Muutke küpsiste eelistusi

E-raamat: Collaborative Fleet Maneuvering for Multiple Autonomous Vehicle Systems

  • Formaat - PDF+DRM
  • Hind: 110,53 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This book presents theoretical foundations and technical implementation guidelines for multi-vehicle fleet maneuvering, which can be implemented by readers and can also be a basis for future research. As a research monograph, this book presents fundamental concepts, theories, and technologies for localization, motion planning, and control of multi-vehicle systems, which can be a reference book for researchers and graduate students from different levels. As a technical guide, this book provides implementation guidelines, pseudocode, and flow diagrams for practitioners to develop their own systems. Readers should have a preliminary knowledge of mobile robotics, state estimation and automatic control to fully understand the contents in this book. To make this book more readable and understandable, extensive experimental results are presented to support each chapter.

Introduction.- Technical Background.- Anchor-Based Flexible Fleet
Maneuvering.- Map-Based Virtual-Structure Fleet Maneuvering in Cluttered
Environments.- Vision-Based Leader-Follower Queue Maneuvering in Unknown
Cluttered Environments.- Vision-Based Flexible Fleet Maneuvering in Unknown
Cluttered Environments.- Map Matching Based Leader-Follower Path Retracing in
Infrastructure-Free Environments.- Multi-UAV Optimal Fleet Flying for Air
Patrol in Constrained Environments.- Conclusion.
Yuanzhe Wang received the B.Eng. degree from the Southeast University, China, in 2010, the M.Eng. degree from the Beihang University, China, in 2013, and the Ph.D. degree from the Nanyang Technological University (NTU), Singapore, in 2019. He is a Research Fellow in the School of Electrical and Electronic Engineering, NTU. He has served as an Associate Editor for The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) from 2020 to 2022. His current research interests include mobile robotics, control application, and cybersecurity in robotics. Danwei Wang received his Ph.D. and M.S.E. degrees from the University of Michigan, Ann Arbor in 1989 and 1984, respectively. He received his B.E. degree from the South China University of Technology, China, in 1982. He is a Professor in the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore. He has served as the Head of the Division of Control and Instrumentation, NTU from 2005 to 2011, the Director of the Center for System Intelligence and Efficiency, NTU from 2014 to 2016, and the Director of the ST Engineering-NTU Corporate Laboratory, NTU from 2015 to 2021. He also served as general chairman, technical chairman and various positions in several international conferences. He was a recipient of Alexander von Humboldt fellowship, Germany. He is a Fellow of Academy of Engineering, Singapore, and a Fellow of IEEE. His research interests include robotics, control engineering, and fault diagnosis.