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Robotic Intelligent Assembly / / by Jing Xu, Hao Su, Rui Chen, Zhimin Hou



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Autore: Xu Jing Visualizza persona
Titolo: Robotic Intelligent Assembly / / by Jing Xu, Hao Su, Rui Chen, Zhimin Hou Visualizza cluster
Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Edizione: 1st ed. 2025.
Descrizione fisica: 1 online resource (XVI, 233 p. 146 illus., 133 illus. in color.)
Disciplina: 629.8
Soggetto topico: Mechatronics
Electronic circuits
Energy harvesting
Multibody systems
Vibration
Mechanics, Applied
Electronic Circuits and Systems
Energy Harvesting
Multibody Systems and Mechanical Vibrations
Persona (resp. second.): SuHao
ChenRui
HouZhimin
Nota di contenuto: Introduction -- Model based strategies -- Implementation -- Learning based strategies theoretical Analysis -- Learning based Implementation -- Conclusion.
Sommario/riassunto: This book explores peg-in-hole assembly strategies to study robotic intelligent assembly. It presents several state-of-the-art principles for peg-in-hole assembly strategies, supported by experimental evidence. In pursuit of theoretical innovation, the author summarizes their research on robotic intelligent assembly over the past decade, highlighting the limitations of model-based algorithms in complex assembly environments and the importance of data efficiency for learning-based algorithms. Each algorithm is supported by extensive experimentation and results demonstrating its effectiveness. A review of research ideas provides readers with a comprehensive understanding of the progress made in this field. This monograph is intended for undergraduate and postgraduate students interested in robotic intelligent assembly, researchers studying robotic intelligent assembly algorithms, and electronic, mechanical, and computer engineers engaged in industrial robot-assisted assembly.
Titolo autorizzato: Robotic Intelligent Assembly  Visualizza cluster
ISBN: 981-9626-57-9
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9911003595503321
Lo trovi qui: Univ. Federico II
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Serie: Research on Intelligent Manufacturing, . 2523-3394