to more complex UAV configurations. Propulsion-system aerodynamics, essential in UAV design, is analyzed through blade-element and momentum theories, analysis which is followed by a description of drag and ground-aerodynamic effects. The central part of the book is dedicated to aerial-manipulator kinematics, dynamics, and control. Based on foundations laid in the opening chapters, this portion of the book is a structured presentation of Newton–Euler dynamic modeling that results in forward and backward equations in both fixed- and moving-coordinate systems. The Lagrange–Euler approach is applied to expand the model further, providing formalisms to model the variable moment of inertia later used to analyze the dynamics of aerial manipulators in contact with the environment. Using knowledge from sensor data, insights are presented into the ways in which linear, robust, and adaptive control techniques can be applied in aerial manipulation so as to tackle the real-world problems faced by scholars and engineers in the design and implementation of aerial robotics systems. The book is completed by path and trajectory planning with vision-based examples for tracking and manipulation.

Download or read book Visual Guidance of Unmanned Aerial Manipulators written by Angel Santamaria-Navarro and published by Springer. This book was released on 2019-09-10 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph covers theoretical and practical aspects of the problem of autonomous guiding of unmanned aerial manipulators using visual information. For the estimation of the vehicle state (position, orientation, velocity, and acceleration), the authors propose a method that relies exclusively on the use of low-cost and highrate sensors together with low-complexity algorithms. This is particularly interesting for applications in which on board computation with low computation power is needed. Another relevant topic covered in this monograph is visual servoing. The authors present an uncalibrated visual servo scheme, capable of estimating at run time, the camera focal length from the observation of a tracked target. The monograph also covers several control techniques, which achieve a number of tasks, such as robot and arm positioning, improve stability and enhance robot arm motions. All methods discussed in this monograph are demonstrated in simulation and through real robot experimentation. The text is appropriate for readers interested in state estimation and control of aerial manipulators, and is a reference book for people who work in mobile robotics research in general.

Download or read book Aerial Robotic Manipulation written by Anibal Ollero and published by Springer. This book was released on 2019-06-27 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aerial robotic manipulation integrates concepts and technologies coming from unmanned aerial systems and robotics manipulation. It includes not only kinematic, dynamics, aerodynamics and control but also perception, planning, design aspects, mechatronics and cooperation between several aerial robotics manipulators. All these topics are considered in this book in which the main research and development approaches in aerial robotic manipulation are presented, including the description of relevant systems. In addition of the research aspects, the book also includes the deployment of real systems both indoors and outdoors, which is a relevant characteristic of the book because most results of aerial robotic manipulation have been validated only indoor using motion tracking systems. Moreover, the book presents two relevant applications: structure assembly and inspection and maintenance, which has started to be applied in the industry. The Chapters of the book will present results of two main European Robotics Projects in aerial robotics manipulation: FP7 ARCAS and H2020 AEROARMS. FP7 ARCAS defined the basic concepts on aerial robotic manipulation, including cooperative manipulation. The H2020 AEROARMS on aerial robot with multiple arms and advanced manipulation capabilities for inspection and maintenance has two general objectives: (1) development of advanced aerial robotic manipulation methods and technologies, including manipulation with dual arms and multi-directional thrusters aerial platforms; and (2) application to the inspection and maintenance.

Download or read book Model Predictive Control in the Process Industry written by Eduardo F. Camacho and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

Download or read book Model Based Control of Flying Robots for Robust Interaction Under Wind Influence written by Teodor Tomić and published by Springer Nature. This book was released on 2022-10-07 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the topic of autonomous flying robots physically interacting with the environment under the influence of wind. It aims to make aerial robots aware of the disturbance, interaction, and faults acting on them. This requires reasoning about the external wrench (force and torque) acting on the robot and distinguishing between wind, interactions, and collisions. The book takes a model-based approach and covers a systematic approach to parameter identification for flying robots. The book aims to provide a wind speed estimate independent of the external wrench, including estimating the wind speed using motor power measurements. Aerodynamics modeling is approached in a data-driven fashion, using ground-truth measurements from a 4D wind tunnel. Finally, the book bridges the gap between trajectory tracking and interaction control, to allow physical interaction under wind influence. Theoretical results are accompanied by extensive simulation and experimental results.

