Advances in Manufacturing ›› 2019, Vol. 7 ›› Issue (1): 64-72.doi: 10.1007/s40436-018-00246-x

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Tool-path generation for industrial robotic surface-based application

He Lyu1, Yue Liu2, Jiao-Yang Guo2, He-Ming Zhang2, Ze-Xiang Li1   

  1. 1 Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, People's Republic of China;
    2 Hong Kong University of Science and Technology, Shenzhen Research Institute, Shenzhen 518057, Guangdong Province, People's Republic of China
  • Received:2018-05-19 Online:2019-03-25 Published:2019-03-22
  • Contact: He Lyu E-mail:hlv@ust.hk
  • Supported by:
    Funding was provided by Research Grants Council, University Grants Committee (Grant No. 16205915) and Innovation and Technology Commission (HK) (Grant No. TS/216/17FP).

Abstract: Industrial robots are widely used in various applications such as machining, painting, and welding. There is a pressing need for a fast and straightforward robot programming method, especially for surface-based tasks. At present, these tasks are time-consuming and expensive, and it requires an experienced and skilled operator to program the robot for a specific task. Hence, it is essential to automate the tool-path generation in order to eliminate the manual planning. This challenging research has attracted great attention from both industry and academia. In this paper, a tool-path generation method based on a mesh model is introduced. The bounding box tree and kdtree are adopted in the algorithm to derive the tool path. In addition, the algorithm is integrated into an offline robot programming system offering a comprehensive solution for robot modeling, simulation, as well as tool-path generation. Finally, a milling experiment is performed by creating tool paths on the surface thereby demonstrating the effectiveness of the system.

The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-018-00246-x.pdf

Key words: Industrial robot, Tool path generation, Simulation, Intelligent manufacturing