A Neural Network Approach to Real-Time Trajectory Generation *

摘要

A neural network approach is proposed for real-time trajectory generation with collision free in an environment with varying obstacles and moving target. This biologically inspired neural network is topologically organised. The dynamics of each neuron is characterised by a shunting equation or an additive equation. Each neuron has only local connections, and the optimal trajectories are generated without any explicitly optimising cost functions and without learning. Therefore the model is computationally eficient. The stability of the network is analytically proved using a Lyapunou function candidate. As examples, the proposed neural network is applied to trajectory formation for a mobile robot in solving maze-type problems, dynamically trucking moving target, and avoiding varying obstacle. The eficiency of the proposed approach is demonstrated through simulation and comparison studies.