Advances in Robotic Systems, Part 1 shows how the activity in robotic systems has increased significantly over the past decade. Major centers of research and development in robotic systems were established on the international scene, and these became focal points for the brilliant research efforts of many academicians and industrial professionals. The systems aspects of robotics, in general, and of robot control, in particular, are manifested through a number of technical facts. This book comprises 10 chapters, with the first focusing on applications of neural networks to robotics. The following chapters then discuss a unified approach to kinematic modeling, identification and compensation for robot calibration; nonlinear control algorithms in robotic systems; and kinematic and dynamic task space motion planning for robot control. Other chapters cover discrete kinematic modeling techniques in Cartesian space for robotic system; force distribution algorithms for multifingered grippers; frequency analysis for a discrete-time robot system; minimum cost trajectory planning for industrial robots; tactile sensing techniques in robotic systems; and sensor data fusion in robotic systems. This book will be of interest to practitioners in the fields of computer science, systems science, and mathematics.

Advances in Robotic Systems, Part 1 shows how the activity in robotic systems has increased significantly over the past decade. Major centers of research and development in robotic systems were established on the international scene, and these became focal points for the brilliant research efforts of many academicians and industrial professionals. The systems aspects of robotics, in general, and of robot control, in particular, are manifested through a number of technical facts. This book comprises 10 chapters, with the first focusing on applications of neural networks to robotics. The following chapters then discuss a unified approach to kinematic modeling, identification and compensation for robot calibration; nonlinear control algorithms in robotic systems; and kinematic and dynamic task space motion planning for robot control. Other chapters cover discrete kinematic modeling techniques in Cartesian space for robotic system; force distribution algorithms for multifingered grippers; frequency analysis for a discrete-time robot system; minimum cost trajectory planning for industrial robots; tactile sensing techniques in robotic systems; and sensor data fusion in robotic systems. This book will be of interest to practitioners in the fields of computer science, systems science, and mathematics.

Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology. The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers in the field but outlines a novel approach to solving Robotics problems. The importance of neural networks in all aspects of Robot arm manipulators, neurocontrol, and Robotic systems is also given thorough and in-depth coverage. All researchers and students dealing with Robotics will find Neural Systems for Robotics of immense interest and assistance. Focuses on the use of neural networks in robotics-one of the hottest application areas for neural networks technology Represents the most up-to-date developments in this rapidly growing application area of neural networks Contains a new and novel approach to solving Robotics problems

Advances in Robotic Systems, Part 2 is the second of a companion set of two volumes on advances in robotic systems dynamics and control. This book comprises nine chapters, with the first focusing on kinesthetic feedback techniques in teleoperated systems. The succeeding chapters then delve into topics such as parallel algorithms and fault-tolerant reconfigurable architecture for robot kinematics and dynamics computations; trajectory planning for robot control; and a control systems perspective. Other chapters cover simplified techniques for adaptive control of robotic systems; theory and applications of configuration control for redundant manipulators; nonlinear feedback for force control of robot manipulators; systolic architectures for dynamic control of manipulators; inverse dynamics; and forward dynamics. This book will be of interest to practitioners in the fields of computer science, systems science, and mathematics.

Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.