Real-time tour construction for a mobile robot in a dynamic environment
Robotics and Autonomous Systems
Neural-network-based path planning for a multirobot system with moving obstacles
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
Neural networks for mobile robot navigation: a survey
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Optimal Robot Path Planning with Cellular Neural Network
International Journal of Intelligent Mechatronics and Robotics
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A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.