Title page for etd-0619117-151814


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URN etd-0619117-151814
Author Jia-Lin Lee
Author's Email Address No Public.
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Department Electrical Engineering
Year 2016
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Adaptive Image-Based Visual Servoing of Robot Manipulators by Reinforcement Learning
Date of Defense 2017-07-18
Page Count 54
Keyword
  • Visual servoing
  • Robot arm
  • Q-learning
  • Reinforcement learning
  • Abstract The main objective of this thesis is to design an intelligent gain controller for a robot arm based on reinforcement learning methods. The controller is applied in image-based visual servoing. This research uses the image processing algorithm to compute the features of desired image and current image. The image feature error is used to generate the state space of Q-learning. The ε-greedy method is applied to choose a suitable action which robot arm will take according to the input state. The action space consists of control gains. In order to make the control system more flexible, this thesis introduces an attenuation value based on the original action space. Each action will be accompanied by an attenuation value to reduce the amount of control when the current feature error is less than a threshold, so that the arm in the vicinity of the target position will more accurate and stable. The learning method will solve the control system problem. The fixed large control gain will lead to the system overshoot. In contrast small the control gain will cause the system to converge slowly in visual servoing. Moreover, Q-learning doesn’t need any knowledge about the environment, it is suitable for controller for decision making. Q-learning gets reward through a learning agent interacting with the environment. The agent will adjust the policy according to the strength of reward and try to maximize reward over time. After some learning iterations, the controller can output a series of control gain to achieve the goal efficiency. The proposed method will be implemented by a 7-axis robot arm in the simulation and experimental environment. The results also is compared with the one of fixed control gain method to verify the efficiency of the proposed method.
    Advisory Committee
  • Tzuu-Hseng S. Li - chair
  • Ming-Yi Ju - co-chair
  • Ching-Chih Tsai - co-chair
  • Yu-Jen Chen - co-chair
  • Kao-Shing Hwang - advisor
  • Files
  • etd-0619117-151814.pdf
  • Indicate in-campus at 0 year and off-campus access at 2 year.
    Date of Submission 2017-07-19

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