Optimal robot path planning with cellular neural network

Yongmin Zhong, Bijan Shirinzadeh, Xiaobu Yuan

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review


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 realtime changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

Original languageEnglish
Title of host publicationAdvanced Engineering and Computational Methodologies for Intelligent Mechatronics and Robotics
EditorsShahin Sirouspour
Place of PublicationHershey PA USA
PublisherIGI Global
Number of pages20
ISBN (Electronic)9781466636354
ISBN (Print)9781466636347
Publication statusPublished - 31 Mar 2013

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