Improved biological neural network approach for path planning of differential drive agricultural robots with arbitrary shape

Abstract

To improve the path planning ability of a differential drive agricultural robot with arbitrary shape, a novel path planning method based on improved biological neural dynamics approach is proposed. Taking a T-shaped robot as an example, its shape features at different orientations are described through kernel matrix rotation. The coordinates and orientation of the robot are used as neuron position parameters to construct a 3-D neural network, and the neuron state is determined by convolutional calculation. Neural connections are defined based on the translational and rotational motion properties of the robot. By subdividing the workspace network, more flexible rotation options for the robot are obtained. Then, biological neural dynamics is established to determine a stable neural activity landscape, and a collision-free path can be obtained by consecutively selecting the adjacent neuron with maximum activity. The two key factors to improve the path planning ability are the flexible rotation options of the robot in a grid workspace and the consideration of the interaction between the robot body and the surrounding obstacles. It is proved that the proposed approach can find a feasible path within the range of rational numbers. Finally, its strong path planning ability is demonstrated by several experiments in narrow and complex environments.