SpringerLink
Log in

Graphic-enhanced collision detection for robotic manufacturing applications in complex environments

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

High-performance collision detection is the key to achieving automatic trajectory planning and secure motion control for industrial robots. However, most existing solutions reported thus far are difficult to guarantee computational accuracy and efficiency simultaneously when it comes to complex manufacturing environments. Therefore, this work presents a graphic-enabled collision detection method for robotic manufacturing applications in complex environments. With this method, a collision detection model that can be applied to arbitrarily complex polyhedrons is first established by using graphics depth peeling technology. A rapid collision detection algorithm utilizing a programmable graphics pipeline of GPU is developed. Then, the proposed collision detection model is extended to robotic manufacturing applications in complex environments. The effectiveness and practicality of the proposed method are verified through comparative simulation experiments, which have shown that the authors’ method can greatly improve the computational efficiency, robustness, and memory consumption when compared with the existing methods, showing great application potential in collision-free trajectory planning and motion planning for robotic manufacturing applications in complex environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

All the data supporting the results are included within the article.

References

  1. Wang WB, Guo Q, Yang ZB, Jiang Y, Xu JT (2023) A state-of-the-art review on robotic milling of complex parts with high efficiency and precision. Robot Comput Integr Manuf 79:102436. https://doi.org/10.1016/j.rcim.2022.102436

    Article  Google Scholar 

  2. Liao ZY, Li JR, **e HL, Wang QH, Zhou XF (2020) Region-based toolpath generation for robotic milling of freeform surfaces with stiffness optimization. Robot Comput Integr Manuf 64:101953. https://doi.org/10.1016/j.rcim.2020.101953

    Article  Google Scholar 

  3. **e HL, Wang QH, Ong SK, Nee AYC, Zhou XF, Liao ZY (2022) Automatic generation of interference-free and posture-smooth toolpath for robotic belt grinding of complex workpieces. IEEE/ASME Trans Mechatron 28(1):518–530. https://doi.org/10.1109/TMECH.2022.3205852

    Article  Google Scholar 

  4. Wang QH, Fang XL, **e HL, Li JR, Liao ZY (2022) Rapid prediction of multi-directionality of polished surface topography based on angular spectrum. Int J Adv Manuf Technol 122(7):2871–2886. https://doi.org/10.1007/s00170-022-09906-6

    Article  Google Scholar 

  5. Safeea M, Neto P (2019) Minimum distance calculation using laser scanner and IMUs for safe human-robot interaction. Robot Comput Integr Manuf 58:33–42. https://doi.org/10.1016/j.rcim.2019.01.008

    Article  Google Scholar 

  6. Wen YL, Pagilla PR (2023) Path-constrained and collision-free optimal trajectory planning for robot manipulators. IEEE Trans Autom Sci Eng 20(2):763–774. https://doi.org/10.1109/TASE.2022.3169989

    Article  Google Scholar 

  7. Jiang LH, Liu SY, Cui YM, Jiang HX (2022) Path planning for robotic manipulator in complex multi-obstacle environment based on improved_RRT. IEEE/ASME Trans Mechatron 27(6):4774–4785. https://doi.org/10.1109/TMECH.2022.3165845

    Article  Google Scholar 

  8. Xu JT, Liu ZF, Zhang CX, Yang CB, Pei YH (2020) Minimal distance calculation between the industrial robot and its workspace based on circle/polygon-slices representation. Appl Math Model 87:691–710. https://doi.org/10.1016/j.apm.2020.06.012

    Article  MathSciNet  Google Scholar 

  9. Boschetti G, Faccio M, Granata I, Minto R (2023) 3D collision avoidance strategy and performance evaluation for human–robot collaborative systems. Comput Ind Eng 179:109225. https://doi.org/10.1016/j.cie.2023.109225

    Article  Google Scholar 

  10. Lin MC, Canny JF (1991) A fast algorithm for incremental distance calculation. In: IEEE International Conference on Robotics and Automation (ICRA), Sacramento, pp 1008–1014. https://doi.org/10.1109/ROBOT.1991.131723

  11. Ong CJ, Gilbert EG (2001) Fast versions of the Gilbert-Johnson-Keerthi distance algorithm: additional results and comparisons. IEEE Trans Robot Autom 17(4):531–539. https://doi.org/10.1109/70.954768

    Article  Google Scholar 

  12. Dietrich A, Wimbock T, Albu-Schaffer A, Hirzinger G (2012) Reactive whole-body control: dynamic mobile manipulation using a large number of actuated degrees of freedom. IEEE Robot Autom Mag 19(2):20–33. https://doi.org/10.1109/MRA.2012.2191432

    Article  Google Scholar 

  13. Montanari M, Petrinic N, Barbieri E (2017) Improving the GJK algorithm for faster and more reliable distance queries between convex objects. ACM Trans Graph 36(3):30. https://doi.org/10.1145/3083724

    Article  Google Scholar 

  14. Han D, Nie H, Chen JB, Chen M (2018) Dynamic obstacle avoidance for manipulators using distance calculation and discrete detection. Robot Comput Integr Manuf 49:98–104. https://doi.org/10.1016/j.rcim.2017.05.013

