Path Planning using a One-shot-sampling Skeleton Map

Abstract

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.