Neural network approaches to dynamic collision-free trajectorygeneration
Yang, S.X.; Meng, M.
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Volume 31, Issue 3, Jun 2001 Page(s):302 - 318
Digital Object Identifier 10.1109/3477.931512
Summary: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. The real-time optimal trajectory is generated through the
dynamic activity landscape of the neural network without explicitly
searching over the free space nor the collision paths, without
explicitly optimizing any global cost functions, without any prior
knowledge of the dynamic environment, and without any learning
procedures. Therefore the model algorithm is computationally efficient.
The stability of the neural network system is guaranteed by the
existence of a Lyapunov function candidate. In addition, this model is
not very sensitive to the model parameters. Several model variations are
presented and the differences are discussed. As examples, the proposed
models are applied to generate collision-free trajectories for a mobile
robot to solve a maze-type of problem, to avoid concave U-shaped
obstacles, to track a moving target and at the same to avoid varying
obstacles, and to generate a trajectory for a two-link planar robot with
two targets. The effectiveness and efficiency of the proposed approaches
are demonstrated through simulation and comparison studies
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