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"Robots Systèmes de commande."
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Distributed Control of Robotic Networks
This self-contained introduction to the distributed control of robotic networks offers a distinctive blend of computer science and control theory. The book presents a broad set of tools for understanding coordination algorithms, determining their correctness, and assessing their complexity; and it analyzes various cooperative strategies for tasks such as consensus, rendezvous, connectivity maintenance, deployment, and boundary estimation. The unifying theme is a formal model for robotic networks that explicitly incorporates their communication, sensing, control, and processing capabilities--a model that in turn leads to a common formal language to describe and analyze coordination algorithms. Written for first- and second-year graduate students in control and robotics, the book will also be useful to researchers in control theory, robotics, distributed algorithms, and automata theory. The book provides explanations of the basic concepts and main results, as well as numerous examples and exercises. Self-contained exposition of graph-theoretic concepts, distributed algorithms, and complexity measures for processor networks with fixed interconnection topology and for robotic networks with position-dependent interconnection topology Detailed treatment of averaging and consensus algorithms interpreted as linear iterations on synchronous networks Introduction of geometric notions such as partitions, proximity graphs, and multicenter functions Detailed treatment of motion coordination algorithms for deployment, rendezvous, connectivity maintenance, and boundary estimation
eBook
Modeling, identification & control of robots
Written by two of Europe's leading robotics experts, this book provides the tools for a unified approach to the modelling of robotic manipulators, whatever their mechanical structure.No other publication covers the three fundamental issues of robotics: modelling, identification and control.
eBook
Robotic Navigation and Mapping with Radar
Focusing on autonomous robotic applications, this cutting-edge resource offers you a practical treatment of short-range radar processing for reliable object detection at the ground level. This unique book demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles. It examines grid based robotic mapping with radar based on measurement likelihood estimation. You find detailed coverage of simultaneous localization and Map Building (SLAM) - an area referred to as the Holy Grailù of autonomous robotic research. The book derives an extended Kalman Filter SLAM algorithm which exploits the penetrating ability of radar. This algorithm allows for the observation of visually occluded objects, as well as the usual directly observed objects, which contributes to a robot 's position and the map state update. Moreover, you discover how the Random Finite Set (RFS) provides a more appropriate approach for representing radar based maps than conventional frameworks.
eBook
Adaptive control of robot manipulators
This book introduces an unified function approximation approach to the control of uncertain robot manipulators containing general uncertainties. It works for free space tracking control as well as compliant motion control. It is applicable to the rigid robot and the flexible joint robot. Even with actuator dynamics, the unified approach is still feasible. All these features make the book stand out from other existing publications.
eBook
Visual Perception and Control of Underwater Robots
This book covers theories and applications from aquatic visual perception and underwater robotics. Within the framework of visual perception for underwater operations, image restoration, binocular measurement, and object detection are addressed. More specifically, the book includes adversarial critic learning for visual restoration, NSGA-II-based calibration for binocular measurement, prior knowledge refinement for object detection, analysis of temporal detection performance, as well as the effect of the aquatic data domain on object detection.
With the aid of visual perception technologies, two up-to-date underwater robot systems are demonstrated. The first system focuses on underwater robotic operation for the task of object collection in sea. The other one is an untethered biomimetic robotic fish with a camera stabilizer and its control methods based on visual tracking. The authors provide a self-contained and comprehensive guide to understand underwater visual perception and control. Bridging the gap between theory and practice in underwater vision, the book features implementable algorithms, numerical examples, and tests, where codes are publicly available. Meanwhile, the mainstream technologies that are covered in the book include deep learning, adversarial learning, evolutionary computation, robust control, and underwater bionics.
Researchers, senior undergraduate and graduate students, and engineers dealing with underwater visual perception and control will benefit from the book.
eBook
Robot learning by visual observation
This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstractionDiscusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problemFocuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regressionConcentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert
eBook
Simultaneous localization and mapping
Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF).
eBook