Dichotomy: Trajectory Planning Algorithm Based on Point Group Distribution

  • Naiting XuEmail author
  • Fan Yang
  • HaiMing Lian
  • Yi Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 657)


The historical navigation trajectory carries rich information about the spatiotemporal patterns of a moving target, as well as its transboundary potentials. To obtain the moving track of a ship or a plane, a large amount of point-to-point data is required via the measurements of satellites or aviation equipments. On the basis of time, we can easily draw a track. However, the distribution of the point-to-point data information provided by each detection unit is irregular due to the inconsistency of the standards of the data units. Some of the regional point groups are densely distributed, while the others may be very sparse. The point density can mess up the trajectory detection and make it not ideal to observe the behavior habits of a target unit. In this study, a dichotomy trajectory planning algorithm was proposed to fix the problem mentioned above. The target area was set as a grid with the points in the grid dichotomized. In that case, the distribution of the targeted point group after dichotomization can perform as similar as possible, and an approximate trajectory line was formed. Such a method considers the change of point density. Results were tested against the performance of the clustering, the random, and the greedy methods, showing that the former had a better adaptability in dealing with different data distribution. In this paper, a novel approximate estimation algorithm based on the density change rate is proposed. Finally, we will compare the performance of our dichotomous method with clustering method, random method, and greedy algorithm through experiments and show the trajectory fitting effect through different distribution data.


Random algorithm model Greedy algorithm model Clustering algorithm model Dichotomy algorithm model Point group distribution Trajectory Trajectory planning 


  1. 1.
    Feng Z, Zhu Y (2017) A survey on trajectory data mining: techniques and applications. IEEE Access 4:2056–2067CrossRefGoogle Scholar
  2. 2.
    Kong F, Lin X (2018) The method and application of big data mining for mobile trajectory of taxi based on MapReduce. Cluster Comput 6:1–8Google Scholar
  3. 3.
    Shan J, Ferreira J, Gonzalez MC (2017) Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans Big Data 3(2):208–219CrossRefGoogle Scholar
  4. 4.
    Yuan G, Sun P, Zhao J et al (2017) A review of moving object trajectory clustering algorithms. Artif Intell Rev 47(1):123–144Google Scholar
  5. 5.
    Krishnan S, Garg A, Patil S et al (2017) Transition state clustering: unsupervised surgical trajectory segmentation for robot learning. Int J Robot Res 36(13–14):027836491774331Google Scholar
  6. 6.
    Xia D, Lu X, Li H et al (2018) A MapReduce-based parallel frequent pattern growth algorithm for spatiotemporal association analysis of mobile trajectory big data. Complexity 2018:1–16Google Scholar
  7. 7.
    Wen-Bo HU, Wei H, Guo-Chao HU (2017) Trajectory adjoint pattern analysis based on OPTICS clustering and association analysis. Comput ModernizationGoogle Scholar
  8. 8.
    Kong X, Song X, Xia F et al (2017) LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data. World Wide Web-Internet Web Inf Syst (3):1–23Google Scholar
  9. 9.
    Mao JL, Jin CQ, Zhang ZG et al (2017) Anomaly detection for trajectory big data: advancements and framework. J SoftwGoogle Scholar
  10. 10.
    Xu N, Yi W et al (2019) Vector quantization: timeline-based location data extraction and route fitting for crowdsourcing. In: Proceedings of the 5th China High Resolution Earth Observation Conference, CHREOC 2018. Lecture notes in electrical engineering, vol 552, pp 28–36Google Scholar
  11. 11.
    Song Y, Liu LM, Han ZZ (2017) A track data compression method for use on radar. Electron Opt Control 24:89–92 + 98Google Scholar
  12. 12.
    Karimki V (1991) Effective circle fitting for particle trajectories. Nucl Instrum Methods Phys Res 305:187–191CrossRefGoogle Scholar
  13. 13.
    Liu FZ, Li HQ, Xiao B (2017) An adaptive track fitting algorithm. J Air Force Early Warning Acad 31:424–426 + 435Google Scholar
  14. 14.
    Liang F, Li H (2019) Research on an improved adaptive fitting algorithm of trajectory information. J Phys Conf Ser 1169:1–6Google Scholar
  15. 15.
    Peng Q, Guo B, Zhu J (2018) Trajectory fitting of aerial bomb based on combination of genetic programming and ant colony optimization. In: Proceedings of the 37th Chinese control conference, 25–27 July 2018, Wuhan, China, pp 4843–4848Google Scholar
  16. 16.
    Lee C, Xu Y (2000) Trajectory fitting with smoothing splines using velocity information. In: Proceedings-IEEE international conference on robotics and automation, vol 3, pp 2796–2801Google Scholar
  17. 17.
    Zhang S, Liu Y (2003) Prediction of moving target trajectory with sliding window polynomial fitting. Opto-Electron Eng 30(4):24–27Google Scholar
  18. 18.
    Ge L, Chen J, Li R (2017) Feedforward control based on Fourier series trajectory fitting method forfor industrialindustrial robot. In: Chinese control and decision conference, 28–30 May 2017Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Naiting Xu
    • 1
    • 2
    Email author
  • Fan Yang
    • 3
  • HaiMing Lian
    • 1
    • 2
  • Yi Wang
    • 1
    • 2
  1. 1.Institute of Electronics, Chinese Academy of SciencesSuzhouChina
  2. 2.Key Laboratory of Intelligent Aerospace Big Data Application TechnologySuzhouChina
  3. 3.Beijing University of Science and TechnologyBeijingChina

Personalised recommendations