Browsing by Author "Hsieh, Chang-Hung"

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A Novel Sensory Mapping Design for Bipedal Walking on a Sloped Surface
(2012-10-12)
Wu, Chiao-Min
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Huang, Chao-Ping
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Hsieh, Chang-Hung
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Song, Kai-Tai
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電子工程學系及電子研究所
This paper presents an environment recognition method for bipedal robots using a time-delay neural network. For a robot to walk in a varying terrain, it is desirable that the robot can adapt to any environment encountered in real-time. This paper aims to develop a sensory mapping unit to recognize environment types from the input sensory data based on an artificial neural network approach. With the proposed sensory mapping design, a bipedal walking robot can obtain real-time environment information and select an appropriate walking pattern accordingly. Due to the time-dependent property of sensory data, the sensory mapping is realized by using a time-delay neural network. The sensory data of earlier time sequences combined with current sensory data are sent to the neural network. The proposed method has been implemented on the humanoid robot NAO for verification. Several interesting experiments were carried out to verify the effectiveness of the sensory mapping design. The mapping design is validated for the uphill, downhill and flat surface cases, where three types of environment can be recognized by the NAO robot online.
CPG-Based Control Design for Bipedal Walking on Unknown Slope Surfaces
(2014)
Song, Kai-Tai
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Hsieh, Chang-Hung
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電控工程研究所
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Institute of Electrical and Control Engineering
The paper presents a walking pattern generator and a balance control system for a bipedal robot to handle an unknown slope. The robot uses onboard gyro and accelerometer sensors to detect the pose information of the upper-body. A controller is proposed for the robot to walk on an unknown slope by adjusting the tilt angle of the upper-body. The theory of central pattern generator (CPG) is applied to generate the walking trajectory. By using the pose information of the upper-body, we developed a method to determine the relationship between the slope surface and the upper-body pose and generate the compensation motion to adjust the tilt angle of the upper-body. The compensation control consists of predictive compensation and immediate compensation. The predictive compensation responds to adjust the upper-body pose before beginning of the next step. The immediate compensation is applied to adjust the upper-body pose during the single support phase. The integrated controller adapts to the unknown slope in real time while robot walking. Using the bipedal robot NAO, the experimental results show that the biped robot can walk successfully on unknown slopes.