When a person picks up a metal part and clamps it in the chuck of a lathe, he begins with his arm, proceeds with his wrist and finishes with his fingers. The arm brings the part near the chuck. The wrist positions the part, giving it the proper orientation to slide in. After the part is inserted, the wrist and fingers make tiny corrections to ensure that it is correctly seated. Today's robot attempting the same operations is at a grave disadvantage if it has to make all motions with the arm. The following work investigates the use of robotic wrists and hands to help industrial robots perform the fine motions needed in a metal working cell. Chapters 1 and 2 are an introduction to the field and a review of previous investigations on related subjects. Little work has been done on grasping and fine manipulation with a robot hand or wrist, but the related subjects of robot arm dynamics and control have an extensive literature.

Numerous books have already been published specializing in one of the well known areas that comprise Mechatronics: mechanical engineering, electronic control and systems. The goal of this book is to collect state-of-the-art contributions that discuss recent developments which show a more coherent synergistic integration between the mentioned areas. The book is divided in three sections. The first section, divided into five chapters, deals with Automatic Control and Artificial Intelligence. The second section discusses Robotics and Vision with six chapters, and the third section considers Other Applications and Theory with two chapters.

This book constitutes the refereed proceedings of the First Robotic Grasping and Manipulation Challenge, RGMC 2016, held at IROS 2016, Daejeon, South Korea, in October 2016.The 13 revised full papers presented were carefully reviewed and are describing the rules, results, competitor systems and future directions of the inaugural competition. The competition was designed to allow researchers focused on the application of robot systems to compare the performance of hand designs as well as autonomous grasping and manipulation solutions across a common set of tasks. The competition was comprised of three tracks that included hand-in-hand grasping, fully autonomous grasping, and simulation.

This book provides a fundamental knowledge of robotic grasping and fixturing (RGF) manipulation. For RGF manipulation to become a science rather than an art, the content of the book is uniquely designed for a thorough understanding of the RGF from the multifingered robot hand grasp, basic fixture design principle, and evaluating and planning of robotic grasping/fixturing, and focuses on the modeling and applications of the RGF. Compared with existing publications, this volume concentrates more on abstract formulation, i.e. mathematical modeling of robotic grasping and fixturing. Thus, it will be a good reference text for academic researchers, manufacturing and industrial engineers and a textbook for engineering graduate students.The book provides readers an overall picture and scientific basis of RGF, the comprehensive information and mathematic models of developing and applying RGF in industry, and presents long term valuable information which is essential and can be used by technical professions as a good reference.

The subject of this paper forms part of a broader effort to model the mechanics of grasping and fine-manipulation for robots. Grasping is the act of acquiring and holding (gripping) an object. Fine-manipulation is an extension of grasping to include control of the object using an end-effector such as a gripper or a hand. A mechanical model of grasping and manipulation forms the basis for controlling grippers and paves the way for robots that can make independent judgments about how to pick up and handle the objects they encounter. In this paper a procedure is developed for computing physical properties with which a grasp may be described. Among these properties are grip strength, stability, compliance and mobility. The results depend strongly on the interaction between the gripping surfaces and the object. For example, a grasp may be unstable when the fingertips are pointed, but stable for rounded fingertips. The analysis suggests that particular kinds of sensory information are especially useful in controlling a grasp and supports the notion that general grasping rules of thumb can be identified for use by robots. Originator- supplied key words include: Mechanical properties, Three dimensional, Fingers, Kinematics.

A modern and unified treatment of the mechanics, planning, and control of robots, suitable for a first course in robotics.

This work describes the development of a class of dexterous robot hands that use steerable, continuously rotating fingertips to perform complex in-hand manipulation of grasped objects, a task that has alluded many widely used robot graspers. While a typical laboratory robot with a parallel-jaw gripper can perform basic pick-and-place tasks, they lack the fine manipulation proficiency needed for more demanding, precise and varied tasks. Inspired by the human hand, many research efforts are focused on creating and controlling human-like (anthropomorphic) hands in the hopes of duplicating human dexterity. One goal of these designs has been to enable robotic in-hand manipulation. Except for small motions, in-hand manipulation with these hands requires grasp-gaiting, a complex process where the fingers "walk" across the object, making and breaking contacts in order to propel the object to a desired pose, which can be an inefficient and difficult approach for a variety of tasks. So far, the fragility, cost, and complexity of these devices have precluded use outside of a laboratory setting. While we share with other researchers the goal of enabling in-hand manipulation, we achieve it by completely different means. We have developed a series of highly non-anthropomorphic grasping devices - the Roller Graspers - that use rotating fingertips to perform full six-degrees-of-freedom (DoF) in-hand manipulation on grasped objects. We do this by intelligently driving the fingertips across the object. The first Roller Grasper used steerable cylindrical fingertip rollers mounted on a pivoting linkage, for a total of three DoF for each of its three fingers. Using scripted motion control, it demonstrated the feasibility of our in-hand manipulation concept. The second and third versions used spherical fingertips which afforded better grasp stability and range of motion. These designs also supported our development of a hierarchical manipulation architecture that allowed the roller graspers to achieve autonomous in-hand manipulation. The manipulation architecture consisted of a sample-based high-level planner and a heuristic low-level policy that allowed the grasper to perform full 6-DoF manipulation of objects with a variety of shapes and sizes. The final version of our hand, called Tactile-Enabled Roller Grasper (TERG), incorporated a novel tactile sensing system that could extract the surface contour of a grasped object as well as the shear force applied at the contact location, even while the fingertips were rotating. This enabled more diverse and robust in-hand manipulation that was not possible in the previous generations.