An improved optimal algorithm for collision detection of hybrid hierarchical bounding box


Collision detection is currently a hot issue in virtual reality and other fields. The efficiency and accuracy of collision detection directly affect the real-time update effect of the virtual reality environment, and it is also an important indicator that affects the user's interactive experience. In a complex virtual reality scene, if the traditional collision detection algorithm (Sphere-OBB) is adopted and the tree structure traversal is used to realize the bounding box traversal detection, the accuracy remains unchanged, but the detection complexity is reduced. If the RAPID collision detection algorithm is used, the separated axis test method and the two-layer hybrid hierarchical surrounding tree structure are used, although the amount of calculation is large, the detection efficiency is improved. Using the separation axis (SAT) algorithm and using the separation axis theorem to determine the vector axis can save a lot of calculation time. The purpose of this research is to propose an improved hybrid-level bounding box collision detection optimization algorithm (ASO) based on the traditional hybrid-level bounding box collision detection algorithm. Firstly, based on the spatio-temporal correlation theory, the hybrid hierarchical bounding box hierarchical tree structure is improved to AABB and OBB from top to bottom. The synchronous descent rule is used to realize the traversal of nodes, and then the triangle area weighting method is used to improve the calculation method of the bottom OBB bounding box node center, solve the bounding box vertex covariance matrix, and improve the efficiency and accuracy of collision detection. The experimental results show that the algorithm proposed in this paper is 35.6% faster than the RAPID detection speed and 29.9% faster than the separation axis (SAT) detection speed under the same accuracy. In the multi-object collision detection, compared with the latest research, the algorithm in this paper shortens the intersection detection time, improves the collision detection efficiency, and meets the real-time update requirements of complex virtual reality scenes.

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  1. 1.

    Lv Z (2020) Virtual reality in the context of Internet of Things. Neural Comput Appl 32:9593–9602

    Article  Google Scholar 

  2. 2.

    Wohlgenannt I, Simons A, Stieglitz S (2020) Virtual reality. Bus Inf Syst Eng 62:455–461

    Article  Google Scholar 

  3. 3.

    Li R, Xu D (2020) Distribution of landscape architecture based on 3D images and virtual reality rationality study. IEEE Access 8:140161–140170

    Article  Google Scholar 

  4. 4.

    Carol M, Huttar KBS (2020) Virtual reality and computer simulation in social work education: a systematic review. J Social Work Educ 56(1):131–141

    Article  Google Scholar 

  5. 5.

    Xing D, Liu F, Liu S, Xu D (2018) Efficient collision detection and detach control for convex prisms in precision manipulation. IEEE Trans Ind Inf 14(12):5316–5326

    Article  Google Scholar 

  6. 6.

    Hui YQ, Wen HL, Wei Z (2020) Human-vehicle collision detection algorithm based on image processing. Int J Patt Recogn Artif Intell 34(8):2055015

    Article  Google Scholar 

  7. 7.

    Römer UJ, Fidlin A, Seemann W (2020) The normal parameterization and its application to collision detection. Mech Mach Theory 151:103906

    Article  Google Scholar 

  8. 8.

    Wang F, Qian Z, Lin Y, Zhang W (2020) Design and rapid construction of a cost-effective virtual haptic device. IEEE/ASME Trans Mechatron.

    Article  Google Scholar 

  9. 9.

    Liu G, Huang X, Guo W (2015) Multilayer obstacle-avoiding X-architecture Steiner minimal tree construction based on particle swarm optimization. IEEE Trans Cybern 45(5):989–1002

    Google Scholar 

  10. 10.

    Jong MK, Yeongho S, Hoe MK, Taesoo K (2020) Interactive character posing with efficient collision handling. Comput Anim Virtual Worlds 30(3):e1923

    Google Scholar 

  11. 11.

    Wang X, Zhang J, Zhang W (2020) The distance between convex sets with Minkowski sum structure: application to collision detection. Comput Optim Appl 77:465–490

    MathSciNet  Article  Google Scholar 

  12. 12.

    Xie M, Niu X (2020) A 3D roaming and collision detection algorithm applicable for massive spatial data. PLoS One 15(2):e0229038

    Article  Google Scholar 

  13. 13.

    Li J, Wang MY (2017) Optimization of hybrid hierarchical bounding box collision detection algorithm under the background of big data. J Jilin Univ (Sci Ed) 553(3):673–677

    Google Scholar 

  14. 14.

    Kabanov AA, Tokarev DA (2020) Collision detection and avoidance method for two cooperative robot manipulators. In: IOP conference series: materials science and engineering, vol 709, pp 044021

  15. 15.

    Jin YX, Cheng QF, Zhang JR, Qi X, Ma B, Jia Y (2020) Self-collision detection algorithm based on fused DNN and AABB-circular bounding box. J Image Graph 25(8):1674–1683

    Google Scholar 

  16. 16.

    Zheng XX, Xie MH, Zhang YY (2017) Fast collision detection algorithm based on spatial partition and linear programming. Comput Eng Appl 53(23):236–240

    Google Scholar 

  17. 17.

    Guo Y, Wang J, Zhou X, Tan Z, Qiu K (2020) A hybrid framework based on warped hierarchical tree for pose estimation of texture-less objects. IEEE Access 8:179813–179822

    Article  Google Scholar 

  18. 18.

    Floyd MC, Christophe D, Taku K (2020) Binary ostensibly-implicit trees for fast collision detection. Comput Graph Forum 39(2):509–521

    Article  Google Scholar 

  19. 19.

    Yu RY, Zhao JL (2018) Collision detection algorithm based on axis aligned bounding box and space partition. Chin J Image Graph 23(12):1925–1937

    Google Scholar 

  20. 20.

