Implementation of computer vision for intelligent control and posture recognition

• Main Authors: Jun-Wei Chang, 張峻瑋
• Other Authors: Wen-June Wang
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/64064719080100398334

博士 === 國立中央大學 === 電機工程學系 === 104 === This dissertation presents three studies based on computer vision containing posture recognition, object grasping by a robot arm, terrain traversability estimation for wheeled mobile robot. In the first study, an effectivity posture recognition method is proposed based on depth image captured by Kinect sensor. Several image processing techniques are applied to extract the features on the human postures such as ratio of body and star skeleton. The ratio of upper and lower body width can fast distinguish the posture whether posture is kneeling or not. Then, Learning Vector Quantization neural network is used to recognize the four categories of human postures forward sitting, stooping, lying and the other. One more check is the final step of the posture recognition method to judge standing and non-forward sitting. The second study utilizes the stereo vision to enhance the accuracy of the robot arm without any external sensors. Due to the backlash, the gripper cannot approach the target object. The stereo vision is applied to recognize the actual position of the gripper and then the fuzzy control is adopted to compensate the position error. After compensation the robot arm can successfully grasp the target object demonstrated in the experiments. The third study develops and implements a fast terrain traversability estimation method using a depth image sensor XtionPro. In this study, a virtual terrain surface image is created and compared with captured upcoming terrain image to extract the features of the terrain. Based on the features, any obstacle and hollow are found. Then, a movement strategy is proposed for robot to make reaction to the obstacle and hollow and approach the goal position.