Handling uncertainty and networked s...
Busoniu, Lucian.

 

  • Handling uncertainty and networked structure in robot control[electronic resource] /
  • 紀錄類型: 書目-語言資料,印刷品 : Monograph/item
    杜威分類號: 629.892
    書名/作者: Handling uncertainty and networked structure in robot control/ edited by Lucian Busoniu, Levente Tamas.
    其他作者: Busoniu, Lucian.
    出版者: Cham : : Springer International Publishing :, 2015.
    面頁冊數: xxviii, 388 p. : : ill., digital ;; 24 cm.
    Contained By: Springer eBooks
    標題: Robots - Control systems.
    標題: Autonomous robots.
    標題: Mobile robots.
    標題: Engineering.
    標題: Control.
    標題: Robotics and Automation.
    標題: Artificial Intelligence (incl. Robotics)
    ISBN: 9783319263274
    ISBN: 9783319263250
    內容註: From the Contents: Part I Learning Control in Unknown Environments -- Robot Learning for Persistent Autonomy -- The Explore-Exploit Dilemma in Nonstationary Decision Making under Uncertainty -- Part II Dealing with Sensing Uncertainty -- Observer Design for Robot Manipulators via Takagi-Sugeno Models and Linear Matrix Inequalities -- Part III Control of Networked and Interconnected Robots -- Vision-based quadcopter navigation in structured environments.
    摘要、提要註: 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 http://extras.springer.com. 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.
    電子資源: http://dx.doi.org/10.1007/978-3-319-26327-4
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