A neural network approach to real-time collision-free navigation of 3-DOF robots in 2D

In this paper, a novel neural network approach to real-time collision-free navigation of holonomic 3-degree-of-freedom (d.o.f) robots in a nonstationary environment is proposed. The's approach is based on a biologically inspired model for dynamic trajectory generation of a point robot or a multi-joint robot manipulator. The state space of the neural network is three-dimensional (3D), where two represent the spatial position in the 2D Cartesian workspace and one represents the orientation of the robot. This model is capable of generating real-time optimal navigation path for 3-d.o.f. robots through the dynamic neural activity landscape without explicitly optimizing arty cost functions, without any learning process, and without any local collision checking procedures. Therefore it is computationally efficient. In addition, this model can deal with real-time navigation with sudden environmental changes, navigation of a robot with multiple targets, anal navigation of multiple robots. The stability of the neural network is guaranteed by Lyapunov stability analysis. The effectiveness and efficiency are demonstrated through simulation studies.