Research on Path Planning Method of an Unmanned Vehicle in Urban Road Environments

  • Yu RuixingEmail author
  • Zhu Bing
  • Cao Meng
  • Zhao Xiao
  • Wang Jiawen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11010)


Path planning is one of the crucial technologies for autonomous driving of unmanned vehicle. It is considerably difficult for unmanned vehicles to perform a path planning assignment in an urban environment due to the complexity of the environmental constraints. In order to solve this problem, a new path planning method is introduced in this paper. We did not regard the unmanned vehicle as a particle, but selected the front-wheel-drive model combined with the mechanical constrains to calculate the path of the vehicle. The constraints of the external environment and mechanical limitations are introduced into different objective functions. This paper also proposes an algorithm based integration of the A* and Stochastic Fractal Search (SFS) algorithms. The integrated algorithm searches for the optimal path, which is the shortest path to the target location of the vehicle’s rear axle mid-point in the raster map, with the help of the A* algorithm. Then, based on the shortest path the SFS algorithm generates the vehicle path, which contains the vehicle status information. Finally, the A* and SFS algorithms are compared under the same simulation environment when searching for a path of the vehicle’s rear suspension. The results simulated on MATLAB R2012a show that the composite method we propose is effective in solving the path planning problem in an urban environment.


Path planning Stochastic Fractal Search A* algorithm Grid map Dynamic programming 



This work was sponsored by Aviation Science Fund Project (20160153001), SAST Foundation (Grant No.SAST2015040), Shaanxi Provincial Department of Education Scientific Research Plan Special Project (17JK0599), Xi’an Shiyou University Youth Science and Technology Innovation Fund project (2015BS18).


  1. 1.
    Wei, S., Zhengda, M.: Smooth path design based on improved A* algorithm. J. Southeast Univ. (Nat. Sci.) s1, 155–161 (2010)Google Scholar
  2. 2.
    Koenig, S., Likhachev, M.: Improved fast replanning for robot navigation in unknown terrain. In: IEEE International Conference on Robotics and Automation, vol. 1, pp. 968–975 (2002)Google Scholar
  3. 3.
    Nyerges, Á., Tihanyi, V.: Trajectory Planning for Automated Vehicles – A Basic Approach. In: Jármai, K., Bolló, B. (eds.) VAE 2018. LNME, pp. 403–412. Springer, Cham (2018). Scholar
  4. 4.
    Lau, G.T.L.: Path planning algorithms for autonomous border patrol vehicles. School of Graduate Studies, Theses (2012)Google Scholar
  5. 5.
    Pamosoaji, A.K., Hong, K.S.: A path-planning algorithm using vector potential functions in triangular regions. IEEE Trans. Syst. Man Cybern. Part B 43(4), 832–842 (2013)CrossRefGoogle Scholar
  6. 6.
    Yang, K., Gan, S.K., Sukkarieh, S.: An Efficient Path Planning and Control Algorithm for RUAV’s in Unknown and Cluttered Environments. In: Valavanis, K.P., Beard, R., Oh, P., Ollero, A., Piegl, L.A., Shim, H. (eds.) Selected papers from the 2nd International Symposium on UAVs, vol. 57, pp. 1–4. Springer, Dordrecht (2009). Scholar
  7. 7.
    Saravanakumar, S., Asokan, T.: Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space. Intell. Serv. Robot. 6(4), 211–224 (2013)CrossRefGoogle Scholar
  8. 8.
    González, D., Pérez, J., Milanés, V.: Parametric-Based Path Generation for Automated Vehicles at Roundabouts. Pergamon Press, Inc., Oxford (2017)CrossRefGoogle Scholar
  9. 9.
    Ji, J., Khajepour, A., Melek, W.W., et al.: Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans. Veh. Technol. 66(2), 952–964 (2017)CrossRefGoogle Scholar
  10. 10.
    Rahman, M.A.: Multi agent coordinated path planning using imporved artificial potential field-based regression search method. Dissertations and Theses, Gradworks (2014)Google Scholar
  11. 11.
    Li, B., Shao, Z.: Precise trajectory optimization for articulated wheeled vehicles in cluttered environments. Adv. Eng. Softw. 92, 40–47 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yu Ruixing
    • 1
    Email author
  • Zhu Bing
    • 2
  • Cao Meng
    • 1
  • Zhao Xiao
    • 3
  • Wang Jiawen
    • 3
  1. 1.Northwestern Polytechnical UniversityXianPeople’s Republic of China
  2. 2.Xi’an Shiyou UniversityXianPeople’s Republic of China
  3. 3.Shanghai Institute of Satellite EngineeringShanghaiPeople’s Republic of China

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