Neural network dynamics for path planning and obstacle avoidance
Neural Networks
Motion planning for carlike robots using a probabilistic learning approach
International Journal of Robotics Research
An algorithm for planning collision-free paths among polyhedral obstacles
Communications of the ACM
Robot Path Planning Using Fluid Model
Journal of Intelligent and Robotic Systems
Fast Motion Planning by Parallel Processing – a Review
Journal of Intelligent and Robotic Systems
Parallel Scientific Computing in C++ and MPI
Parallel Scientific Computing in C++ and MPI
International Journal of Circuit Theory and Applications - CNN Technology
Virtual assembly with biologically inspired intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient dynamic system for real-time robot-path planning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive behavior navigation of a mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Dynamical neural networks for planning and low-level robot control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach
IEEE Transactions on Neural Networks
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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.