內容大鋼
This book presents the latest advances for the frontier cross disciplinary field of robotics, intelligent control and learning. Sevenchapters are provided to cover the key common theories and technologies of robots, including the robot mapping and navigation, robotrecharging and smart power management, robot arm manipulation,unmanned vehicle control, intelligent manufacturing systems, etc.The book proposes a unique new perspective using time seriesprediction to control robots. Especially with the fast increasing ofvarious data in robotics, this new robot control mode using timeseries prediction has become very important. The book provides thecomplete cases for the most popular application scenes of robotpredictive control. By this first monograph on the topic of robot timeseries predictive control in the world, author provides importantreferences for the engineers, scientists and students in the field ofrobotics and artificial intelligence.
Hui LIU is Professor and Vice dean of the School of Traffic & Transportation Engineering, Central South University, China. His mainresearch interests include computational intelligence, intelligentrobotics in traffic & transportation engineering, and nonlinear signalmodeling & forecasting. He holds double Ph.D degrees from China(Traffic & Transportation Engineering, from Central South University in 2011) and Germany (Automation Engineering, from University of Rostock in 2013), and obtained his professorship degree inAutomation Engineering from University of Rostock in 2016. He haspublished more than 100 international research papers and authorized beyond 100 invention patents in the field of robotics, datascience, and time series predictive control, as the first inventor.
