Optimal robot path planning with cellular neural network

Yongmin Zhongm, Bijan Shirinzadeh, Xiaobu Yuan

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)


This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

Original languageEnglish
Pages (from-to)20-39
Number of pages20
JournalInternational Journal of Intelligent Mechatronics and Robotics
Issue number1
Publication statusPublished - Jan 2011


  • Analogy systems
  • Cellular neural network
  • Collision avoidance
  • Dynamic environments
  • Robot path planning

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