Mechatronics and Robotics: New Trends and Challenges

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
About the Editors
Contributors
Chapter 1 Mechatronics versus Robotics
	1.1 Mechatronics: Definitions and Evolution
	1.2 Mechatronics versus Robotics
	1.3 Mechatronics and Robotics: New Research Trends and Challenges
		1.3.1 Advanced Actuators for Mechatronics
		1.3.2 Advanced Sensors for Mechatronics
		1.3.3 Model-Based Control Techniques for Mechatronics
		1.3.4 Control and Manipulation
		1.3.5 Navigation, Environment Description, and Map Building
		1.3.6 Path Planning and Collision Avoidance
		1.3.7 Robot Programming
		1.3.8 Network Robotics
		1.3.9 Intelligent, Adaptive Humanoids for Human Assistance
		1.3.10 Advanced Sensors and Vision Systems
		1.3.11 Human–Robot Interaction
	References
Chapter 2 Advanced Actuators for Mechatronics
	2.1 Introduction
	2.2 Actuators Including Strain Wave Gearings and Applications to Precision
 Control
		2.2.1 Modeling of Angular Transmission Errors
		2.2.2 Feedforward Compensation in Precision Positioning
	2.3 Piezoelectric Actuators with Self-Sensing Techniques for Precision Stage
		2.3.1 System Configuration of Piezo-Driven Stage System
		2.3.2 Design of Plant System Including Bridge Circuit
		2.3.3 Minor Loop Controller Design Considering Vibration Suppression
		2.3.4 Experimental Verifications
	References
Chapter 3 Advanced Sensors for Mechatronics
	3.1 State of Art
	3.2 Advanced Vision-Based Control Applications
	3.3 Advanced Tactile Sensing
		3.3.1 Classification of Tactile Sensing
		3.3.2 Intrinsic Tactile Sensing
		3.3.3 Extrinsic Tactile Sensing
	3.4 Advanced Electroencephalography
	3.5 Advanced Human Motion Sensing
		3.5.1 Overview
		3.5.2 Recent Trends
		3.5.3 Activity Recognition as Child’s Motion Sensing
		3.5.4 For Further Consideration
	3.6 Advanced Approaches for Human Motion Detection
	References
Chapter 4 Model-Based Control for High-Tech Mechatronic Systems
	4.1 Introduction
		4.1.1 Motion Systems
		4.1.2 Industrial State of the Art
		4.1.3 Developments in Lithography
		4.1.4 Developments in Precision Motion Systems
		4.1.5 Towards Next-Generation Motion Control: The Necessity of a
 Model-Based Approach
		4.1.6 Contribution: From Manual Tuning to Model-Based Synthesis
	4.2 Motion Systems
	4.3 Feedback Control Design
		4.3.1 System Identification—Obtaining an FRF
		4.3.2 Loop-Shaping—the SISO Case
		4.3.3 Loop-Shaping—the MIMO Case
			4.3.3.1 Interaction Analysis
			4.3.3.2 Decoupling and Independent SISO Design
			4.3.3.3 Multi-loop Feedback Control Design with Robustness
 for Interaction
			4.3.3.4 Sequential Loop Closing
	4.4 Model-Based Control Design
		4.4.1 Standard Plant Approach
			4.4.1.1 Weighting Filter Selection for Performance Specification
			4.4.1.2 Obtaining a Nominal Model
			4.4.1.3 Uncertainty Modeling
		4.4.2 Case Study 1: Multivariable Robust Control
		4.4.3 Case Study 2: Overactuation and Oversensing
		4.4.4 Case Study 3: Inferential Control
	4.5 Conclusion
	4.6 Acknowledgment
	References
Chapter 5 Control and Manipulation
	5.1 Introduction
	5.2 Motion Control
		5.2.1 Joint Space Control
		5.2.2 Control of Robots with Elastic Joints
		5.2.3 Task Space Control
		5.2.4 Multi-Priority Control
	5.3 Force Control
		5.3.1 Interaction of the End Effector with the Environment
		5.3.2 Holonomic Constraints
		5.3.3 Hybrid Force/Motion Control
		5.3.4 Impedance Control
		5.3.5 Multi-Priority Interaction
	5.4 Future Directions and Recommended Reading
	References
Chapter 6 Navigation, Environment Description, and Map Building
	6.1 The Robotic Navigation Problem
		6.1.1 Where Am I Going
		6.1.2 How Do I Get There
			6.1.2.1 Robotic Path Planning
			6.1.2.2 Robotic Trajectory Following
