Robot Manipulator Redundancy Resolution
Yunong Zhang; Long Jin
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Sold by UK BOOKS STORE, London, LONDO, United Kingdom
AbeBooks Seller since 11 March 2024
Condition: New
Quantity: 15 available
Add to basketBrand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Introduces a revolutionary, quadratic-programming based approach to solving long-standing problems in motion planning and control of redundant manipulators
This book describes a novel quadratic programming approach to solving redundancy resolutions problems with redundant manipulators. Known as ``QP-unified motion planning and control of redundant manipulators'' theory, it systematically solves difficult optimization problems of inequality-constrained motion planning and control of redundant manipulators that have plagued robotics engineers and systems designers for more than a quarter century.
An example of redundancy resolution could involve a robotic limb with six joints, or degrees of freedom (DOFs), with which to position an object. As only five numbers are required to specify the position and orientation of the object, the robot can move with one remaining DOF through practically infinite poses while performing a specified task. In this case redundancy resolution refers to the process of choosing an optimal pose from among that infinite set. A critical issue in robotic systems control, the redundancy resolution problem has been widely studied for decades, and numerous solutions have been proposed. This book investigates various approaches to motion planning and control of redundant robot manipulators and describes the most successful strategy thus far developed for resolving redundancy resolution problems.
Robot Manipulator Redundancy Resolution is must-reading for advanced undergraduates and graduate students of robotics, mechatronics, mechanical engineering, tracking control, neural dynamics/neural networks, numerical algorithms, computation and optimization, simulation and modelling, analog, and digital circuits. It is also a valuable working resource for practicing robotics engineers and systems designers and industrial researchers.
Yunong Zhang, PhD, is a professor at the School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China, and an associate editor at IEEE Transactions on Neural Networks and Learning Systems. He has researched motion planning and control of redundant manipulators and recurrent neural networks for 19 years, and he holds seven authorized patents.
Long Jin is pursuing his doctorate in Communication and Information Systems at the School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China. His main research interests include robotics, neural networks, and intelligent information processing.
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