arXiv subjects
Publications and source records attributed to Victor Wu.
We show that the $C^*$-algebra of a countable directed graph is singly generated. As a consequence, any $C^*$-algebra generated by a countable family of projections and partial isometries satisfying Cuntz-Krieger relations is singly generated.
We introduce $C^*$-algebras associated to directed graphs of groups. In particular, we associate a combinatorial $C^*$-algebra to each row-finite directed graph of groups with no sources, and show that this $C^*$-algebra is Morita equivalent to the crossed product coming from the corresponding group action on the boundary of a directed tree. Finally, we show that these $C^*$-algebras (and their Morita equivalent crossed products) contain the class of stable UCT Kirchberg algebras.
We study group actions on multitrees, which are directed graphs in which there is at most one directed path between any two vertices. In our main result we describe a six-term exact sequence in $K$-theory for the reduced crossed product $C_0(\partial E)\rtimes_r G$ induced from the action of a countable discrete group $G$ on a row-finite, finitely-aligned multitree $E$ with no sources. We provide formulas for the $K$-theory of $C_0(\partial E) \rtimes_r G$ in the case where $G$ acts freely on $E$, and in the case where all vertex stabilisers are infinite cyclic. We study the action $G\curvearrowright \partial E$ in a range of settings, and describe minimality, local contractivity, topological freeness, and amenability in terms of properties of the underlying data. In an application of our main theorem, we describe a six-term exact sequence in $K$-theory for the crossed product induced from a group acting on the boundary of an undirected tree.
This paper presents the development of a multi-sensor user interface to facilitate the instruction of arc welding tasks. Traditional methods to acquire hand-eye coordination skills are typically conducted through one-to-one instruction where trainees must wear protective helmets and conduct several tests. This approach is inefficient as the harmful light emitted from the electric arc impedes the close monitoring of the process; Practitioners can only observe a small bright spot. To tackle these problems, recent training approaches have leveraged virtual reality to safely simulate the process and visualize the geometry of the workpieces. However, the synthetic nature of these types of simulation platforms reduces their effectiveness as they fail to comprise actual welding interactions with the environment, which hinders the trainees' learning process. To provide users with a real welding experience, we have developed a new multi-sensor extended reality platform for arc welding training. Our system is composed of: (1) An HDR camera, monitoring the real welding spot in real-time; (2) A depth sensor, capturing the 3D geometry of the scene; and (3) A head-mounted VR display, visualizing the process safely. Our innovative platform provides users with a "bot trainer", virtual cues of the seam geometry, automatic spot tracking, and performance scores. To validate the platform's feasibility, we conduct extensive experiments with several welding training tasks. We show that compared with the traditional training practice and recent virtual reality approaches, our automated multi-sensor method achieves better performances in terms of accuracy, learning curve, and effectiveness.
This paper presents a point cloud based robotic system for arc welding. Using hand gesture controls, the system scans partial point cloud views of workpiece and reconstructs them into a complete 3D model by a linear iterative closest point algorithm. Then, a bilateral filter is extended to denoise the workpiece model and preserve important geometrical information. To extract the welding seam from the model, a novel intensity-based algorithm is proposed that detects edge points and generates a smooth 6-DOF welding path. The methods are tested on multiple workpieces with different joint types and poses. Experimental results prove the robustness and efficiency of this robotic system on automatic path planning for welding applications.
In this paper, in order to pursue high-efficiency robotic arc welding tasks, we propose a method based on point cloud acquired by an RGB-D sensor. The method consists of two parts: welding groove detection and 3D welding trajectory generation. The actual welding scene could be displayed in 3D point cloud format. Focusing on the geometric feature of the welding groove, the detection algorithm is capable of adapting well to different welding workpieces with a V-type welding groove. Meanwhile, a 3D welding trajectory involving 6-DOF poses of the welding groove for robotic manipulator motion is generated. With an acceptable error in trajectory generation, the robotic manipulator could drive the welding torch to follow the trajectory and execute welding tasks. In this paper, details of the integrated robotic system are also presented. Experimental results prove application value of the presented welding robotic system.