Skip to main content
SpringerLink
Log in

High-quality trajectory planning for heterogeneous individuals

针对异质群体的高质量轨迹规划方法

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

Based on trajectory planning with maximum velocity and acceleration constraints, a novel high-quality trajectory planning method was proposed for heterogeneous individuals in crowd simulation. The proposed method ensured that the individual’s path was smooth and its velocity was continuous. Based on the physiological constraints of humans with maximum velocity and acceleration, time-optimal trajectory and feasible region were derived by solving kinodynamic planning problem. Subsequently, a high-quality trajectory planning algorithm was designed to compute the trajectory with appropriate variation of velocity. The simulation results demonstrate that the proposed trajectory planning method has more authenticities and can generate high-quality trajectories for virtual humans in crowd simulation.

摘要

基于具有最大速度和最大加速度约束的轨迹规划方法,针对人群仿真中的异质群体,提出一种 新颖的高质量轨迹规划方法。该方法能够保证个体的运动路径是平滑的,个体的速度是连续的。考虑 到真实人具有最大速度和最大加速度的生理学约束,本文首先推导出一种时间最优的轨迹和可行域来 解决kinodynamic规划问题。随后,通过计算具有恰当速度多样性的轨迹设计出一种高质量的轨迹规 划方法。仿真结果表明提出的轨迹规划方法具有更高的真实性,能够为人群仿真中的虚拟人提供高质 量的运动轨迹。

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. XUE Zhu-xin, DONG Qing, FAN Xiang-tao, JIN Qing-wen, JIAN Hong-deng, LIU Jian. Fuzzy logic-based model that incorporates personality traits for heterogeneous pedestrians [J]. Symmetry-Basel, 2017, 9(10). DOI: https://doi.org/10.3390/sym9100239.

    Google Scholar 

  2. LE P T, HOANG H V, SANG H A, SEUNG G L, DONG H K, TAE C C. Univector field method-based multi-agent navigation for pursuit problem in obstacle environments [J]. Journal of Central South University, 2017, 24(4): 1002–1012. DOI: https://doi.org/10.1007/s11771-017-3502-0.

    Article  Google Scholar 

  3. XU Ming-liang, LI Chao-chao, LV Pei, CHEN Wei, DINESH M, DENG Zhi-gang, ZHOU Bing. Crowd simulation model integrating physiology-psychology-physics factors [J]. Computing Research Repository (CoRR), 2018, 18(1): 1–14. https://dblp.uni-trier.de/pers/hd/x/Xu:Mingliang,2018-08-1/.

    Google Scholar 

  4. BELLOMO N, CLARKE D, GIBELLI L, TOWNSEND P, VREUGDENHIL B J. Human behaviours in evacuation crowd dynamics: From modelling to big data toward crisis management [J]. Physics of Life Reviews, 2016, 18: 1–21. DOI: https://doi.org/10.1016/j.plrev.2016.05.014.

    Article  Google Scholar 

  5. CHOSET H M, HUTCHINSON S, LYNCH K M, KANTOR G, BURGARD W, KAVRAKI L E, THRUN S. Principles of robot motion: Theory, algorithms, and implementations [M]. MIT Press, 2005.

    MATH  Google Scholar 

  6. NARANG S, BEST A, FENG A, KANG S H, MANOCHA D. Motion recognition of self and others on realistic 3D avatars [J]. Computer Animation and Virtual Worlds, 2017, 28(3, 4): e1762. DOI: https://doi.org/10.1002/cav.1762.

    Article  Google Scholar 

  7. KAPADIA M, PELECHANO N, ALLBECK J. Virtual crowds: Steps toward behavioral realism [M]. Synthesis Lectures on Visual Computing: Morgan & Claypool Publishers, 2015.

    Google Scholar 

  8. LI Meng, LIANG Jia-hong, LI Shi-lei. Flocking behavior with multiple leaders and global trajectory [J]. Journal of Central South University, 2014, 21(6): 2324–2333. DOI: https://doi.org/10.1007/s11771-014-2184-0.

    Article  MathSciNet  Google Scholar 

  9. WALLAR A, PLAKU E. Path planning for swarms by combining probabilistic roadmaps and potential fields [C]// 14th Annual Conference on Towards Autonomous Robotic Systems. Berlin, Heidelberg, 2013: 417–428. DOI: https://doi.org/10.1007/978-3-662-43645-5_43.

    Google Scholar 

  10. LIU Qian. The effect of dedicated exit on the evacuation of heterogeneous pedestrians [J]. Physica A-Statistical Mechanics and Its Applications, 2018, 506: 305–323. DOI: https://doi.org/10.1016/j.physa.2018.04.032.

    Article  Google Scholar 

  11. CHARALAMBOUS P, CHRYSANTHOU Y. The PAG Crowd: A graph based approach for efficient data-driven crowd simulation [C]//Computer Graphics Forum, 2014, 33(8): 95–108. DOI: https://doi.org/10.1111/cgf.12403.

    Article  Google Scholar 

  12. KREMYZAS A, JAKLIN N, GERAERTS R. Towards social behavior in virtual-agent navigation [J]. Science China Information Sciences, 2016, 59(11): 1–17. DOI: https://doi.org/10.1007/s11432-016-0074-9.

    Article  Google Scholar 

  13. BEST A, NARANG S, CURTIS S, MANOCHA D. Densesense: Interactive crowd simulation using density-dependent filters [C]//Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Copenhagen, Denmark, 2014: 97–102. http://gamma.cs.unc.edu/DenseSense/,2018-08-1/.

