Biologically Inspired Series-Parallel Hybrid Robots

Design, Analysis, and Control

1st Edition - November 27, 2024
Authors: Shivesh Kumar, Andreas Mueller, Frank Kirchner
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 8 4 8 2 - 2
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 8 4 8 3 - 9

Biologically Inspired Series-Parallel Hybrid Robots: Design, Analysis and Control provides an extensive review of the state-of-the-art in series-parallel hybrid robots, covering… Read more

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Biologically Inspired Series-Parallel Hybrid Robots: Design, Analysis and Control provides an extensive review of the state-of-the-art in series-parallel hybrid robots, covering all aspects of their mechatronic system design, modelling, and control. This book highlights the modular and distributed aspects of their mechanical, electronics, and software design, introducing various modern methods for modelling the kinematics and dynamics of complex robots. These methods are also introduced in the form of algorithms or pseudo-code which can be easily programmed with modern programming languages. Presenting case studies on various popular series-parallel hybrid robots which will inspire new robot developers, this book will be especially useful for academic and industrial researchers in this exciting field, as well as graduate-level students to bring them closer to the latest technology in mechanical design and control aspects of the area.

Title of Book
Cover image
Title page
Table of Contents
Copyright
List of contributors
Preface
Part 1: Introduction
Chapter 1: Motivation
1.1. Introduction
1.2. Series-parallel hybrid robots
1.3. Complexity in their modeling and control
1.4. Structure
Chapter 2: Modular and decentralized design principles and applications
2.1. Series-parallel hybrid designs
2.1.1. Humanoids
2.1.2. Multi-legged robots
2.1.3. Exoskeletons
2.1.4. Industrial automation
2.2. Modular and distributive aspects
2.2.1. Hardware
2.2.2. Software
2.3. Modeling and control
2.3.1. Modeling
2.3.2. Kinematics
2.3.3. Dynamics
2.3.4. Control
2.3.5. Adopted practices
2.4. Conclusion
Part 2: Geometric analysis
Chapter 3: Methods for geometric analysis of parallel mechanisms
3.1. Problem description
3.2. Algebraic geometry
3.2.1. Study's kinematic mapping
3.2.2. Tangent half-angle substitution
3.2.3. Towards global kinematics
3.3. Screw theory and Lie group methods
3.3.1. Fundamentals of screw theory
3.3.2. Matrix exponential and matrix logarithm maps
3.3.3. Screw representation of joint motion
3.3.4. Towards local analysis
3.4. Example: Lambda mechanism
3.4.1. Mobility analysis
3.4.2. Geometric analysis
3.5. Conclusion
Chapter 4: 2-DOF orientational parallel mechanisms
4.1. Introduction
4.2. Architecture and constraint equations
4.2.1. Ankle (2SP_RR+1U) and torso (2SP_U+1U) mechanism
4.2.2. Wrist mechanism (2SP_U+2RSU+1U)
4.3. Solving forward and inverse kinematics
4.3.1. Ankle (2SP_RR+1U) and torso (2SP_U+1U) mechanism
4.3.2. Wrist mechanism (2SP_U+2RSU+1U)
4.4. Workspace, singularity, and performance analysis
4.4.1. Ankle (2SP_RR+1U) and torso (2SP_U+1U) mechanism
4.4.2. Wrist mechanism (2SP_U+2RSU+1U)
4.4.3. Comparison between 2SP_RR+1U and 2SP_U+1U designs
4.4.4. Comparison between 2SP_U+2RSU+1U and 2SP_U+1U designs
4.5. Conclusion
Chapter 5: 3-DOF orientational parallel mechanism
5.1. Introduction
5.2. Mechanism's design description
5.2.1. Type synthesis
5.2.2. Design and construction
5.2.3. Topology and general mobility
5.2.4. Design features
5.2.5. Design comparison
5.3. Mechanism architecture and constraint equations
5.4. Inverse kinematics
5.4.1. Inverse kinematics
5.4.2. Rotative inverse kinematic model
5.5. Conclusion
Part 3: Kinematics, dynamics, and control
Chapter 6: Kinematics and dynamics of tree type systems
6.1. Graph based topological description
6.1.1. Numbering scheme for topological graphs
6.1.2. Representation of topological graphs
6.2. Recursive kinematics computation
6.2.1. Position of a kinematic chain
6.2.2. Velocity of a kinematic chain
6.2.3. Acceleration of a kinematic chain
6.3. Dynamics of a single rigid body
6.3.1. Physical properties of a rigid body
6.3.2. Classical formulation
6.3.3. Twist-wrench formulation
6.4. Inverse dynamics
6.4.1. Initialization
6.4.2. Forward recursion
6.4.3. Backward recursion
6.4.4. Computational complexity
6.5. EOM in closed form
6.6. Conclusion
Chapter 7: Modular algorithms for kinematics and dynamics of series-parallel hybrid robots
7.1. Modeling rigid-body systems with closed loops
7.1.1. Loop constraints
7.1.2. Equations of motion (EOM)
7.1.3. Loop closure functions
7.1.4. Forward and inverse dynamics
7.1.5. Comparison of numerical and analytical resolution of loop constraints: case study of a 3D slider crank mechanism
7.2. Notion of modularity
7.2.1. Definition of submechanism module
7.2.2. Guidelines for selection
7.3. Topological modeling
7.3.1. Bottom-up composition
7.3.2. Top-down decomposition
7.4. Kinematics and dynamics
7.4.1. Submechanism module
7.4.2. Series-parallel hybrid composition
7.4.3. Computational effort
7.5. Conclusion
Chapter 8: Forward dynamics with constraint embedding for dynamic simulation
8.1. Introduction
8.2. Articulated body inertia
8.2.1. Calculation of articulated body inertia
8.3. Articulated body algorithm
8.4. Recursive forward dynamics using constraint embedding
8.4.1. Strategy for constraint embedding
8.4.2. ABA for forward dynamics of closed loops
8.4.3. Mass matrix factorization and inversion
8.5. Example
8.6. Validation of the constraint embedding approach
8.7. Conclusion
Chapter 9: Whole-body control
9.1. Motivation
9.2. Whole-body control architecture
9.2.1. Velocity-based WBC
9.2.2. Acceleration-based WBC
9.3. Experimental results
9.3.1. Application of box constraints in actuation space
9.3.2. Computational performance
9.4. Discussion and outlook
Chapter 10: Whole-body trajectory optimization
