Visual Perception And Robotic Manipulation
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Author | : Geoffrey Taylor |
Publisher | : Springer |
Total Pages | : 231 |
Release | : 2008-08-18 |
Genre | : Technology & Engineering |
ISBN | : 3540334556 |
This book moves toward the realization of domestic robots by presenting an integrated view of computer vision and robotics, covering fundamental topics including optimal sensor design, visual servo-ing, 3D object modelling and recognition, and multi-cue tracking, emphasizing robustness throughout. Covering theory and implementation, experimental results and comprehensive multimedia support including video clips, VRML data, C++ code and lecture slides, this book is a practical reference for roboticists and a valuable teaching resource.
Author | : Pedram Azad |
Publisher | : Springer Science & Business Media |
Total Pages | : 273 |
Release | : 2009-11-19 |
Genre | : Technology & Engineering |
ISBN | : 3642042295 |
Dealing with visual perception in robots and its applications to manipulation and imitation, this monograph focuses on stereo-based methods and systems for object recognition and 6 DoF pose estimation as well as for marker-less human motion capture.
Author | : Daniel Sebastian Leidner |
Publisher | : Springer |
Total Pages | : 186 |
Release | : 2018-12-08 |
Genre | : Technology & Engineering |
ISBN | : 3030048586 |
In order to achieve human-like performance, this book covers the four steps of reasoning a robot must provide in the concept of intelligent physical compliance: to represent, plan, execute, and interpret compliant manipulation tasks. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. It is investigated how symbolic task descriptions can be translated into meaningful robot commands.Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands the humanoid robot Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment
Author | : Dinh-Cuong Hoang |
Publisher | : |
Total Pages | : |
Release | : 2021 |
Genre | : |
ISBN | : |
Author | : Alexandros Iosifidis |
Publisher | : Academic Press |
Total Pages | : 638 |
Release | : 2022-02-04 |
Genre | : Computers |
ISBN | : 0323885721 |
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Author | : Jian Chen |
Publisher | : CRC Press |
Total Pages | : 361 |
Release | : 2018-06-14 |
Genre | : Computers |
ISBN | : 042995123X |
This book describes visual perception and control methods for robotic systems that need to interact with the environment. Multiple view geometry is utilized to extract low-dimensional geometric information from abundant and high-dimensional image information, making it convenient to develop general solutions for robot perception and control tasks. In this book, multiple view geometry is used for geometric modeling and scaled pose estimation. Then Lyapunov methods are applied to design stabilizing control laws in the presence of model uncertainties and multiple constraints.
Author | : Jürgen Sturm |
Publisher | : Springer |
Total Pages | : 216 |
Release | : 2013-12-12 |
Genre | : Technology & Engineering |
ISBN | : 3642371604 |
This book presents techniques that enable mobile manipulation robots to autonomously adapt to new situations. Covers kinematic modeling and learning; self-calibration; tactile sensing and object recognition; imitation learning and programming by demonstration.
Author | : Anibal Ollero |
Publisher | : Springer |
Total Pages | : 385 |
Release | : 2019-06-27 |
Genre | : Technology & Engineering |
ISBN | : 3030129454 |
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.
Author | : David Israel González Aguirre |
Publisher | : Springer |
Total Pages | : 253 |
Release | : 2018-09-01 |
Genre | : Technology & Engineering |
ISBN | : 3319978411 |
This book provides an overview of model-based environmental visual perception for humanoid robots. The visual perception of a humanoid robot creates a bidirectional bridge connecting sensor signals with internal representations of environmental objects. The objective of such perception systems is to answer two fundamental questions: What & where is it? To answer these questions using a sensor-to-representation bridge, coordinated processes are conducted to extract and exploit cues matching robot’s mental representations to physical entities. These include sensor & actuator modeling, calibration, filtering, and feature extraction for state estimation. This book discusses the following topics in depth: • Active Sensing: Robust probabilistic methods for optimal, high dynamic range image acquisition are suitable for use with inexpensive cameras. This enables ideal sensing in arbitrary environmental conditions encountered in human-centric spaces. The book quantitatively shows the importance of equipping robots with dependable visual sensing. • Feature Extraction & Recognition: Parameter-free, edge extraction methods based on structural graphs enable the representation of geometric primitives effectively and efficiently. This is done by eccentricity segmentation providing excellent recognition even on noisy & low-resolution images. Stereoscopic vision, Euclidean metric and graph-shape descriptors are shown to be powerful mechanisms for difficult recognition tasks. • Global Self-Localization & Depth Uncertainty Learning: Simultaneous feature matching for global localization and 6D self-pose estimation are addressed by a novel geometric and probabilistic concept using intersection of Gaussian spheres. The path from intuition to the closed-form optimal solution determining the robot location is described, including a supervised learning method for uncertainty depth modeling based on extensive ground-truth training data from a motion capture system. The methods and experiments are presented in self-contained chapters with comparisons and the state of the art. The algorithms were implemented and empirically evaluated on two humanoid robots: ARMAR III-A & B. The excellent robustness, performance and derived results received an award at the IEEE conference on humanoid robots and the contributions have been utilized for numerous visual manipulation tasks with demonstration at distinguished venues such as ICRA, CeBIT, IAS, and Automatica.
Author | : Peter Raymond Florence |
Publisher | : |
Total Pages | : 141 |
Release | : 2020 |
Genre | : |
ISBN | : |
We would like to have highly useful robots which can richly perceive their world, semantically distinguish its fine details, and physically interact with it sufficiently for useful robotic manipulation. This is hard to achieve with previous methods: prior work has not equipped robots with the scalable ability to understand the dense visual state of their varied environments. The limitations have both been in the state representations used, and how to acquire them without significant human labeling effort. In this thesis we present work that leverages self-supervision, particularly via a mix of geometrical computer vision, deep visual learning, and robotic systems, to scalably produce dense visual inferences of the world state. These methods either enable robots to teach themselves dense visual models without human supervision, or they act as a large multiplying factor on the value of information provided by humans. Specifically, we develop a pipeline for providing ground truth labels of visual data in cluttered and multi-object scenes, we introduce the novel application of dense visual object descriptors to robotic manipulation and provide a fully robot-supervised pipeline to acquire them, and we leverage this dense visual understanding to efficiently learn new manipulation skills through imitation. With real robot hardware we demonstrate contact-rich tasks manipulating household objects, including generalizing across a class of objects, manipulating deformable objects, and manipulating a textureless symmetrical object, all with closed-loop, real-time vision-based manipulation policies.