    Google Scholar 

  14. MARCELO K, MUBBASIR K. Geometric and discrete path planning for interactive virtual worlds [C]//ACM SIGGRAPH 2016: the 43rd International Conference and Exhibition on Computer Graphics & Interactive Techniques. Anaheim, CA, 2016. DOI: https://doi.org/10.1145/2897826.2927310.

    Google Scholar 

  15. NORMAN J, ANGELOS K, ROLAND G. Adding sociality to virtual pedestrian groups [C]//Proceedings of the 21st ACM Symposium on Virtual Reality Software and Technology. Beijing, China, 2015: 163–172. DOI: https://doi.org/10.1145/2821592.282159.

    Google Scholar 

  16. YAN Zhe-ping, LIU Yi-bo, YU Chang-bin, Zhou Jia-jia. Leader-following coordination of multiple UUVs formation under two independent topologies and time-varying delays [J]. Journal of Central South University, 2017, 24(2): 382–393. DOI: https://doi.org/10.1007/s11771-017-3440-x.

    Article  Google Scholar 

  17. MARBLE J D, BEKRIS K E. Asymptotically near-optimal is good enough for motion planning [J]. Robotics Research, 2017: 419–436. DOI: https://doi.org/10.1007/978-3-319-29363-9_24.

    Chapter  Google Scholar 

  18. RAHMANI V, PELECHANO N. Improvements to hierarchical pathfinding for navigation meshes [C]//Proceedings of the Tenth International Conference on Motion in Games. Barcelona, Spain, 2017: 2–8. DOI: https://doi.org/10.1145/3136457.3136465.

    Google Scholar 

  19. NINOMIYA K, KAPADIA M, SHOULSON A, GARCIA F, BADLER N. Planning approaches to constraint-aware navigation in dynamic environments [J]. Computer Animation and Virtual Worlds, 2015, 26(2): 119–139. DOI: https://doi.org/10.1002/cav.1622.

    Article  Google Scholar 

  20. AGARWAL P K, FOX K, SALZMAN O. An efficient algorithm for computing high-quality paths amid polygonal obstacles [J]. ACM Transactions on Algorithms (TALG), 2018, 14(4): 46–67. DOI: https://doi.org/10.1145/3230650.

    MathSciNet  MATH  Google Scholar 

  21. LINO C, CHRISTIE M. Intuitive and efficient camera control with the toric space [J]. ACM Transactions on Graphics, 2015, 34(4): 1–12. DOI: https://doi.org/10.1145/2766965.

    Article  Google Scholar 

  22. LI Bai, WANG Ke-xin, SHAO Zhi-jiang. Time-optimal maneuver planning in automatic parallel parking using a simultaneous dynamic optimization approach [J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(11): 3263–3274. DOI: https://doi.org/10.1109/TITS.2016.2546386.

    Article  Google Scholar 

  23. LI Bai, LIU Hong, XIAO Duo, YU Gui-zhen, ZHANG You-min. Centralized and optimal motion planning for large-scale AGV systems: A generic approach [J]. Advances in Engineering Software, 2017, 106: 33–46. DOI: https://doi.org/10.1016/j.advengsoft.2017.01.002

    Article  Google Scholar 

  24. BROGAN D C, JOHNSON N L. Realistic human walking paths [C]//Proceedings of the 16th International Conference on IEEE Computer Animation and Social Agents, 2003: 94–101. DOI: https://doi.org/10.1109/CASA.2003.1199309.

    Google Scholar 

  25. TOHFEH F, FAKHARIAN A. Polynomial based optimal trajectory planning and obstacle avoidance for an omni-directional robot [C]//Proceedings of the 5th Conference on Artificial Intelligence and Robotics, 2015: 53–59. DOI: https://doi.org/10.1109/RIOS.2015.7270731.

    Google Scholar 

  26. BUTLER S D, MOLL M, KAVRAKI L E. A general algorithm for time-optimal trajectory generation subject to minimum and maximum constraints [C]//Proceedings of the Workshop on the Algorithmic Foundations of Robotics, 2016: 1–16. https://pdfs.semanticscholar.org/2875/f611c908e5bba796a72ffb5cf349d96009bc.pdf,2018-08-1/.

    Google Scholar 

  27. BUTLER S D. General algorithms for the time-optimal trajectory generation problem [D]. Houston, USA: Rice University, 2017.

    Google Scholar 

  28. CHEN Yu-xiao, PENG Huei, GRIZZLE J. Obstacle avoidance for low-speed autonomous vehicles with barrier function [J]. IEEE Transactions on Control Systems Technology, 2018, 26(1): 194–206. DOI: https://doi.org/10.1109/TCST.2017.2654063.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Army Academy of Artillery and Air Defense, Hefei, 230031, China

    Meng Li  (李猛)

  2. Department of Information Security, Naval University of Engineering, Wuhan, 430033, China

    Shi-lei Li  (李石磊)

  3. Communication Countmeasure Department, Aviation University of the Air Force, Changchun, 130022, China

    Yuan-zheng Ge  (葛渊峥)

Authors
  1. Meng Li  (李猛)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shi-lei Li  (李石磊)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuan-zheng Ge  (葛渊峥)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meng Li  (李猛).

Additional information

Foundation item: Project(1708085QF158) supported by the Natural Science Foundation of Anhui Province, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, M., Li, Sl. & Ge, Yz. High-quality trajectory planning for heterogeneous individuals. J. Cent. South Univ. 26, 654–664 (2019). https://doi.org/10.1007/s11771-019-4036-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-019-4036-4

Key words

关键词

Search

Navigation