Download or read book Robot Operating System ROS written by Anis Koubaa and published by Springer. This book was released on 2017-05-25 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hybrid Predictive Control for Aerial Robotic Physical Interaction Towards Inspection Operations written by Georgios Darivianakis and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predictive Control of Uncertain Systems Based on Motion Prediction written by DONGHAN LEE and published by . This book was released on 2017 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model predictive control is a very popular control scheme in a wide range of fields including driver assistance systems and autonomous robots. For example, in driver assistance systems, predictive control allows for improved safety and comfort. However, its implementation is a challenge in uncertain environments. Therefore, it is desirable to predict the evolution of the environment in which the controlled system operates. In other words, we pursue a highly accurate forecast of the environment so that we may achieve feasible and reliable action from the controller. This dissertation presents a systematic framework that uses predictive control and forecasts of the future environment to operate under uncertainties and constraints. In particular, we focus on enhancing the performance of a predictive control scheme based on an accurate trajectory prediction of any targets controlled by humans (e.g., vehicles driven by human or humans themselves). We propose several motion-prediction-models using physics-based and data-driven approaches to improve the accuracy of the forecast. An interacting-multiple-model approach with Kalman filter techniques is useful in environments where it is difficult to have prior data sets such as disaster sites. Based on collected data from experimental vehicles, machine learning methods including hidden Markov models, convolution neural networks, and recurrent neural networks are used to enhance long-term predictions. Furthermore, we present predictive controls based on a probabilistic view of uncertain forecasts. The effectiveness of the proposed framework is demonstrated via applications such as human-companion robots, automotive adaptive cruise control, and autonomous lane change assist. The results of both simulation and real experimental data show the synergy between the motion prediction models and the predictive control designs.

Download or read book Adaptive and Learning Controllers for High Accuracy Trajectory Tracking in Changing Conditions written by Karime Pereida and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots are expected to perform tasks reliably in unknown and dynamic environments, which can include unknown disturbances and changing dynamics. This thesis investigates controllers for high-accuracy trajectory tracking of quadrotors in changing conditions. The proposed controllers use $\mathcal{L}_1$ adaptive control to handle disturbances and learning- and optimization-based controllers to improve tracking in changing conditions. The underlying $\Lone$ adaptive controller forces systems subject to unknown disturbances and changing dynamics to behave close to a specified reference model. Learning- and optimization-based controllers improve the tracking performance of the $\mathcal{L}_1$ controlled systems. This thesis presents five frameworks: \begin{enumerate*} \item Iterative Learning Control (ILC), \item Multi-Robot Transfer Learning, \item Multi-Robot, Multi-Task Transfer Learning, \item Adaptive Model Predictive Control (MPC), and \item Robust, Adaptive Model Predictive Control. \end{enumerate*} ILC calculates a feedforward input that minimizes tracking error by using information from previous iterations of the same trajectory. ILC assumes the system has a repeatable behaviour, which may not be true in changing conditions. Repeatability is achieved by using an underlying $\mathcal{L}_1$ adaptive controller. Dynamically different systems can behave in the same specified way if they are equipped with an $\mathcal{L}_1$ adaptive controller. Hence, trajectories learned on one system can directly be transferred to a dynamically different system in a Multi-Robot Transfer Learning framework. Typically, a new learning process has to be started for each desired trajectory. The Multi-Task Transfer Learning framework uses insights from control systems theory to generalize previously learned tasks and enable a Multi-Robot, Multi-Task Transfer Learning framework. In order to eliminate the need for a learning phase to achieve high-accuracy trajectory tracking, $\mathcal{L}_1$ adaptive control is combined with model predictive control. The proposed adaptive MPC leverages the performance guarantees of $\mathcal{L}_1$ adaptive controller to improve tracking performance on the first iteration. However, modelling errors may remain despite the presence of the $\mathcal{L}_1$ adaptive controller. Therefore, we propose and show performance guarantees of a robust adaptive MPC that is robust to modelling errors and improves the trajectory tracking performance in changing conditions. The proposed frameworks were implemented on the Parrot AR.Drone 2.0 and Bebop 2 quadrotors and showed high-accuracy trajectory tracking in changing conditions.

Download or read book Local Optimization Methods of Model Predictive Control on Robot Contact Problems written by Yuhan Zhao and published by . This book was released on 2019 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Behavior based Model Predictive Control for Networked Multi agent Systems written by Greg Nathanael Droge and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.

