Neural-network-driven method for optimal path planning via high-accuracy region prediction

Pub Date : 2023-11-08 DOI:10.1007/s10015-023-00915-6
Yuan Huang, Cheng-Tien Tsao, Tianyu Shen, Hee-Hyol Lee
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Abstract

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.

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通过高精度区域预测实现最优路径规划的神经网络驱动方法
基于采样的路径规划算法严重依赖均匀采样,因此性能不可靠且耗时,尤其是在复杂环境中。最近,神经网络驱动的方法预测区域作为采样域,以实现非均匀采样并减少计算时间。然而,区域预测的准确性阻碍了进一步的改进。我们提出了一种基于采样的算法,简称为区域预测神经网络 RRT* (RPNN-RRT*),在高精度区域预测的基础上快速获得最佳路径。首先,我们实现了一个区域预测神经网络(RPNN),为 RPNN-RRT* 预测准确的区域。在编码器和解码器之间的串联过程中,我们采用了全层信道关注模块来增强特征融合。此外,还设计了三级层次损失来学习像素、地图和斑块特征。建立了一个名为 "复杂环境运动规划 "的数据集,以测试其在复杂环境中的性能。消融研究和测试结果表明,与其他区域预测模型相比,RPNN 的区域预测准确率高达 89.13%。此外,RPNN-RRT*在不同的复杂场景中的表现,在最优路径规划的计算时间、采样效率和成功率方面都表现出了显著而可靠的优势。
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