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A novel feature-guided trajectory generation method based on point cloud for robotic grinding of freeform welds

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Abstract

Robotic grinding of welds on freeform surfaces poses an increasing challenge to automatic generation of grinding trajectory while conventional teaching-playback mode and off-line programming method are ineffective. This paper proposes a novel feature-guided trajectory generation method based on point cloud data to perform an efficient grinding process for welds on a freeform surface. The 3D contour of the workpiece was measured by a laser profile scanner. Parent curve of each scanning line was fitted by means of moving average filter, and then, the weld feature points were reliably extracted out of the scattered point cloud through two stages of feature recognition. To achieve the movement guidance of the manipulator, B-spline fitting method was conducted to generate a smooth 3D curve which was discretized into actual tool contact points by an optimized interpolation algorithm and computed the tool postures by cross multiply algorithm. By using robotic force control, the desired force was planned for every tool contact point in order to compensate the error of the processing path. Verification shows that the maximum root mean square root error of recognition of the proposed algorithm is less than 0.7 mm and the computational time is saved by 65.12% in comparison with the reverse engineering method.

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Availability of data and material

The data and material that support the findings of this study are available from the corresponding author, upon reasonable request.

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Funding

This work was supported by the National Key Research and Development Program of China (Grant numbers: 2018YFC0310400) and Guangzhou Risong Intelligent Technology Holding Co., Ltd. China (Grant numbers: 2020-L021).

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Authors and Affiliations

  1. Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Hengjian Feng, Xukai Ren, Lufeng Li, Xiaoqiang Zhang, Huabin Chen, Ze Chai & Xiaoqi Chen

  2. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Xiaoqi Chen

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  1. Hengjian Feng
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  2. Xukai Ren
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  3. Lufeng Li
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  4. Xiaoqiang Zhang
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  5. Huabin Chen
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  6. Ze Chai
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  7. Xiaoqi Chen
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Correspondence to Huabin Chen or Xiaoqi Chen.

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Feng, H., Ren, X., Li, L. et al. A novel feature-guided trajectory generation method based on point cloud for robotic grinding of freeform welds. Int J Adv Manuf Technol 115, 1763–1781 (2021). https://doi.org/10.1007/s00170-021-07095-2

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  • DOI: https://doi.org/10.1007/s00170-021-07095-2

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