    Tang YH, Hou J, Wu TT et al (2018) Hybrid collision detection algorithm based on particle transformation and bounding box. J Harbin Eng Univ 39(10):1695–1701

    Google Scholar 

  21. 21.

    Xiao L, Mei G, Cuo MSl (2020) Comparative investigation of GPU-accelerated triangle–triangle intersection algorithms for collision detection. Multimed Tools Appl.

    Article  Google Scholar 

  22. 22.

    Wang H, Zhang X, L. Zhou X (2020) Intersection detection algorithm based on hybrid bounding box for geological modeling with faults. IEEE Access 8:29538–29546

    Article  Google Scholar 

  23. 23.

    Jin YX, Qin ZP, Li Z (2017) Collision detection algorithm for deformable body based on R-sphere bounding sphere. Comput Eng Des 38(1):92–96

    Google Scholar 

  24. 24.

    Xu J, Liu Z, Yang C, Li L, Pei Y (2020) A pseudo-distance algorithm for collision detection of manipulators using convex-plane-polygons-based representation. Robot Comput Integrat Manuf 66:10199

    Google Scholar 

  25. 25.

    Tang M, Tong R, Wang W, Manocha D (2014) Fast and exact continuous collision detection with Bernstein sign classification. ACM Trans Graph 33(6):1–8

    Article  Google Scholar 

  26. 26.

    Tian Y, Li Y, Pan L, Morris H (2020) Research on group animation design technology based on artificial fish swarm algorithm. J Intell Fuzzy Syst 38:1137–1145

    Article  Google Scholar 

  27. 27.

    Park J, Baek H (2020) Stereo vision-based obstacle collision avoidance for a quadrotor using ellipsoidal bounding box and hierarchical clustering. Aerosp Sci Technol 103:105882

    Article  Google Scholar 

  28. 28.

    Capellman J, Salin L (2020) Collision detection. MonoGame mastery. Apress, Berkeley

    Google Scholar 

  29. 29.

    Xia C, Zhang B, Wang H, Qiao S, Zhang A (2020) A minimum-volume oriented bounding box strategy for improving the performance of urban cellular automata based on vectorization and parallel computing technology. GIScience Remote Sens 57(1):91–106

    Article  Google Scholar 

  30. 30.

    Song T, Shu T, Mei Z et al (2016) A collision detection algorithm based on spatial partitioning and hybrid bounding box. Firepower Command Control 41(11):94–97

    Google Scholar 

  31. 31.

    Weghorst H, Hooper G, Greenberg DP (1984) Improved computational methods for ray tracing. ACM Trans Comput Graph 3(1):52–69

    Article  Google Scholar 

  32. 32.

    Gottschalk S, Lin M, 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, pp 171–180

  33. 33.

    Liu D, Shi G (2020) Ship collision risk assessment based on collision detection algorithm. IEEE Access 8:161969–161980

    Article  Google Scholar 

  34. 34.

    Friston SJ, Steed A (2018) Real-time collision detection for deformable characters with radial fields. IEEE Trans Visual Comput Graph 99:1–12

    Google Scholar 

  35. 35.

    Malzer C, Baum M (2020) A hybrid approach to hierarchical density-based cluster selection. In: 2020 IEEE international conference on multisensor fusion and integration for intelligent systems (MFI). Karlsruhe, Germany, pp 223–228

  36. 36.

    Chen C, Pan Y, Li D, Zhang S, Zhao Z, Hong J (2020) A virtual-physical collision detection interface for AR-based interactive teaching of robot. Robot Comput Integr Manuf 64:101948

    Article  Google Scholar 

  37. 37.

    Geng C, Gao F (2018) An improved algorithm of the collision detection based on OBB. In: 2018 Second international conference of sensor network and computer engineering. Atlantis Press, pp 35–38

  38. 38.

    Qian J, Zheng Y, Qi D (2020) Multi-scale rotated bounding box-based deep learning method for electric railway detection. In: 2020 39th Chinese control conference (CCC), Shenyang, China, pp 7166–7171

  39. 39.

    Wang C, Zhang ZL, Long Y, Wang SD (2018) Improved hybrid bounding box collision detection algorithm. J Syst Simul 30(11):4236–4243

    Google Scholar 

  40. 40.

    Melero FJ, Aguilera Á, Feito FR (2019) Fast collision detection between high resolution polygonal models. Comput Graph 83:97–106

    Article  Google Scholar 

  41. 41.

    Zhang H, Zhang P, Hou H (2019) An improved collision detection algorithm for hair simulation study. In: 2019 IEEE 3rd advanced information management, communicates, electronic and automation control conference, Chongqing, China, pp 1408–1411

Download references


Research on interactive design of 3D animation based on virtual reality technology (no. 2018GkQNCX042). Research on the mechanism of urban waste classification and recycling in the artificial intelligence environment (no. 2020GZGJ315). Research and implementation of online course knowledge recommendation system based on learning diagnosis model (no. 2020KTSCX378). Research on the third language teaching quality monitoring mechanism based on PDCA cycle theory (no. 2020WQNCX109). mooc+spoc hybrid teaching model oriented to deep learning (no. 19GGZ006). Self-construction and application of English vocabulary corpus in the context of big data (no. 2020WQNCX111). Research on key technologies of channel heterogeneous content distribution network (no. 2020ZDZX3108).

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Gan, B., Dong, Q. An improved optimal algorithm for collision detection of hybrid hierarchical bounding box. Evol. Intel. (2021).

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  • Bounding box
  • Collision detection algorithm
  • Artificial intelligence
  • Hybrid structure
  • Virtual reality