Unmanned Aircraft Vehicle Path Planning Based on SVM Algorithm

  • Yanhong Chen
  • Wei Zu
  • Guoliang Fan
  • Hongxing Chang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)


This paper describes an approach of using image processing and patters classification techniques for navigating the unmanned aircraft vehicle in known irregular environment. In the case of 2D path planning, a feasible flight path connecting the start and goal point can be regarded as a separating surface that divides the space into two regions. This suggests a dual problem of first dividing the whole space into such two regions and then picking up the boundary as a path. We use support vector machine to solve this dual problem. SVM can generate a nonlinear separating surface based on the margin maximization principle. First, we generate a novel search space which contains flyable and no-fly regions from 3D surface of minimum risk and pick up key obstacle points as samples. Second, a safe and smooth path is generated through SVM. Results from simulations show that the path planner is able to plan an optimal path efficiently due to the simplicity of the search space.


Path planning Image processing Support vector machine Surface of minimum risk 



This work was supported by Knowledge innovation project of Chinese Academy of Sciences (YYYJ-1122).


  1. 1.
    Jingwen T, Meijuan G, Erhong L (2007) Dynamic collision avoidance path planning for mobile robot based on multi-sensor data fusion by support vector machine. In: IEEE international conference on mechatronics and automation, Harbin, pp 2779–2783Google Scholar
  2. 2.
    Scott A, Bortoff (2000) Path planning for UAVs. In: Proceedings of the American control conference, Chicago, pp 364–368Google Scholar
  3. 3.
    Bruno S, Mario M, Gianluca D, Jonh Koo T (2001) Vision based navigation for an unmanned aerial vehicle. In: Proceedings of the IEEE international conference on robotics and automation, Seoul, pp 1757–1765Google Scholar
  4. 4.
    Menon PKA, Kim E, Cheng VHL (1991) Optimal trajectory synthesis for terrain-following. J Guid Control Dyna 4(14):807–813CrossRefGoogle Scholar
  5. 5.
    Zhizhong H, Kehu X, Chunlin S (2000) A smooth algorithm of digital terrain model used in low-altitude penetration. J Nanjing Univ Aeronaut Astronaut 32(5):493–498Google Scholar
  6. 6.
    Chris B (2006) Pattern recognition and machine learning. Springer, BerlinGoogle Scholar
  7. 7.
    Jun M (2006) Support vector path planning. In: IEEE international conference on intelligent robots and systems, Beijing, pp 2894–2899Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yanhong Chen
    • 1
  • Wei Zu
    • 1
  • Guoliang Fan
    • 1
  • Hongxing Chang
    • 1
  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations