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.

翻译：基于采样的路径规划算法严重依赖均匀采样，在复杂环境中存在可靠性差、耗时长的缺陷。近年来，基于神经网络的方法通过预测区域作为采样域实现非均匀采样并减少计算时间，但区域预测的准确性制约了性能的进一步提升。本文提出一种基于采样的算法——区域预测神经网络RRT*（RPNN-RRT*），通过高精度区域预测快速获取最优路径。首先，我们构建区域预测神经网络（RPNN）为RPNN-RRT*预测精确区域。在编码器与解码器的拼接过程中引入全层通道注意力模块增强特征融合，并设计三级层次损失函数学习像素级、图像级与区域块级特征。为测试复杂环境下的性能，建立了名为复杂环境运动规划的数据集。消融实验与测试结果表明，与其他区域预测模型相比，RPNN的区域预测准确率达到89.13%。此外，RPNN-RRT*在不同复杂场景中均表现出显著且稳定的优势，在计算时间、采样效率与最优路径规划成功率方面均有卓越表现。