10.1. Introduction
10.2. Mathematical background
10.2.1. Constrained multi-body dynamics
10.2.2. Trajectory optimization formulation
10.3. Experimental design
10.3.1. Closed-loop mechanisms in RH5 Manus robot
10.3.2. Box constraints
10.3.3. Tree abstraction of RH5 Manus
10.3.4. Optimal control formulation
10.4. Results
10.4.1. Tree abstraction model vs full hybrid model
10.4.2. Experimental results
10.4.3. Computational timings
10.5. Discussion and conclusion
Part 4: Case studies on mechatronic system design
Chapter 11: Charlie, a hominidae walking robot
11.1. Introduction
11.1.1. Motivation
11.1.2. Biological inspiration
11.1.3. Application scenarios
11.2. Mechatronic system design
11.2.1. Mechanical design
11.2.2. Electronics design
11.2.3. Decentralized control architecture
11.3. Modeling and control
11.3.1. Kinematic modeling
11.3.2. Low-level control
11.3.3. Motion control
11.3.4. Guidance and navigation
11.4. Conclusion and outlook
11.4.1. Success stories
11.4.2. Design limitations or lessons learned
11.4.3. Future work
Chapter 12: Multi-legged robot Mantis
12.1. Introduction
12.1.1. Problem description
12.1.2. Biological inspiration
12.1.3. Application scenarios
12.2. Mechatronic system design
12.2.1. Mechanical design
12.2.2. Parallel kinematics
12.2.3. Electronics design
12.2.4. Software design
12.3. Modeling and control
12.3.1. Modeling
12.3.2. Low level control
12.3.3. Mid level control
12.3.4. High level control
12.4. Conclusion and outlook
12.4.1. Success stories
12.4.2. Design limitations and lessons learned
12.4.3. Future work
Chapter 13: Sherpa, a family of wheeled-leg rovers
13.1. Introduction
13.1.1. Problem description
13.1.2. Biological inspiration
13.1.3. Application scenarios
13.2. Mechatronic system design
13.2.1. Mechanical design
13.2.2. Electronic design
13.2.3. Software design
13.3. Modeling and control
13.3.1. Modeling
13.3.2. Low-level control
13.3.3. Mid-level control
13.3.4. High-level control
13.4. Conclusion and outlook
13.4.1. Success stories
13.4.2. Design limitations and lessons learned
13.4.3. Future work
Chapter 14: Recupera exoskeletons
14.1. Introduction
14.1.1. Motivation for series-parallel hybrid design
14.1.2. Application scenarios
14.2. Mechatronic system design
14.2.1. Mechanical design
14.2.2. Electrical and electronic design
14.3. Modeling and control
14.3.1. Modular robot description models
14.3.2. Kinematics and dynamics
14.3.3. Exoskeleton control
14.4. Results and discussion
14.4.1. Gravity compensations mode
14.4.2. Rehabilitation
14.4.3. Teleoperation
14.4.4. Comparison with similar exoskeleton systems
14.5. Conclusion and outlook
Chapter 15: RH5 Pedes humanoid
15.1. Introduction
15.1.1. Problem description
15.1.2. Application scenarios
15.2. Mechatronic system design
15.2.1. Mechanical design
15.2.2. Electronic design
15.3. Modeling and control
15.3.1. Robot modeling and control based on DDP
15.4. Results and discussion
15.4.1. Results
15.4.2. Discussion
15.5. Conclusion
Chapter 16: ARTER: a walking excavator robot
16.1. Introduction
16.1.1. Problem description
16.1.2. Biological inspiration
16.1.3. Application scenarios
16.2. Mechatronic system design
16.2.1. Mechanical design
16.2.2. Electronic design
16.2.3. Software design
16.3. Modeling and control
16.3.1. Modeling
16.3.2. Low-level control
16.3.3. Mid-level control
16.3.4. High-level control
16.4. Conclusion and outlook
16.4.1. Success stories
16.4.2. Design limitations
16.4.3. Lessons learned
16.4.4. Future work
Part 5: Software and outlook
Chapter 17: Phobos: creation and maintenance of complex robot models
17.1. Model information
17.1.1. Kinematic properties
17.1.2. Geometric information
17.1.3. Closing the gap between design and hardware
17.1.4. Annotating complex components
17.1.5. Additional information
17.1.6. Modular approach for complex robots
17.2. Model formats
17.2.1. URDF & SDF
17.2.2. SMURF
17.2.3. Scenes & assemblies
17.3. Blender add-on
17.4. API & tools
17.4.1. API
17.4.2. Command line tools
17.5. Continuous integration
17.5.1. CI-pipeline-setup
17.5.2. Processing workflow
17.5.3. Test for model integrity & consistency
17.5.4. Model deployment
17.6. Conclusion & outlook
Chapter 18: HyRoDyn: Hybrid Robot Dynamics
18.1. Motivation
18.1.1. Developer workflow
18.1.2. User workflow
18.2. SMURF: robot description for HyRoDyn
18.2.1. Parallel submechanisms with known analytic solutions
18.2.2. Generic parallel submechanism
18.2.3. Example: RH5 Manus
18.3. Phobos: visual editor for HyRoDyn
18.3.1. Top-down modeling
18.3.2. Bottom-up modeling
18.4. HyRoDyn software library
18.4.1. Submechanism libraries
18.4.2. Computational performance
18.5. Integration in middleware
18.5.1. Interfacing with other components
18.5.2. Examples
18.6. Automated robot analysis using HyRoDynPy
18.7. Conclusion
Chapter 19: Design of a flexible bio-inspired robot for inspection of pipelines
19.1. Introduction
19.2. Rigid bio-inspired piping inspection robot—an overview
19.3. Design, analysis, and synthesis of a tensegrity mechanism
19.3.1. Identification of optimal design parameters
19.3.2. Singularity and workspace analysis
19.3.3. Prototyping and control of the tensegrity mechanism
19.4. Optimal design of the bio-inspired robot
19.4.1. First optimization problem
19.4.2. Second optimization problem
19.5. Static force modeling of the flexible robot assembly
19.6. Conclusions and future works
Chapter 20: Optimization of parallel mechanisms with joint limits and collision constraints
20.0. Erratum
20.1. Design considerations in PKM optimization
20.1.1. Objective function
20.1.2. Constraints
20.2. Proposed algorithm for mechanism optimization
20.2.1. Local search algorithm: the NM (NM) algorithm
20.2.2. Global search algorithm
20.2.3. Cascade optimization
20.3. Results and discussion
20.3.1. 1-DOF lambda mechanism
20.3.2. 2-DOF RCM mechanism
20.3.3. Dimension reduction
20.4. Conclusions
Index

