Learning Control for Space Robotic Operation Using Support Vector Machines

  • Panfeng Huang
  • Wenfu Xu
  • Yangsheng Xu
  • Bin Liang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Automatical operation of space robots is a challenging and ultimate goal of space servicing. In this paper, we present a novel approach for tracking and catching operation of space robots based on learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parameterize the model of HCS, and then develop a new SVM-based leaning structure to improve HCS in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, which makes it efficient in modeling, understanding and transferring its learning process. The simulation results demonstrate that the proposed method is useful and feasible in tracking trajectory and catching objects autonomously.


Support Vector Machine Joint Space Support Vector Regression Tracking Trajectory Learn Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Panfeng Huang
    • 1
    • 2
  • Wenfu Xu
    • 3
  • Yangsheng Xu
    • 2
  • Bin Liang
    • 3
  1. 1.College of AstronauticsNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of Automation and Computer-Aided EngineeringThe Chinese University of Hong KongShatin, Hong Kong
  3. 3.Shenzhen Space Technology CenterHarbin Institute of TechnologyShenzhenChina

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