Design and Implementation of Fuzzy Q-learning Based Weight-lifting Auto-balancing Control Strategy for Adult-sized Humanoid Robots

論文翻譯標題: 以模糊Q-學習為基礎之負重自動平衡控制策略於大型人形機器人之研製
  • 王 浩丞

學生論文: Master's Thesis


This thesis proposes a control method that improves the ability of adult-sized humanoid robots to adapt to weight-lifting situations First the architecture of the hardware software and the design concepts for the third generation adult-sized robot David III are introduced In order to achieve the goal of having humanoid robots automatically balance their motion for weight-lifting situations feedback control is added to the motion control system The feedback sensors include a three-axis accelerometer and a three-axis gyroscopic which would be processed by Kalman filter as well as eight force sensors providing the zero moment point (ZMP) information on the robot These feedback signals are used as the input of a Fuzzy Q-learning controller which adjusts the motions to keep the stabilization of the robot The Fuzzy Q-learning controller consists of two stages: one is the stage of fitting the output weights of each pose in motion patterns and the second is training the rule-table of the controller The experiment shows that the controller allows the adult-sized robot to walk stably no matter whether it lifts a weight or has a backpack on its back Thus the developed controller does indeed keep the balance of the robot in different situations which gives the robot the ability to adapt to various environments in the manner of human beings
獎項日期2014 7月 21
監督員Tzuu-Hseng S. Li (Supervisor)