SP-NN: A novel neural network approach for path planning

Shuai Li, Max Q.H. Meng, Wanming Chen, Yangming Li, Zhuhong You, Yajin Zhou, Lei Sun, Huawei Liang, Kai Jiang, Qinglei Guo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

19 Citations (Scopus)


In this paper, a neural network approach named shortest path neural networks (SP-NN) is proposed for real-time on-line path planning. Based on grid-based map and mapping this kind of map to neural networks, this proposed method is capable of generating the globally shortest path from the target position to the start position without collision with any obstacles. The dynamics of each neuron is distinctive to other previously presented methods by other researchers and ensures that the generated path is shortest without collision and that the state of neurons varied continuously. Extensive simulations show the efficiency of the presented method.
Original languageEnglish
Title of host publication2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)9781424417582
Publication statusPublished - 1 Jan 2007
Externally publishedYes
Event2007 IEEE International Conference on Robotics and Biomimetics, ROBIO - Yalong Bay, Sanya, China
Duration: 15 Dec 200718 Dec 2007


Conference2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
CityYalong Bay, Sanya


  • Labyrinth environment
  • Mobile robot
  • Neural networks
  • On-line planning
  • Path planning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Biomaterials

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