Browsing by Author "Wu, Cheng-Hei"

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CAD-based Pose Estimation Design for Random Bin Picking using a RGB-D Camera
(2017-09-01)
Song, Kai-Tai
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Wu, Cheng-Hei
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Jiang, Sin-Yi
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資訊工程學系
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Department of Computer Science
This paper presents a CAD-based six-degrees-of-freedom (6-DoF) pose estimation design for random bin picking for multiple objects. A virtual camera generates a point cloud database for the objects using their 3D CAD models. To reduce the computational time of 3D pose estimation, a voxel grid filter reduces the number of points for the 3D cloud of the objects. A voting scheme is used for object recognition and to estimate the 6-DoF pose for different objects. An outlier filter filters out badly matching poses so that the robot arm always picks up the upper object in the bin, which increases the success rate. In a computer simulation using a synthetic scene, the average recognition rate is 97.81 % for three different objects with various poses. A series of experiments have been conducted to validate the proposed method using a Kuka robot arm. The average recognition rate for three objects is 92.39 % and the picking success rate is 89.67 %.
CAD-Based Pose Estimation for Random Bin-Picking of Multiple Objects Using a RGB-D Camera
(2015)
Wu, Cheng-Hei
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Jiang, Sin-Yi
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Song, Kai-Tai
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電控工程研究所
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Institute of Electrical and Control Engineering
In this paper, we propose a CAD-based 6-DOF pose estimation design for random bin-picking of multiple different objects using a Kinect RGB-D sensor. 3D CAD models of objects are constructed via a virtual camera, which generates a point cloud database for object recognition and pose estimation. A voxel grid filter is suggested to reduce the number of 3D point cloud of objects for reducing computing time of pose estimation. A voting-scheme method was adopted for the 6-DOF pose estimation a swell as object recognition of different type objects in the bin. Furthermore, an outlier filter is designed to filter out bad matching poses and occluded ones, so that the robot arm always picks up the upper object in the bin to increase pick up success rate. A series of experiments on a Kuka 6-axis robot revels that the proposed system works satisfactorily to pick up all random objects in the bin. The average recognition rate of three different type objects is 93.9% and the pickup success rate is 89.7%.
Design and Experiment of a Human-Touch Robot
(2014)
Song, Kai-Tai
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Jiang, Sin-Yi
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Lin, Ming-Han
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Wu, Cheng-Hei
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Chiu, Yi-Fu
In this paper we propose a shared-control based teleoperation design for a dual-arm omnidirectional mobile robot. The robot not only can adapt its motion to environment autonomously, but also move according to the user\'s remote commands. To achieve this goal, a shared-control scheme for effective remote control of the mobile robot is developed. For tele-presence and remote control, a user interface is designed for a smart phone/tablet. The proposed controller determines the human and autonomy control gains by computing user\'s confidence factor. In this approach, the robot assists a user to manipulate its motion from a remote site by compensating local insufficiency of human remote control. Practical experiments validate the proposed design and demonstrate that the proposed shared-control approach of a dual-arm omnidirectional mobile robot can have potential use for future home service tasks.