Hierarchical Behavior Controller in Robotic Applications

  • Yasuhisa Hasegawa
  • Toshio Fukuda
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 21)


In this chapter, we propose a new controller and a learning algorithm. Our system consists of several subcontrollers to indicate the desired trajectories for robot’s actuators. This algorithm selects the subcontroller which is not appropriate and needs to be tuned, by evaluating each subcontroller using multiple regression analysis based on previously obtained evaluation values. This can reduce the learning iterations by avoiding attempts to tune good subcontrollers. The proposed algorithm is applied to the problem of selecting and tuning subcontrollers at a middle layer in the hierarchical behavior controller in order to compensate imperfect initial controllers. The hierarchical behavior controller is applied to the problem of controlling a seven-link brachiation robot, which moves dynamically from branch to branch like a gibbon, a long-armed ape, swinging its body in a pendulum (Figure 1).


Fuzzy Rule Fuzzy Controller Feedback Controller Soft Computing Fuzzy Logic Controller 
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 1998

Authors and Affiliations

  • Yasuhisa Hasegawa
    • 1
  • Toshio Fukuda
    • 1
  1. 1.Dept. of Micro System EngineeringNagoya UniversityJapan

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