    Article  Google Scholar 

  15. Palmer IJ, Grimsdale RL (1995) Collision detection for animation using sphere-trees. Comput Graph Forum 14(2):105–116. https://doi.org/10.1111/1467-8659.1420105

    Article  Google Scholar 

  16. Gvd B (1997) Efficient collision detection of complex deformable models using AABB trees. J Graphics Tools 2(4):1–13. https://doi.org/10.1080/10867651.1997.10487480

    Article  Google Scholar 

  17. Gottschalk S, Lin MC, Manocha D (1996) OBBTree: a hierarchical structure for rapid interference detection. In: Proceedings of the 23rd annual conference on Computer graphics and interactive techniques: Association for Computing Machinery, pp 171–180

    Chapter  Google Scholar 

  18. Klosowski JT, Held M, Mitchell JSB, Sowizral H, Zikan K (1998) Efficient collision detection using bounding volume hierarchies of k-DOPs. IEEE Trans Vis Comput Graph 4(1):21–36. https://doi.org/10.1109/2945.675649

    Article  Google Scholar 

  19. Fan Q, Tao B, Gong ZY, Zhao XW, Ding H (2023) Fast global collision detection method based on feature-point-set for robotic machining of large complex components. IEEE Trans Autom Sci Eng 20(1):470–481. https://doi.org/10.1109/TASE.2022.3157731

    Article  Google Scholar 

  20. Huang Z, Yang X, Min J, Wang HY, Wei PX (2021) Collision detection algorithm on abrasive belt grinding blisk based on improved octree segmentation. Int J Adv Manuf Technol 118:4105–4121. https://doi.org/10.1007/s00170-021-08213-w

    Article  Google Scholar 

  21. Guo XY, Wang R, Zhong SS, Ge YH, Yue LY (2023) Boundary construction method of collision avoidance for conventional cutters. Int J Adv Manuf Technol 127:65–80. https://doi.org/10.1007/s00170-023-11419-9

    Article  Google Scholar 

  22. Morimoto K, Inui M (2007) A GPU based algorithm for determining the optimal cutting direction in deep mold machining. In: 2007 IEEE int symp assem manuf IEEE; 2007, pp 203–208

    Google Scholar 

  23. Bi QZ, Wang YH, Ding H (2010) A GPU-based algorithm for generating collision-free and orientation-smooth five-axis finishing tool paths of a ball-end cutter. Int J Prod Res 48(4):1105–1124. https://doi.org/10.1080/00207540802570685

    Article  Google Scholar 

  24. Wang QH, Li JR, Gong HQ (2007) Graphics-assisted cutter orientation correction for collision-free five-axis machining. Int J Prod Res 45(13):2875–2894. https://doi.org/10.1080/00207540600767798

    Article  Google Scholar 

  25. **e HL, Wang QH, Liao ZY, Li JR, Zhou XF (2022) A rapid approach of computing multi-axis tool accessible space for general cutter model by utilizing programmable graphics pipeline. IEEE/ASME Trans Mechatron 27(5):3373–3384. https://doi.org/10.1109/TMECH.2021.3135417

    Article  Google Scholar 

  26. Kessenich J, Sellers G, Shreiner D (2016) OpenGL® programming guide: the official guide to learning Opengl, version 4.5 with SPIR-V, 9th edn. Addison-Wesley, Reading, MA, USA

    Google Scholar 

  27. Yeon-Ho I, Chang-Young H, Lee-Sup K (2005) A method to generate soft shadows using a layered depth image and war**. IEEE Trans Vis Comput Graph 11(3):265–272. https://doi.org/10.1109/TVCG.2005.37

    Article  Google Scholar 

  28. Schroeder WJ, Avila LS, Hoffman W (2000) Visualizing with VTK: a tutorial. IEEE Comput Graph Appl 20(5):20–27. https://doi.org/10.1109/38.865875

    Article  Google Scholar 

Download references

Funding

This work was partially supported by Key-Area Research and Development Program of Guangdong Province, China (grant number 2021B0101190002); Science & Technology Research Program of Guangzhou, China (grant number 202103020004); and Basic and Applied Basic Research Special Project of Guangzhou, China (grant number SL2024A04J00939).

Author information

Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, China

    **g-Rong Li, Rui-Hui **n, Qing-Hui Wang & Yue-Feng Li

  2. School of Design, South China University of Technology, Guangzhou, 510006, China

    Hai-Long **e

Authors
  1. **g-Rong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui-Hui **n
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qing-Hui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yue-Feng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hai-Long **e
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.-R.L.: supervision and writing, review and editing. R.-H.X.: methodology; software; experiment and data processing, and writing, original draft. Q.-H.W.: funding acquisition, resources, and project administration. Y.-F.L.: software and formal analysis. H.-L.X.: conceptualization; methodology; writing, review and editing; and format analysis.

Corresponding author

Correspondence to Hai-Long **e.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

The authors agree to participate in this research.

Consent for publication

The authors agree to the publication of the article.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, JR., **n, RH., Wang, QH. et al. Graphic-enhanced collision detection for robotic manufacturing applications in complex environments. Int J Adv Manuf Technol 130, 3291–3305 (2024). https://doi.org/10.1007/s00170-023-12851-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-12851-7

Keywords

Search

Navigation