EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link: https://ebookbell.com/faq

We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.

For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Collision Detection For Robot Manipulators Methods And Algorithms Kyu Min Park

Name: Collision Detection For Robot Manipulators Methods And Algorithms Kyu Min Park
SKU: BELL-50279078
Price: 31.00 USD
Availability: InStock
Rating: 5 (50 reviews)

SKU: BELL-50279078

$ 31.00 ~~$ 45.00~~ (-31%)

5.0

50 reviews

Collision Detection For Robot Manipulators Methods And Algorithms Kyu Min Park instant download after payment.

Publisher: Springer

File Extension: PDF

File size: 2.6 MB

Pages: 132

Author: Kyu Min Park, Frank C. Park

ISBN: 9783031301940, 3031301943

Language: English

Year: 2023

Product desciption

Collision Detection For Robot Manipulators Methods And Algorithms Kyu Min Park by Kyu Min Park, Frank C. Park 9783031301940, 3031301943 instant download after payment.

This book provides a concise survey and description of recent collision detection methods for robot manipulators. Beginning with a review of robot kinodynamic models and preliminaries on basic statistical learning methods, the book covers fundamental aspects of the collision detection problem, from collision types and collision detection performance criteria to model-free versus model-based methods, and the more recent data-driven learning-based approaches to collision detection. Special effort has been given to describing and evaluating existing methods with a unified set of notation, systematically categorizing these methods according to a basic set of criteria, and summarizing the advantages and disadvantages of each method. This book is the first to comprehensively organize the growing body of learning-based collision detection methods, ranging from basic supervised learning methods to more advanced approaches based on unsupervised learning and transfer learning techniques. Step-by-step implementation details and pseudocode descriptions are provided for key algorithms. Collision detection performance is measured with respect to both conventional criteria such as detection delay and the number of false alarms, as well as criteria that measure generalization capability for learning-based methods. Whether it be for research or commercial applications, in settings ranging from industrial factories to physical human–robot interaction experiments, this book can help the reader choose and successfully implement the most appropriate detection method that suits their robot system and application.