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Tool-path generation for industrial robotic surface-based application

  • He LyuEmail author
  • Yue Liu
  • Jiao-Yang Guo
  • He-Ming Zhang
  • Ze-Xiang Li
Article
  • 24 Downloads

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 kd-tree 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.

Keywords

Industrial robot Tool path generation Simulation Intelligent manufacturing 

Notes

Acknowledgements

Funding was provided by Research Grants Council, University Grants Committee (Grant No. 16205915) and Innovation and Technology Commission (HK) (Grant No. TS/216/17FP).

References

  1. 1.
    Lee S, Li C, Kim D et al (2009) The direct teaching and playback method for robotic deburring system using the adaptive force-control. In: IEEE international symposium on assembly and manufacturing, 17–20 Nov 2009, Seoul, South Korea, pp 235–241Google Scholar
  2. 2.
    Kim HJ, Back J, Song JB (2009) Direct teaching and playback algorithm for peg-in-hole task using impedance control. J Inst Control Robot Syst 15(5):538–542CrossRefGoogle Scholar
  3. 3.
    Asakawa N, Toda K, Takeuchi Y (2002) Automation of chamfering by an industrial robot; for the case of hole on free-curved surface. Robot Comput Integr Manuf 18(5–6):379–385CrossRefGoogle Scholar
  4. 4.
    Nagata F, Kusumoto Y, Fujimoto Y et al (2007) Robotic sanding system for new designed furniture with free-formed surface. Robot Comput Integr Manuf 23(4):371–379CrossRefGoogle Scholar
  5. 5.
    Buckmaster DJ, Newman WS, Somes SD (2008) Compliant motion control for robust robotic surface finishing. In: World congress on intelligent control and automation, 25–27 June 2008, Chongqing, China, pp 559–564Google Scholar
  6. 6.
    Minetto R, Volpato N, Stolfi J et al (2017) An optimal algorithm for 3D triangle mesh slicing. Comput Aided Des 92:1–10CrossRefGoogle Scholar
  7. 7.
    Sun YW, Guo DM, Jia ZY et al (2006) Iso-parametric tool path generation from triangular meshes for free-form surface machining. Int J Adv Manuf Technol 28(7–8):721–726Google Scholar
  8. 8.
    Ding S, Mannan M, Poo AN et al (2003) Adaptive iso-planar tool path generation for machining of free-form surfaces. Comput Aided Des 35(2):141–153CrossRefGoogle Scholar
  9. 9.
    Ericson C (2004) Real-time collision detection. CRC Press, Boca RatonCrossRefGoogle Scholar
  10. 10.
    Bergen GVD (1997) Efficient collision detection of complex deformable models using AABB trees. J Graph Tools 2(4):1–13CrossRefzbMATHGoogle Scholar
  11. 11.
    Gottschalk S, Lin MC, Manocha D (1996) OBB tree: a hierarchical structure for rapid interference detection. In: Proceedings of the 23rd annual conference on computer graphics and interactive techniques, New Orleans, LA, USA, 4–9 August, pp 171–180Google Scholar
  12. 12.
    Klosowski JT, Held M, Mitchell JS et al (1998) Efficient collision detection using bounding volume hierarchies of k-dops. IEEE Trans Vis Comput Graph 4(1):21–36CrossRefGoogle Scholar
  13. 13.
    Larsen E, Gottschalk S, Lin MC et al (2000) Fast distance queries with rectangular swept sphere volumes. Proc IEEE Int Conf Robot Autom 4:3719–3726Google Scholar
  14. 14.
    Quinlan S (1994) Efficient distance computation between non- convex objects. In: Proceedings of the IEEE international conference on robotics and automation, 8–13 May, San Diego, USA, pp 3324–3329Google Scholar
  15. 15.
    Tropp O, Tal A, Shimshoni I (2006) A fast triangle to triangle intersection test for collision detection. Comput Anim Virtual Worlds 17(5):527–535CrossRefGoogle Scholar
  16. 16.
    Sabharwal CL, Leopold JL, McGeehan D (2013) Triangle-triangle intersection determination and classification to support qualitative spatial reasoning. Polibits 48:13–22CrossRefGoogle Scholar
  17. 17.
    Wald I, Havran V (2006) On building fast kd-trees for ray tracing, and on doing that in O(N log N). In: IEEE symposium on interactive ray tracing, Salt Lake City, USA, 18–20 Sept, pp 61–69Google Scholar
  18. 18.
    Lyu H, Song X, Dai D et al (2017) Tool path interpolation and redundancy optimization of manipulator. In: The 13th IEEE conference on automation science and engineering (CASE), 20–23 Aug, Xi’an, China, pp 770–775Google Scholar
  19. 19.
    The 3D modeling and visualization platform. http://www.anycad.net/. Accessed 30 March 2018
  20. 20.
    Murray RM, Li ZX, Sastry SS et al (1994) A mathematical introduction to robotic manipulation. Chemical Rubber Company Press, Boca RatonzbMATHGoogle Scholar
  21. 21.
  22. 22.
    Marvie controller product detail. http://www.googoltech.com.cn/product/mcp/marvie/142/. Accessed 2 Sept 2018

Copyright information

© Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronic and Computer EngineeringHong Kong University of Science and TechnologyHong KongPeople’s Republic of China
  2. 2.Hong Kong University of Science and Technology, Shenzhen Research InstituteShenzhenPeople’s Republic of China

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