Cognitive Reasoning For Compliant Robot Manipulation
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Cognitive Reasoning for Compliant Robot Manipulation
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
Informatics in Control, Automation and Robotics
The book focuses the latest endeavours relating researches and developments conducted in fields of Control, Robotics and Automation. Through more than ten revised and extended articles, the present book aims to provide the most up-to-date state-of-art of the aforementioned fields allowing researcher, PhD students and engineers not only updating their knowledge but also benefiting from the source of inspiration that represents the set of selected articles of the book. The deliberate intention of editors to cover as well theoretical facets of those fields as their practical accomplishments and implementations offers the benefit of gathering in a same volume a factual and well-balanced prospect of nowadays research in those topics. A special attention toward “Intelligent Robots and Control” may characterize another benefit of this book.
Probability, Combinatorics and Control
Author: Andrey Kostogryzov
language: en
Publisher: BoD – Books on Demand
Release Date: 2020-04-15
Probabilistic and combinatorial techniques are often used for solving advanced problems. This book describes different probabilistic modeling methods and their applications in various areas, such as artificial intelligence, offshore platforms, social networks, and others. It aims to educate how modern probabilistic and combinatorial models may be created to formalize uncertainties; to train how new probabilistic models can be generated for the systems of complex structures; to describe the correct use of the presented models for rational control in systems creation and operation; and to demonstrate analytical possibilities and practical effects for solving different system problems on each life cycle stage.