INTRODUCTION: Brief History. Multifingered Hands and Dextrous Manipulation. Outline of the Book. Bibliography. RIGID BODY MOTION: Rigid Body Transformations. Rotational Motion in R3. Rigid Motion in R3. Velocity of a Rigid Body. Wrenches and Reciprocal Screws. MANIPULATOR KINEMATICS: Introduction. Forward Kinematics. Inverse Kinematics. The Manipulator Jacobian. Redundant and Parallel Manipulators. ROBOT DYNAMICS AND CONTROL: Introduction. Lagrange's Equations. Dynamics of Open-Chain Manipulators. Lyapunov Stability Theory. Position Control and Trajectory Tracking. Control of Constrained Manipulators. MULTIFINGERED HAND KINEMATICS: Introduction to Grasping. Grasp Statics. Force-Closure. Grasp Planning. Grasp Constraints. Rolling Contact Kinematics. HAND DYNAMICS AND CONTROL: Lagrange's Equations with Constraints. Robot Hand Dynamics. Redundant and Nonmanipulable Robot Systems. Kinematics and Statics of Tendon Actuation. Control of Robot Hands. NONHOLONOMIC BEHAVIOR IN ROBOTIC SYSTEMS: Introduction. Controllability and Frobenius' Theorem. Examples of Nonholonomic Systems. Structure of Nonholonomic Systems. NONHOLONOMIC MOTION PLANNING: Introduction. Steering Model Control Systems Using Sinusoids. General Methods for Steering. Dynamic Finger Repositioning. FUTURE PROSPECTS: Robots in Hazardous Environments. Medical Applications for Multifingered Hands. Robots on a Small Scale: Microrobotics. APPENDICES: Lie Groups and Robot Kinematics. A Mathematica Package for Screw Calculus. Bibliography. Index Each chapter also includes a Summary, Bibliography, and Exercises

http://www.cds.caltech.edu/%7Emurray/books/MLS/pdf/mls94-complete.pdf

A Mathematical Introduction to Robotic Manipulation

Preface. 1. Introduction. 2. Rigid Motions and Homogeneous Transformations. 3. Forward and Inverse Kinematics. 4. Velocity Kinematics-The Jacobian. 5. Path and Trajectory Planning. 6. Independent Joint Control. 7. Dynamics. 8. Multivariable Control. 9. Force Control. 10. Geometric Nonlinear Control. 11. Computer Vision. 12. Vision-Based Control. Appendix A: Trigonometry. Appendix B: Linear Algebra. Appendix C: Dynamical Systems. Appendix D: Lyapunov Stability. Index.

/pdf/robot-modeling-and-control-2n3memrp6g.pdf

Robot Modeling and Control

Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.

http://www.gbv.de/dms/hebis-darmstadt/toc/120387220.pdf

Introduction to Autonomous Mobile Robots

Background and Purpose. Treatment of patients with impingement symptoms commonly includes exercises intended to restore “normal” movement patterns. Evidence that indicates the existence of abnormal patterns in people with shoulder pain is limited. The purpose of this investigation was to analyze glenohumeral and scapulothoracic kinematics and associated scapulothoracic muscle activity in a group of subjects with symptoms of shoulder impingement relative to a group of subjects without symptoms of shoulder impingement matched for occupational exposure to overhead work. Subjects. Fifty-two subjects were recruited from a population of construction workers with routine exposure to overhead work. Methods. Surface electromyographic data were collected from the upper and lower parts of the trapezius muscle and from the serratus anterior muscle. Electromagnetic sensors simultaneously tracked 3-dimensional motion of the trunk, scapula, and humerus during humeral elevation in the scapular plane in 3 hand-held load conditions: (1) no load, (2) 2.3-kg load, and (3) 4.6-kg load. An analysis of variance model was used to test for group and load effects for 3 phases of motion (31°–60°, 61°–90°, and 91°–120°). Results. Relative to the group without impingement, the group with impingement showed decreased scapular upward rotation at the end of the first of the 3 phases of interest, increased anterior tipping at the end of the third phase of interest, and increased scapular medial rotation under the load conditions. At the same time, upper and lower trapezius muscle electromyographic activity increased in the group with impingement as compared with the group without impingement in the final 2 phases, although the upper trapezius muscle changes were apparent only during the 4.6-kg load condition. The serratus anterior muscle demonstrated decreased activity in the group with impingement across all loads and phases. Conclusion and Discussion. Scapular tipping (rotation about a medial to lateral axis) and serratus anterior muscle function are important to consider in the rehabilitation of patients with symptoms of shoulder impingement related to occupational exposure to overhead work.

