Browsing by Author "Lin, Chien-Chou"

Now showing 1 - 7 of 7

A CONCURRENT PLANNING ALGORITHM FOR DUAL-ARM SYSTEMS
(2007)
Chuang, Jen-Hui
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Chan, Ting-Wei
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Lin, Chien-Chou
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資訊工程學系
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Department of Computer Science
An algorithm for path planning of object held by multiple manipulators is presented in this paper. The proposed path planning algorithm is composed with two planners: path planner of the object and motion planner of manipulators. Both planners use a generalized potential model to evaluate the repulsion between object/manipulators and obstacles, so as to avoid collision. By adding virtual obstacle planes to roughly divide the workspace into several subspaces, a concurrent approach of path planning is also proposed. With each subspace designated to a manipulator, collisions between manipulators can be avoided. Therefore, paths of all. manipulators can be planned at the same time.
A novel point cloud registration using 2D image features
(2017-01-07)
Lin, Chien-Chou
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Tai, Yen-Chou
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Lee, Jhong-Jin
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Chen, Yong-Sheng
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資訊工程學系
Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.
A Path Planning Algorithm using Generalized Potential Model for Hyper-Redundant Robots with 2-DOF Joints
(2011-06-01)
Lin, Chien-Chou
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Chuang, Jen-Hui
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Hsieh, Cheng-Tieng
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資訊工程學系
;
Department of Computer Science
This paper proposes a potential-based path planning algorithm of articulated robots with 2-DOF joints. The algorithm is an extension of a previous algorithm developed for 3-DOF joints. While 3-DOF joints result in a more straightforward potential minimization algorithm, 2-DOF joints are obviously more practical for active operations. The proposed approach computes repulsive force and torque between charged objects by using generalized potential model. A collision-free path can be obtained by locally adjusting the robot configuration to search for minimum potential configurations using these force and torque. The optimization of path safeness, through the innovative potential minimization algorithm, makes the proposed approach unique. In order to speedup the computation, a sequential planning strategy is adopted. Simulation results show that the proposed algorithm works well compared with 3-DOF-joint algorithm, in terms of collision avoidance and computation efficiency.
A POTENTIAL-BASED PATH PLANNING ALGORITHM FOR HYPER-REDUNDANT MANIPULATORS
(2010-04-01)
Lin, Chien-Chou
;
Chuang, Jen-Hui
;
資訊工程學系
;
Department of Computer Science
A novel collision avoidance algorithm based on the generalized potential model is proposed to solve the path-planning problem of hyper-redundant manipulators in 3D workspace. The approach computes repulsive force and torque between manipulator and obstacles by using the workspace information directly. A collision-free path for a manipulator can then be obtained by locally adjusting the manipulator configuration to search for minimum potential configurations using these forces and torques. The proposed approach is efficient since these potential gradients are analytically tractable. Furthermore, the proposed algorithm is also extended to dual-arm systems. Simulation results show that the proposed algorithm works well, in terms of computation time and collision avoidance.
An alternate priority planning algorithm for dual-arm systems
(2006)
Chuang, Jen-Hui
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Lin, Chien-Chou
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Chou, Tsun-Hou
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資訊工程學系
;
Department of Computer Science
An alternate priority path planning algorithm for dual-manipulator systems is proposed in this paper. A master-slave architecture is used to deal with the coordination of two manipulators by alternately identifying configurations of the two manipulators. The proposed method utilizes a generalized potential field to evaluate repulsion between manipulators and obstacles in a workspace, so collision avoidance of the planned path can be guaranteed. The simulation results show that proposed algorithm is efficient, even in a narrow passage.
Finding point correspondence using local similarity and global constraint
(2006)
Chuang, Jen-Hui
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Kao, Jau-Hong
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Lin, Chien-Chou
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資訊工程學系
;
Department of Computer Science
Establishing feature point correspondences from a pair of stereo images or a long sequence of images is a very important research topic in computer vision. In this paper, an algorithm using local similarity and global constraint to obtain point correspondence is proposed. The point correspondences are obtained by comparing the color codes, computed by image gradients obtained as by-products from the corner detector, and spatial relationships among neighboring feature points.
Two new, near-infrared, fluorescent probes as potential tools for imaging bone repair
(2020-02-13)
Lin, Chien-Chou
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Chang, Walter Hong-Shong
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Cheng, Tsai-Mu
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Chiu, Li-Hsuan
;
Wang, Yen-Hsun
A precise imaging technique to evaluate osteogenesis, osteodifferentiation, and osseointegration following peri-implant surgery is in high clinical demand. Herein, we report the generation of two new, near-infrared (NIR) fluorescent probes for use in the molecular imaging of bone repair. The first probe aims to monitor the in vitro differentiation of human mesenchymal stem cells (MSCs) into osteoblasts. A NIR fluorochrome was conjugated to a cyclic peptide that binds to integrin alpha 5 beta 1, a factor that promotes osteogenesis in MSCs and therefore functioned as an osteoblast-specific marker. The second probe aims to monitor osteogenesis, and was generated by conjugating the drug pamidronate to a NIR fluorescent gold nanocluster. Pamidronate specifically binds to hydroxyapatite (HA), a mineral present in bone that is produced by osteoblasts, and therefore provides a functional marker for new bone formation. Our results show that both probes bind to their specific targets in vitro-differentiated osteoblasts, and not to undifferentiated MSCs, and emit NIR fluorescence for functional detection. This in vitro work demonstrates the ability of these probes to bind to active osteoblasts and their mineral deposits and highlight their potential utility as clinical tools for the imaging of the osseointegration process at the molecular level.