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

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

A survey of multi-agent formation control

There is an increasing interest in studying control systems employing multiple sensors and actuators that are geographically distributed. Communication is an important component of these distributed and networked control systems. Hence, there is a need to understand the interactions between the control components and the communication components of the distributed system. In this paper, we formulate a control problem with a communication channel connecting the sensor to the controller. Our task involves designing the channel encoder and channel decoder along with the controller to achieve different control objectives. We provide upper and lower bounds on the channel rate required to achieve these different control objectives. In many cases, these bounds are tight. In doing so, we characterize the "information complexity" of different control objectives.

/pdf/control-under-communication-constraints-4wmocza5m6.pdf

Control under communication constraints

A current survey of the emerging field of networked control systems is provided. The aim is to introduce the fundamental issues involved in designing successful networked control systems, to provide a snapshot assessment of the current state of research in the field, to suggest useful future research directions, and to provide a broad perspective on recent fundamental results. Reflecting the goals of the Special Issue itself, this paper surveys relevant work from the areas of systems and control, signal processing, detection and estimation, data fusion, and distributed systems. We discuss appropriate network architectures, topics such as coding for robustly stable control in the presence of time-varying channel capacity, channels with fixed versus adaptively variable data width, issues in data rate problems in nonlinear feedback problems, and problems in routing for stability and performance. In surveying current research on networked control systems, we find that recent theoretical advances and target applications are intimately intertwined. The common goal of papers in the Special Issue which follows is to describe key aspects of this relationship. We also aim to provide a bridge between networked control systems and closely related contemporary work dealing with sensor networks and wireless communication protocols

Control and Communication Challenges in Networked Real-Time Systems

This special issue provides information on current and future research directions in the emerging field of networked control systems. The first paper provides a comprehensive overview of the field. The remaining 11 papers are organized in three sections: a section on the "current state of the technology of networked control systems"; a section on foundations of networked real-time systems"; and a section on "wireless networks - the backbone of networked control systems." Each of the papers is briefly discussed.

Special Issue on Technology of Networked Control Systems

In the growing literature on redundant manipulator control, a number of techniques have been proposed for solving the inverse kinemetics problem. Some of these techniques are surveyed with a discussion of strengths and weaknesses of each. A new approach, called the extended Jacobian technique, is also presented. It is argued that because this technique may be expected to lift closed end effector paths to closed joint angle paths, it provides a promising approach for the control of kinematically redundant industrial manipulators. It is further shown that this technique may be implemented as a suitably parameterized generalized inverse method.

Kinematic programming alternatives for redundant manipulators

Guest Editorial Special Issue on Networked Control Systems

In this paper, we introduce two distinct types of nonlinear dynamical systems, /spl Tscr//sub 1/ and /spl Tscr//sub 2/, both of which possess a triangular structure. It is shown that all systems belonging to /spl Tscr//sub 1/ can be made stable and that if they belong to a subclass /spl Tscr//sub 1s/, the stability holds globally. A precise characterization of the general class of nonlinear systems transformable to /spl Tscr//sub 1/ is carried out. The second class, /spl Tscr//sub 2/, corresponds to a set of second-order nonlinear differential equations and is motivated by problems that occur in mechanical systems. It is shown that global tracking can be achieved for all systems in /spl Tscr//sub 2/. A constructive approach is used in all cases to develop the adaptive controller, and both stabilization and tracking are shown to be realizable. Simple examples are given to illustrate the different classes of nonlinear systems as well as the idea behind the approach used to stabilize them. >

John Baillieul

Papers

Control and Communication Challenges in Networked Real-Time Systems

Special Issue on Technology of Networked Control Systems

Kinematic programming alternatives for redundant manipulators

Guest Editorial Special Issue on Networked Control Systems

Adaptive control of nonlinear systems with a triangular structure