  Sampling-based path planning algorithms suffer from heavy reliance on uniform
sampling, which accounts for unreliable and time-consuming performance,
especially in complex environments. Recently, neural-network-driven methods
predict regions as sampling domains to realize a non-uniform sampling and
reduce calculation time. However, the accuracy of region prediction hinders
further improvement. We propose a sampling-based algorithm, abbreviated to
Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the
optimal path based on a high-accuracy region prediction. First, we implement a
region prediction neural network (RPNN), to predict accurate regions for the
RPNN-RRT*. A full-layer channel-wise attention module is employed to enhance
the feature fusion in the concatenation between the encoder and decoder.
Moreover, a three-level hierarchy loss is designed to learn the pixel-wise,
map-wise, and patch-wise features. A dataset, named Complex Environment Motion
Planning, is established to test the performance in complex environments.
Ablation studies and test results show that a high accuracy of 89.13% is
achieved by the RPNN for region prediction, compared with other region
prediction models. In addition, the RPNN-RRT* performs in different complex
scenarios, demonstrating significant and reliable superiority in terms of the
calculation time, sampling efficiency, and success rate for optimal path
planning.