https://ir.uiowa.edu/cgi/viewcontent.cgi?article=1000&context=oeh_pubs

Alterations in Shoulder Kinematics and Associated Muscle Activity in People With Symptoms of Shoulder Impingement

Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this article, we conduct a thorough survey to fully understand FSL. Starting from a formal definition of FSL, we distinguish FSL from several relevant machine learning problems. We then point out that the core issue in FSL is that the empirical risk minimizer is unreliable. Based on how prior knowledge can be used to handle this core issue, we categorize FSL methods from three perspectives: (i) data, which uses prior knowledge to augment the supervised experience; (ii) model, which uses prior knowledge to reduce the size of the hypothesis space; and (iii) algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space. With this taxonomy, we review and discuss the pros and cons of each category. Promising directions, in the aspects of the FSL problem setups, techniques, applications, and theories, are also proposed to provide insights for future research.1

Generalizing from a Few Examples: A Survey on Few-shot Learning

Introduction to robotics: Mechanics and control

A computer is used for generating a part inspection plan for a coordinate measuring machine (CMM), in a feature-based rapid design system (RDS), having a Feature-Based Design Environment (FBDE), an Episodal Associative Memory (EAM), Fabrication Planning (FAB), and an Inspection Plan (INSP), with features which include form features (D1) which define the form or shape of the part, manufacturing features (D2), inspection features (D3), and geometric and design (GD&T) features (D4). The Inspection Plan (INSP) includes interaction means wherein the inspector interacts with the system to guide it to a desired result, and the inspector can define setups, measurement points, sequence for the points, and the via points. For the inspector's sequence input, a learning process is included, so that the inspector's desired sequence is sent to discovery means for organizing patterns and defining rules, which by recalling relevant past experiences, and learning from the inspector's input, creates a "self-improving" expert system.

Automated process planning for quality control inspection

A surface of a 3-D object is digitized automatically and stored in a memory (88). Based on the signal representation, the 3-D object areas of the surface are defined for color and boundaries. Coatings application paths are defined within each area and various parameters for applying coatings for each point in each path are also defined including spray pattern, amount of coatings to be applied, speed of application along the path, among others; for each area, the points of the selected path are correlated with the angle of attack, spray pattern, amount of coatings and speed. A set of programmed instructions are compiled for execution in a general purpose computer. An assembly line of objects to be painted may then be set up and the retrieved instructions executed.

Automated three-dimensional precision coatings application apparatus

A surface of a 3-D object is digitized automatically and stored in a memory. Based on the signal representation of the 3-D object areas of the surface are defined for color and boundaries. Coatings application paths are defined within each area and various parameters for applying coatings for each point in each path are also defined including spray pattern, amount of coatings to be applied, speed of application along the path, among others; for each area, the points of the selected path are correlated with the angle of attack, spray pattern, amount of coatings and speed. A set of programmed instructions are compiled for execution in a general purpose computer. An assembly line of objects to be painted may then be set up and the retrieved instructions executed.

Automated three-dimensional precision effector method

This paper describes polysilicon micromotors with single and pyramidal diffraction grating elements fabricated on the polished surface of large-area rotors for optical scanning applications. While taking full advantage of planar processing, such scanners have high-quality scan profiles, good efficiency, meter working distances, and multiple out of plane beam diffraction orders. Chemical-mechanical polishing was used to reduce the 5-/spl mu/m-thick polysilicon rotors' average surface roughness from 420 /spl Aring/ to below 17 /spl Aring/, with less than 1500-/spl Aring/ film removal, improving the optical performance of the gratings as well as the definition, delineation, and side wall quality of the device features. Self-assembled monolayers (SAM) were found to improve the overall micromotor's dynamic performance. SAM-coated scanners could operate at voltages as low as 15 V and maximum operational speeds of 5200 rpm. The gratings were tested optically at 633-nm wavelength and were verified to have spatial periods of 1.80 and 3.86 /spl mu/m, closely matching their design values. Stepping and continuous mode dynamic operation of the scanners was demonstrated with visible diffraction orders at meter distances away.

Francis L. Merat

Papers

Introduction to robotics: Mechanics and control

Automated process planning for quality control inspection

Automated three-dimensional precision coatings application apparatus

Automated three-dimensional precision effector method

Diffraction grating scanners using polysilicon micromotors