© 2015

Handling Uncertainty and Networked Structure in Robot Control

  • Lucian Busoniu
  • Levente Tamás


  • Presents in-depth coverage of a range of algorithms, which readers can implement and modify for their own systems

  • Demonstrates practical investigations and case studies that highlight the applicability of the techniques

  • Describes a selection of modern methods, offering readers up-to-date means of tackling uncertainty and network structure in robot control

  • Provides additional electronic material, such as source code and experimental data


Part of the Studies in Systems, Decision and Control book series (SSDC, volume 42)

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Learning Control in Unknown Environments

    1. Front Matter
      Pages 1-1
    2. Petar Kormushev, Seyed Reza Ahmadzadeh
      Pages 3-28
    3. Gabriel A. D. Lopes, Esmaeil Najafi, Subramanya P. Nageshrao, Robert Babuška
      Pages 53-74
    4. Seyed Reza Ahmadzadeh, Petar Kormushev
      Pages 75-99
  3. Dealing with Sensing Uncertainty

    1. Front Matter
      Pages 101-101
    2. Víctor Estrada-Manzo, Zsófia Lendek, Thierry-Marie Guerra
      Pages 103-128
    3. Robert Frohlich, Levente Tamás, Zoltan Kato
      Pages 129-151
    4. Michael Beetz, Ferenc Bálint-Benczédi, Nico Blodow, Christian Kerl, Zoltán-Csaba Márton, Daniel Nyga et al.
      Pages 181-208
    5. Henry Carrillo, José A. Castellanos
      Pages 209-235
    6. Karol Hausman, Dejan Pangercic, Zoltán-Csaba Márton, Ferenc Bálint-Benczédi, Christian Bersch, Megha Gupta et al.
      Pages 237-262
  4. Control of Networked and Interconnected Robots

    1. Front Matter
      Pages 263-263
    2. Előd Páll, Levente Tamás, Lucian Buşoniu
      Pages 265-290
    3. Piroska Haller, Lőrinc Márton, Zoltán Szántó, Tamás Vajda
      Pages 291-311
    4. Marcos Cesar Bragagnolo, Irinel-Constantin Morărescu, Lucian Buşoniu, Pierre Riedinger
      Pages 313-333
    5. Haci M. Guzey, Travis Dierks, Sarangapani Jagannathan
      Pages 335-360
    6. Amélie Chevalier, Cosmin Copot, Robin De Keyser, Andres Hernandez, Clara Ionescu
      Pages 361-386
  5. Back Matter
    Pages 387-388

About this book


This book focuses on two challenges posed in robot control by the increasing adoption of robots in the everyday human environment: uncertainty and networked communication. Part I of the book describes learning control to address environmental uncertainty. Part II discusses state estimation, active sensing, and complex scenario perception to tackle sensing uncertainty. Part III completes the book with control of networked robots and multi-robot teams.

Each chapter features in-depth technical coverage and case studies highlighting the applicability of the techniques, with real robots or in simulation. Platforms include mobile ground, aerial, and underwater robots, as well as humanoid robots and robot arms. Source code and experimental data are available at

The text gathers contributions from academic and industry experts, and offers a valuable resource for researchers or graduate students in robot control and perception. It also benefits researchers in related areas, such as computer vision, nonlinear and learning control, and multi-agent systems.


Autonomous Mobile Robots Learning-based Control Networked Multi-robot Systems Networked Single-robot Systems Perception in Complex Systems State Estimation

Editors and affiliations

  • Lucian Busoniu
    • 1
  • Levente Tamás
    • 2
  1. 1.Automation DepartmentTechnical University of Cluj-NapocaCluj-NapocaRomania
  2. 2.Automation DepartmentTechnical University of Cluj-NapocaCluj-NapocaRomania

About the editors

Lucian Busoniu received the M.Sc. degree (valedictorian) from the Technical University of Cluj-Napoca, Romania, in 2003 and the Ph.D. degree (cum laude) from the Delft University of Technology, the Netherlands, in 2009. He has held research positions in the Netherlands and France, and is currently an associate professor with the Department of Automation at the Technical University of Cluj-Napoca. His fundamental interests include planning-based methods for nonlinear optimal control, reinforcement learning and dynamic programming with function approximation, and multiagent systems; while his practical focus is applying these techniques to robotics. He has coauthored a book and more than 50 papers and book chapters on these topics. He was the recipient of the 2009 Andrew P. Sage Award for the best paper in the IEEE Transactions on Systems, Man, and Cybernetics. 

Levente Tamas received the M.Sc. (valedictorian) and the Ph.D. degree in electrical engineering from Technical University of Cluj-Napoca, Romania, in 2005 and 2010, respectively. He took part in several postdoctoral programs dealing with 3D perception and robotics, the most recent one spent at the Bern University of Applied Sciences, Switzerland. He is currently with the Department of Automation, Technical University of Cluj-Napoca, Romania. His research focuses on 3D perception and planning for autonomous mobile robots, and has resulted in several well ranked conference papers, journal articles, and book chapters in this field.

Bibliographic information

  • Book Title Handling Uncertainty and Networked Structure in Robot Control
  • Editors Lucian Bușoniu
    Levente Tamás
  • Series Title Studies in Systems, Decision and Control
  • Series Abbreviated Title Studies in Systems, Decision and Control
  • DOI
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-26325-0
  • Softcover ISBN 978-3-319-79932-2
  • eBook ISBN 978-3-319-26327-4
  • Series ISSN 2198-4182
  • Series E-ISSN 2198-4190
  • Edition Number 1
  • Number of Pages XXVIII, 388
  • Number of Illustrations 26 b/w illustrations, 146 illustrations in colour
  • Topics Control and Systems Theory
    Robotics and Automation
    Artificial Intelligence
  • Buy this book on publisher's site
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