20 International Journal of Intelligent Mechatronics and Robotics, 1(1), 20-39, January-March 2011 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Analogy Systems, Cellular Neural Network, Collision Avoidance, Dynamic Environments, Robot Path Planning INTRODUCTION Real-time collision-free motion planning in a non-stationary environment is an important and challenging issue in many autonomous systems including robotics and intelligent systems. It provides intelligent robotic systems with an ability to plan motions and to navigate autonomously. This ability becomes critical particularly for robots which operate in dynamic environments, where unpredictable and sud- den changes may occur. Whenever the robot’s Optimal Robot Path Planning with Cellular Neural Network Yongmin Zhongm, Curtin University of Technology,Australia Bijan Shirinzadeh, Monash University, Australia Xiaobu Yuan, University of Windsor, Canada ABSTRACT 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 interac- tion 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. sensory system detects a dynamic change, its planning system has to adapt and modify its paths accordingly. Prominent examples include real world environments that involve interac- tion with people, such as museums, shops, and households. Based on the analogy between cellular neural network (CNN) and robot path plan- ning, this paper presents a new neural dynamics based methodology for optimal collision-free robot path generation in an arbitrarily vary- ing environment. The real-time collision-free robot trajectory is formulated as the dynamic CNN activity. The target activity is treated as an energy source injected into the neural DOI: 10.4018/ijimr.2011010102