Neural network approaches to dynamic collision-free trajectory generation

@article{Yang2001NeuralNA,
  title={Neural network approaches to dynamic collision-free trajectory generation},
  author={Simon X. Yang and Max Q.-H. Meng},
  journal={IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
  year={2001},
  volume={31 3},
  pages={302-18}
}
In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons… CONTINUE READING
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