Kinematic Control of Redundant Robot Arms Using Neural Networks

Preface

In recent decades, robotics has attracted more and more attention from researchers since it has been widely used in scientific research and engineering applications, such as space exploration, underwater surveys, industrial and military industries, welding, painting and assembly, medical applications, and so on. Much effort has been spent on robotics, and different types of robot manipulators have thus been developed and investigated, such as serial manipulators consisting of redundant manipulators and mobile manipulators, parallel manipulators, and cable‐driven manipulators. A redundant manipulator is often designed as a series of links connected by motor‐actuated joints that extends from a fixed base to an end‐effector while a mobile manipulator is often designed as a robotic device composed of a mobile platform and a redundant manipulator fixed to the platform. Different from these serial manipulators, a parallel manipulator is a mechanical system that usually uses several serial chains to support a single platform, or end‐effector. Using these manipulators to save labor and increase accuracy is becoming common practice in various industrial fields. As a consequence, many approaches have been proposed, investigated and employed for the control of robot manipulators. Among them, thanks to the many advantages in parallel distributed structure, nonlinear mapping, ability to learn from examples, high generalization performance, and capability to approximate an arbitrary ...