Download or read book Introduction to Multicopter Design and Control written by Quan Quan and published by Springer. This book was released on 2017-06-23 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first textbook specially on multicopter systems in the world. It provides a comprehensive overview of multicopter systems, rather than focusing on a single method or technique. The fifteen chapters are divided into five parts, covering the topics of multicopter design, modeling, state estimation, control, and decision-making. It differs from other books in the field in three major respects: it is basic and practical, offering self-contained content and presenting hands-on methods; it is comprehensive and systematic; and it is timely. It is also closely related to the autopilot that users often employ today and provides insights into the code employed. As such, it offers a valuable resource for anyone interested in multicopters, including students, teachers, researchers, and engineers. This introductory text is a welcome addition to the literature on multicopter design and control, on which the author is an acknowledged authority. The book is directed to advanced undergraduate and beginning graduate students in aeronautical and control (or electrical) engineering, as well as to multicopter designers and hobbyists. ------- Professor W. Murray Wonham, University of Toronto "This is the single best introduction to multicopter control. Clear, comprehensive and progressing from basic principles to advanced techniques, it's a must read for anyone hoping to learn how to design flying robots." ------- Chris Anderson, 3D Robotics CEO.

Download or read book Vol Stationnaire Et Suivi de Trajectoire D un Quadri rotor Bas s Sur la Vision written by and published by . This book was released on 2011 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research work is devoted to the development of original and robust control methods aiming at performing autonomous hover flights and navigation of a mini quad-rotor robotic helicopter. The vehicle is guided by an imaging system and a combination of inertial and altitude sensors, which allow a relative localization of the UAV with respect to its surrounding environment. The main idea of using this kind of sensor suit consists on enabling the robotic platform to perform autonomous tasks indoors as well as outdoors. The development of a real-time experimental platform is presented, consisting of a quad-rotor aerial vehicle and a supervisory ground station where imaging and control algorithms are executed. A control strategy for improving the attitude stabilization of the quad-rotor is proposed and tested in real-time experiments. The technique uses low cost compo-nents and an extra control loop based on motor armature current feedback. Two vision-based strategies are introduced, which allow the vehicle to localize it- self with respect an artificial landmark placed on ground. Such information together with a control strategy allows stabilizing the quad-rotor 3-dimensional position in real time experiments. A comparison of nonlinear controllers is addressed, with the objective of evaluating which control strategy is the most effective approach for stabilizing the quad-rotor when using visual feedback. For estimating the translational dynamics of the quad-rotor, imaging, inertial and altitude sensors are combined in a state observer. This sensing system allows the vehicle to estimate its relative position and translational velocity when evolving in unstructured, indoors, GPs-denied environments. Three different state observers are compared in real time experiments, aiming at obtaining the most effective approach for combining imaging and inertial sensors. Each state observer is tested in a real-time experiment, where the estimated states are used in a control strategy for stabilizing the vehicle's position during flight.

Download or read book Search and Rescue Robotics written by Multiple Authors and published by BoD – Books on Demand. This book was released on 2017-08-23 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the event of large crises (earthquakes, typhoons, floods, ...), a primordial task of the fire and rescue services is the search for human survivors on the incident site. This is a complex and dangerous task, which - too often - leads to loss of lives among the human crisis managers themselves. This book explains how unmanned search can be added to the toolkit of the search and rescue workers, offering a valuable tool to save human lives and to speed up the search and rescue process. The introduction of robotic tools in the world of search and rescue is not straightforward, due to the fact that the search and rescue context is extremely technology-unfriendly, meaning that very robust solutions, which can be deployed extremely quickly, are required. Multiple research projects across the world are tackling this problem and in this book, a special focus is placed on showcasing the results of the European Union ICARUS project on this subject. The ICARUS project proposes to equip first responders with a comprehensive and integrated set of unmanned search and rescue tools, to increase the situational awareness of human crisis managers, so that more work can be done in a shorter amount of time. The ICARUS tools consist of assistive unmanned air, ground, and sea vehicles, equipped with victim-detection sensors. The unmanned vehicles collaborate as a coordinated team, communicating via ad hoc cognitive radio networking. To ensure optimal human-robot collaboration, these tools are seamlessly integrated into the command and control equipment of the human crisis managers and a set of training and support tools is provided to them in order to learn to use the ICARUS system. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement number 285417. The publishing of this book was funded by the EC FP7 Post-Grant Open Access Pilot programme.