		6.1.3 Where Have I Been? And Where Am I
	6.2 Environment Representations for Robotic Navigation
		6.2.1 Feature-Based Maps
		6.2.2 Occupancy Grid–Based Maps
	6.3 Simultaneous Localization and Mapping—SLAM
		6.3.1 SLAM: A Brief Overview
		6.3.2 SLAM Front-End
		6.3.3 SLAM Back-End
			6.3.3.1 Filtering Techniques
			6.3.3.2 Graph-Based Approaches
		6.3.4 SLAM: Problem Definition
		6.3.5 EKF-Based SLAM Algorithms
		6.3.6 Rao–Blackwellized Particle Filters–Based SLAM Algorithms
		6.3.7 Graph-Based SLAM Algorithms
		6.3.8 Loop Closure Detection and Verification
			6.3.8.1 Loop Closure Detection
			6.3.8.2 Loop Closure Verification
	6.4 Conclusions and Further Reading
	References
Chapter 7 Path Planning and Collision Avoidance
	7.1 State of the Art
	7.2 Curve Descriptions
		7.2.1 Polynomials
		7.2.2 Non-Uniform Rational Basis Splines (NURBS
	7.3 Geometric Path Planning
		7.3.1 Continuous Paths
			7.3.1.1 Geometric Primitives
			7.3.1.2 Clothoids
			7.3.1.3 Orientation Parametrization
		7.3.2 Point-to-Point Paths
	7.4 Dynamic Path Planning
		7.4.1 Explicit Description Using Motion Primitives
		7.4.2 Continuous Path Time-Optimal Motion
			7.4.2.1 Dynamic Programming and Numerical Integration
			7.4.2.2 Numerical Solution of the Optimal Control Problem
		7.4.3 Continous Path Time-Optimal Motion for Redundant Robots
		7.4.4 Point-to-Point Time-Optimal Motion
	7.5 Collision Avoidance
	References
Chapter 8 Robot Programming
	8.1 History and State-of-the-Art
	8.2 Software Engineering in Robotics: Goals, Objectives, Challenges
	8.3 Software Engineering Technologies Beneficial in Robotics
	8.4 The Step Change to Model-Driven Approaches in Robotics
	8.5 Composable Models and Software for Robotic Systems
		8.5.1 Ecosystem Tiers, Blocks, Ports, Connectors
		8.5.2 The Software Component Model
		8.5.3 Services, System Design, and Workflow
		8.5.4 Models, Data Sheets, and Dependency Graphs
		8.5.5 The Mixed Port Component as a Migration Path
		8.5.6 Interoperability, Coverage, and Conformance
	8.6 SmartMDSD Toolchain Version 3
	8.7 Future Opportunities: A Path to Sustainability
	References
Chapter 9 Network Robotics
	9.1 Introduction
	9.2 State of the Art
		9.2.1 Graph Theory and Consensus
		9.2.2 Cooperative Multi-Robot Behaviors
		9.2.3 Notation and Mathematical Operators
	9.3 Decentralized Trajectory Tracking for Multi-Robot Systems
		9.3.1 Model of the System
		9.3.2 Periodic Setpoint Definition
		9.3.3 Centralized Control Law for Setpoint Tracking
		9.3.4 Decentralized Implementation
			9.3.4.1 Decentralized Output Estimation
			9.3.4.2 Decentralized Independent Robot State Estimation
			9.3.4.3 Decentralized State Estimation and Control Strategy
	9.4 Simulations and Experiments
	9.5 Conclusions
	References
Chapter 10 Intelligent, Adaptive Humanoids for Human Assistance
	10.1 Humanoids—an Introduction
	10.2 Coman Humanoid—Design and Construction
		10.2.1 Compliant Actuation Unit
		10.2.2 Torso and Arm Design
		10.2.3 Leg, Hip, and Waist Design
		10.2.4 Onboard Processing
		10.2.5 Sensing
	10.3 Robot Interfaces
		10.3.1 Control Architecture
	10.4 Conclusions and Future Challenges
	References
Chapter 11 Advanced Sensors and Vision Systems
	11.1 Overview
	11.2 Visual Odometry
		11.2.1 Feature Point Descriptor
		11.2.2 Omnidirectional Vision–Based Rotation Estimation
		11.2.3 Vanishing Points for Rotation Estimation
	11.3 Advanced Sensors for Autonomous Navigation
		11.3.1 Laser Range Finder for Motion Estimation
		11.3.2 GPS for Rectifying Visual Odometry
	11.4 Intelligent Transportation Systems Based on a Combination of Multiple
 Sensors
	11.5 Discussion
	References
Chapter 12 Human–Robot Interaction
	12.1 Human–Robot Interaction
	References
Index