目錄
Preface
Abbreviations
CHAPTER 1 Introduction
1.1 Robotics and Control Technology
1.1.1 Robotics
1.1.2 Robotics Control Technology
1.2 Time Series Forecasting in Robotics Control
1.2.1 Time Series Forecasting Objectives
1.2.2 Time Series Forecasting Methods
1.3 Predictive Control in Robotics
1.3.1 Uncertainty Problems in Predictive Control of Robotics..
1.3.2 Model Predictive Control
1.3.3 Significance and Purpose of Research
1.4 Scope of This Book
References
CHAPTER 2 Robot Navigation Position Time Series Predictive Control
2.1 Introduction
2.2 Robot Navigation Position Time Series Measurement
2.3 Robot Navigation Position Time Series Uncertainty Analysis
2.4 Robot Navigation Position Time Series Statistical Forecasting Method
2.4.1 ARIMA Forecasting Algorithm
2.4.2 ARIMA-GARCH Forecasting Algorithm
2.5 Robot Navigation Position Time Series Intelligent Forecasting Method
2.5.1 RBF Neural Network Forecasting Algorithm
2.5.2 Elman Neural Network Forecasting Algorithm
2.5.3 Extreme Learning Machine Forecasting Algorithm
2.6 Robot Navigation Position Time Series Deep Learning Forecasting Method
2.6.1 LSTM Deep Neural Network Forecasting Algorithm
2.6.2 ESN Deep Neural Network Forecasting Algorithm
2.7 Comparative Analysis of Forecasting Performance
2.8 Robot Anti-Collision Monitoring and Control Based on Navigation Position Forecasting
2.9 Conclusions
References
CHAPTER 3 Mobile Robot Power Time Series Predictive Control
3.1 Introduction
3.2 Mobile Robot Power Time Series Measurement
3.3 Mobile Robot Power Time Series Uncertainty Analysis
3.4 Mobile Robot Power Time Series Statistical Forecasting Method
3.4.1 Experimental Design
3.4.2 Modeling Steps
3.4.3 Forecasting Results
3.5 Mobile Robot Power Time Series Intelligent Forecasting Method
3.5.1 Experimental Design
3.5.2 Modeling Steps
3.5.3 Forecasting Results
3.6 Mobile Robot Power Time Series Deep Learning Forecasting Method .
3.6.1 Experimental Design
3.6.2 Modeling Steps
3.6.3 Forecasting Results
3.7 Comparative Analysis of Forecasting Performance
3.7.1 Analysis of Statistical Methods
3.7.2 Analysis of Intelligent Methods
3.7.3 Analysis of Deep Learning Methods
3.8 Mobile Robot Delivery Process Control Based on Power Forecasting..
3.9 Conclusions
References
CHAPTER 4 Robot Arm Time Series Predictive Control
4.1 Introduction
4.2 Robot Arm Time Series Measurement
4.3 Robot Arm Time Series Uncertainty Analysis
4.4 Robot Arm Time Series Statistical Forecasting Method
4.4.1 Pandit-Wu Forecasting Algorithm
4.4.2 KF-ARMA Forecasting Algorithm
4.5 Robot Arm Time Series Intelligent Forecasting Method
4.5.1 RELM Forecasting Algorithm
4.5.2 XGBoost Forecasting Algorithm
4.5.3 GRNN Forecasting Algorithm
4.6 Robot Arm Time-Series Deep Learning Forecasting Method
4.6.1 Autoencoder Deep Neural Network Forecasting Algorithm
4.6.2 Deep Belief Network Forecasting Algorithm
4.7 Comparative Analysis of Forecasting Performance
4.7.1 Analysis of Statistical Methods
4.7.2 Analysis of Intelligent Methods
4.7.3 Analysis of Deep Learning Methods
4.8 Robot Arm Positioning Control Based on Arm Forecasting
4.9 Conclusions
References
CHAPTER 5 Unmanned Vehicle Time Series Predictive Control
5.1 Introduction
5.2 Unmanned Vehicle Time Series Measurement
5.3 Unmanned Vehicle Time Series Uncertainty Analysis
5.4 Unmanned Vehicle Time Series Statistical Forecasting Method
5.4.1 Kalman Filter Forecasting Algorithm
5.4.2 Fuzzy Time Series Forecasting Algorithm
5.5 Unmanned Vehicle Time Series Intelligent Forecasting Method
5.5.1 Elman Neural Network Forecasting Algorithm
5.5.2 NAR Neural Network Forecasting Algorithm
5.5.3 ANFIS Neural Network Forecasting Algorithm
5.6 Unmanned Vehicle Time Series Deep Learning Forecasting Method...
5.6.1 RNN Deep Neural Network Forecasting Algorithm
5.6.2 LSTM Deep Neural Network Forecasting Algorithm
5.6.3 GRU Deep Neural Network Forecasting Algorithm
5.7 Comparative Analysis of Forecasting Performance
5.7.1 Analysis of Statistical Methods
5.7.2 Analysis of Intelligent Methods
5.7.3 Analysis of Deep Learning Methods
5.8 Unmanned Vehicle Navigation Control Based on Multi-Source
Position Time Series Fusion
5.8.1 Unmanned Vehicle Fusion Positioning
5.8.2 Unmanned Vehicle Navigation Control
5.9 Unmanned Vehicle Charging Control Based on Multi-Source Power Time Series Fusion
5.10 Conclusions
References
CHAPTER 6 Wearable Assistive Robot Time Series Predictive Control
6.1 Introduction
6.2 Wearable Assistive Robot Time Series Measurement
6.3 Wearable Assistive Robot Time Series Uncertainty Analysis
6.4 Wearable Assistive Robot Time Series Statistical Forecasting Method.
6.4.1 Experimental Design
6.4.2 Modeling Step
6.4.3 Forecasting Results
6.5 Wearable Assistive Robot Time Series Intelligent Forecasting Method.
6.5.1 Experimental Design
6.5.2 Modeling Step
6.5.3 Forecasting Results
6.6 Wearable Assistive Robot Time-Series Deep Learning Forecasting Method
6.6.1 Experimental Design
6.6.2 Modeling Step
6.6.3 Forecasting Results
6.7 Comparative Analysis of Forecasting Performance
6.8 Wearable Assistive Robot Motion Control Based on Forecasting
6.9 Conclusions
References
CHAPTER 7 Intelligent Manufacturing Performance Prediction and Application
7.1 Introduction
7.2 Data Acquisition
7.2.1 Data-Driven Method
7.2.2 Model-Driven Method
7.3 Prediction Modeling
7.3.1 Regression Algorithms
7.3.2 Artificial Neural Network (ANN)
7.3.3 Comparison Analysis
7.4 Application
7.4.1 System Configuration
7.4.2 The Other Application
7.5 Conclusions
References
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