Path planning algorithms fundamentally aim to compute collision-free paths, with many works focusing on finding the optimal distance path. However, for several applications, a more suitable approach is to balance response time, path safety, and path length. In this context, a skeleton map is a useful tool in graph-based schemes, as it provides an intrinsic representation of the free workspace. However, standard skeletonization algorithms are computationally expensive, as they are primarly oriented towards image processing tasks. We propose an efficient path-planning methodology that finds safe paths within an acceptable processing time. This methodology leverages a Deep Denoising Autoencoder (DDAE) based on the U-Net architecture to compute a skeletonized version of the navigation map, which we refer to as SkelUnet. The SkelUnet network facilitates exploration of the entire workspace through one-shot sampling (OSS), as opposed to the iterative or probabilistic sampling used by previous algorithms. SkelUnet is trained and tested on a dataset consisting of 12,500 two-dimensional dungeon maps. The motion planning methodology is evaluated in a simulation environment with an Unmanned Aerial Vehicle (UAV) in 250 previously unseen maps and assessed using several navigation metrics to quantify the navigability of the computed paths. The results demonstrate that using SkelUnet to construct the roadmap offers significant advantages, such as connecting all regions of free workspace, providing safer paths, and reducing processing time.

翻译：路径规划算法的根本目标在于计算无碰撞路径，许多研究聚焦于寻找最优距离路径。然而，对于若干应用场景而言，更适宜的方法是在响应时间、路径安全性与路径长度之间取得平衡。在此背景下，骨架图成为基于图式方案中的有效工具，因其提供了自由工作空间的内在表征。然而，标准骨架化算法计算成本高昂，这主要源于其最初面向图像处理任务的设计导向。本文提出一种高效的路径规划方法，能够在可接受的处理时间内找到安全路径。该方法基于U-Net架构的深度去噪自编码器（DDAE）计算导航地图的骨架化版本，我们将其称为SkelUnet。SkelUnet网络通过单次采样（OSS）实现对整个工作空间的探索，这与先前算法采用的迭代或概率采样方式形成对比。SkelUnet在包含12,500张二维地下城地图的数据集上进行训练与测试。该运动规划方法在模拟环境中通过无人机（UAV）在250张未见地图中进行评估，并采用多项导航指标对计算路径的通行性进行量化分析。实验结果表明，利用SkelUnet构建路线图具有显著优势：能够连通自由工作空间的所有区域、提供更安全的路径并有效减少处理时间。