 Название: Performance Analysis and Optimization of Parallel Manipulators
Автор: Qinchuan Li, Chao Yang, Lingmin Xu, Wei Ye
Издательство: Springer
Серия: Research on Intelligent Manufacturing
Год: 2023
Страниц: 303
Язык: английский
Формат: pdf (true)
Размер: 10.5 MB
This book investigates the performance analysis and optimization design of parallel manipulators in detail. It discusses performance evaluation indices for workspace, kinematic, stiffness, and dynamic performance, single- and multi-objective optimization design methods, and ways to improve optimization design efficiency of parallel manipulators. This book collects the authors’ research results previously scattered in many journals and conference proceedings and presents them in a unified form after the methodical edition. As a result, numerous performance analyses and optimization of parallel manipulators are presented, in which the readers in the robotics community may be greatly interested. More importantly, readers can use the methods and tools introduced in this book to carry out performance evaluation and optimization of parallel manipulators by themselves. The book can provide important reference and guideline for undergraduate and graduate students, engineers, and researchers who are interested in design and application of parallel manipulators.
The general approach to the considered problems lies in the fact that robot-related performance indices present scalar functions synthesized from matrix models, and among them, the most important are the Jacobian matrix, stiffness and compliance matrices, and mass matrix separately formulated within the static and dynamic versions. The general specific feature of these matrices is the dimensional inhomogeneity resulting in the absence of matrix eigenvalues. This feature is valid for matrices associated with both parallel- and serial-kinematic mechanisms; thus, this feature allows the application of some results formulated for serial-kinematics robots to be applied to parallel-kinematics robots. Additionally, this is the reason for the widespread application of the matrix determinants when synthesizing performance indices since the determinant presents a unique invariant of a dimensionally inhomogeneous matrix.
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