Computer ScienceScience & MathematicsEconomics & FinanceBusiness & ManagementPolitics & GovernmentHistoryPhilosophy

AI based Robot Safe Learning and Control

by Xuefeng Zhou, Zhihao Xu, Shuai Li, Hongmin Wu, Taobo Cheng, Xiaojing Lv

AI based Robot Safe Learning and Control

Subscribe to new books via dBooks.org telegram channel

Join
DescriptionTable of ContentsDetailsReport an issue

Book Description

This open book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors' papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

This open book is licensed under a Creative Commons License (CC BY). You can download AI based Robot Safe Learning and Control ebook for free in PDF format (9.3 MB).

Table of Contents

Chapter 1
Adaptive Jacobian Based Trajectory Tracking for Redundant Manipulators with Model Uncertainties in Repetitive Tasks
Chapter 2
RNN Based Trajectory Control for Manipulators with Uncertain Kinematic Parameters
Chapter 3
RNN Based Adaptive Compliance Control for Robots with Model Uncertainties
Chapter 4
Deep RNN Based Obstacle Avoidance Control for Redundant Manipulators
Chapter 5
Optimization-Based Compliant Control for Manipulators Under Dynamic Obstacle Constraints
Chapter 6
RNN for Motion-Force Control of Redundant Manipulators with Optimal Joint Torque

Book Details

Title
AI based Robot Safe Learning and Control
Subject
Engineering and Technology
Publisher
Springer
Published
2020
Pages
138
Edition
1
Language
English
ISBN13
9789811555022
ISBN10
9811555028
ISBN13 Digital
9789811555039
ISBN10 Digital
9811555036
PDF Size
9.3 MB
License
CC BY

Related Books

Statistical Learning and Sequential Prediction
This free book will focus on theoretical aspects of Statistical Learning and Sequential Prediction. Until recently, these two subjects have been treated separately within the learning community. The course will follow a unified approach to analyzing learning in both scenarios. To make this happen, we shall bring together ideas from probability and ...
Risk Communication for the Future
The conventional approach to risk communication, based on a centralized and controlled model, has led to blatant failures in the management of recent safety related events. In parallel, several cases have proved that actors not thought of as risk governance or safety management contributors may play a positive role regarding safety. Building on the...
The Models of Engaged Learning and Teaching
This book provides a practical philosophy for promoting students' sophisticated thinking from Early Childhood to PhD in ways that explicitly interconnect across the years of education. It will help teachers, academics and the broader learning and teaching community to understand and implement these connections by introducing a conceptual frame...
Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
This open book focuses on robot introspection, which has a direct impact on physical human - robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is...
Stability and Control of Linear Systems
This advanced textbook introduces the main concepts and advances in systems and control theory, and highlights the importance of geometric ideas in the context of possible extensions to the more recent developments in nonlinear systems theory. Although inspired by engineering applications, the content is presented within a strong theoretical framew...
Deep Learning with JavaScript
Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of t...