Shivesh Kumar

Shivesh Kumar is an assistant professor at the Division of Dynamics, Department of Mechanics and Maritime Sciences, Chalmers University of Technology in Gothenburg, Sweden. He is also a visiting researcher at the Robotics Innovation Center, German Research Center for Artificial Intelligence in Bremen, Germany. He obtained his PhD degree from the faculty of Mathematics and Computer Science at the University of Bremen (2019). His research interests include kinematics, dynamics, and control of robots with applications in the fields of exoskeletons, humanoids, rehabilitation, and industrial automation.

Affiliations and expertise

Researcher and Team leader, Mechanics and Control team, Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany

Andreas Mueller

Andreas Mueller obtained diploma degrees in mathematics, electrical engineering, and mechanical engineering, and a PhD in mechanics. He received his Habilitation in mechanics and is currently professor and director of the Institute of Robotics at the Johannes Kepler University, Linz, Austria. His current research interests include holistic modelling, model-based and optimal control of mechatronic systems, redundant robotic systems, parallel kinematic machines, biomechanics, and computational dynamics.

Affiliations and expertise

Professor, Robotics, Johannes Kepler University, Linz, Austria

Frank Kirchner

Frank Kirchner studied computer science and neurobiology at the University Bonn, where he received his PhD degree in computer science. He was senior scientist at the Gesellschaft für Mathematik und Datenverarbeitung (GMD) in Sankt Augustin, Germany, and a Senior Scientist at the Department for Electrical Engineering at Northeastern University in Boston, USA. Dr. Kirchner was first appointed adjunct and then tenure track assistant professor at the Northeastern University, and then as a full professor at the University of Bremen. Since December 2005, Dr. Kirchner has also been director of the Robotics Innovation Centre (RIC) in Bremen.

Affiliations and expertise

Professor, University of Bremen; Director, Robotics Innovation Center (RIC), Bremen, Germany

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Biologically Inspired Series-Parallel Hybrid Robots

Design, Analysis, and Control

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