I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called /spl alpha/-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or /spl alpha/-agents, and virtual agents associated with /spl alpha/-agents called /spl beta/- and /spl gamma/-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms.

/pdf/flocking-for-multi-agent-dynamic-systems-algorithms-and-5efqu9aidp.pdf

Flocking for multi-agent dynamic systems: algorithms and theory

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.

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Design, fabrication and control of soft robots

In this work, we present a general approach to task planning based on contingent planning and run-time sensing, which forms part of a robot task planning framework called KVP. Using the general-purpose PKS planner, we model information-gathering actions at plan time that have multiple possible outcomes at run time. As a result, perception and sensing arise as necessary preconditions for manipulation, rather than being hard-coded as a task itself. We demonstrate the effectiveness of our approach on two simple scenarios covering visual and force sensing, and discuss its applicability to more general tasks in automation and mobile manipulation, involving arbitrary numbers of sensors and manipulators.

Robot task planning with contingencies for run-time sensing

A formidable challenge in the development of human-friendly robots is to simultaneously achieve desired levels of performance and safety. To address this issue, a hybrid actuation concept has been proposed, combining large, low impedance actuators and small, high-frequency actuators. However, the determination of design parameters remains a challenge, as stiffness and electrical motor torque capacity simultaneously affect both the control performance and the safety of the manipulator. Using analytical models of the hybrid actuation system, we propose a methodology to achieve a combination of low impedance and high control bandwidth. The optimized parameters are verified and compared with previous ones through simulation and experimentation.

/pdf/analysis-of-torque-capacities-in-hybrid-actuation-for-human-207tjatu5m.pdf

Analysis of torque capacities in hybrid actuation for human-friendly robot design

This paper describes an efficient recursive algorithm for the computation of the extended operational space inertia matrix of an n-link branching (tree-like) redundant robotic mechanism with multiple operational points. The proposed algorithm behaves linearly with respect to n in practice. Therefore, as the number of links increases, this algorithm performs significantly better than the existing O(n/sup 3/) symbolic method. The experimental results of this algorithm are presented using real-time dynamic simulation.

/pdf/efficient-algorithm-for-extended-operational-space-inertia-3734tz5n20.pdf

Efficient algorithm for extended operational space inertia matrix

Robot dynamics has been traditionally viewed from the perspective of a manipulator's joint motions, and significant effort has been devoted to the development of joint space dynamic models and control methodologies. However, the limitations of joint space control techniques, especially in constrained motion tasks, have motivated alternative approaches for dealing with task-level dynamics and control. The operational space formulation, which falls within this line of research, has been driven by the need to develop mathematical models for the description, analysis, and control of robot dynamics with respect to task behavior. In this article, we review the operational space task-level models and discuss the various control methodologies that have been developed in this framework. These include : the unified motion and force control approach ; the notion of dynamic consistency in redundant manipulator control ; the reduced effective inertia property associated with macro-/mini-manipulator systems and the dextrous dynamic coordination strategy proposed for their control ; and the augmented-object model for the control of robot systems involving multiple manipulators.

The Operational Space Framework

The paper investigates the dynamic characterization of redundant manipulators and formalizes the problem of dynamic optimization in manipulator design. The dynamic performance of a manipulator is described by both inertial and acceleration characteristics as perceived at the end-effector operational point. The inertial characteristics at this point are given by the operational space kinetic energy matrix (pseudo-kinetic energy matrix for a redundant manipulator) which is dependent on the kinematic and inertial parameters of the manipulator and varies with its configuration. The acceleration characteristics of the end-effector are described by a joint torque/acceleration transmission matrix. In addition to their dependency on the kinematic and inertial parameters, the acceleration characteristics depend on the velocities and actuator torque bounds. The dynamic optimization is formalized in terms of finding the design parameters under the various constraints to achieve the smallest most isotropic and most uniform end-effector inertial properties, while providing the largest, most isotropic, and most uniform bounds on the magnitude of end-effector acceleration. This approach is used in the design of ARTISAN, a ten-degree-of-freedom manipulator currently under development at Stanford University.

Oussama Khatib

Papers

Robot task planning with contingencies for run-time sensing

Analysis of torque capacities in hybrid actuation for human-friendly robot design

Efficient algorithm for extended operational space inertia matrix

The Operational Space Framework

Isotropic and Uniform Inertial and Acceleration Characteristics: Issues in the Design of Redundant Manipulators