Название: Human-Aware Robotics: Modeling Human Motor Skills for the Design, Planning and Control of a New Generation of Robotic DevicesАвтор: Giuseppe Averta
Издательство: Springer
Серия: Springer Tracts in Advanced Robotics
Год: 2022
Страниц: 284
Язык: английский
Формат: pdf (true)
Размер: 10.0 MB
This book moves from a thorough investigation of human capabilities during movements and interactions with objects and environment and translates those principles into the design planning and control of innovative mechatronic systems, providing significant advancements in the fields of human–robot interaction, autonomous robots, prosthetics and assistive devices. The work presented in this monograph is characterized by a significant paradigmatic shift with respect to typical approaches, as it always place the human at the center of the technology developed, and the human represents the starting point and the actual beneficiary of the developed solutions.
The content of this book is targeted to robotics and neuroscience enthusiasts, researchers and makers, students and simple lovers of the matter.
Contents:
1. Introduction
Part I. Taming the Complexity of Human Motion Generation
2. Understanding the Principal Modes of Natural Movements in Temporal Domain
3. Quantifying the Time-Invariance Properties of Upper Limb Synergies
4. Evidences on the Hierarchical Control of Human Hands
Part II. On the Design of Nature-Inspired Prostheses and the Assessment of Motion Impairment
5. Using Nature-Inspired Principles to Design of Robotic Limbs: The Soft Wrist
6. A Novel Approach to Quantify Motion Impairment
7. A Novel Mechatronic System for Evaluating Elbow Muscular Spasticity Relying on Tonic Stretch Reflex Threshold Estimation
Part III. Transferring Human Principles to Cobots and Autonomous Robots
8. Natural Motion: Embedding Human-Likeliness in Robot Movements
9. A Focus on Motion Dynamics: Planning Impedance Behaviors in Physical Interaction
10. Learning from Humans How to Grasp: A Reactive-Based Approach
11. Learning from Humans How to Grasp: Enhancing the Reaching Strategy
12. Learning to Prevent Grasp Failure with Soft Hands: From On-Line Prediction to Dual-Arm Grasp Recovery
13. Dexterity Augmentation of Robotic Hands: A Study on the Kinetic Domain
14. Exploiting Principal Components for Robots Walking: An Approach for Sub-Optimal Locomotion
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