用深度学习直接预测最优路径，大幅降低导航规划计算时间。
Fast End-to-End Generation of Belief Space Paths for Minimum Sensing Navigation
- 用U-Net从问题描述直接生成路径候选
- 相比采样基线算法，计算时间显著减少
- 适合需要实时路径规划的机器人系统
我们重新审视高斯信念空间中的运动规划问题。由于现有基于采样的规划器因高维特性导致计算成本高昂，本文提出一种方法，利用深度学习模型直接从问题描述中预测最优路径候选。该方法包含三个步骤：首先，构建大规模输入输出对数据集，输入图像编码问题（如起始状态、目标状态和障碍物位置），输出图像编码解决方案（即最短路径的真值）；任意现有规划器均可用于生成该数据集。其次，采用U-Net架构学习输入与输出之间的依赖关系。最后，将训练好的U-Net模型应用于新问题，通过解析输出图像（表示路径分布）重构最优路径候选。实验表明，所提方法相比基于采样的基线算法显著降低计算时间。
原文摘要 · Abstract (English)
We revisit the problem of motion planning in the Gaussian belief space. Motivated by the fact that most existing sampling-based planners suffer from high computational costs due to the high-dimensional nature of the problem, we propose an approach that leverages a deep learning model to predict optimal path candidates directly from the problem description. Our proposed approach consists of three steps. First, we prepare a training dataset comprising a large number of input-output pairs: the input image encodes the problem to be solved (e.g., start states, goal states, and obstacle locations), whereas the output image encodes the solution (i.e., the ground truth of the shortest path). Any existing planner can be used to generate this training dataset. Next, we leverage the U-Net architecture to learn the dependencies between the input and output data. Finally, a trained U-Net model is applied to a new problem encoded as an input image. From the U-Net's output image, which is interpreted as a distribution of paths,an optimal path candidate is reconstructed. The proposed method significantly reduces computation time compared to the sampling-based baseline algorithm.
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