Path Planning using a One-shot-sampling Skeleton Map

Path planning algorithms aim to compute a collision-free path, and many works focus on finding the optimal distance path. However, for some applications, a more suitable approach is to balance response time, safety of the paths, and path length. In this context, a skeleton map is a useful tool in graph-based schemes, as it provides an intrinsic representation of free configuration space. However, skeletonization algorithms are very resource-intensive, being primarily 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 Auto-Encoder (DDAE) based on 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 process used by exact algorithms or the probabilistic sampling process. SkelUnet is trained and tested on a dataset consisting of 12,500 bi-dimensional dungeon maps. The motion planning methodology is evaluated in a simulation environment for an Unmanned Aerial Vehicle (UAV) using 250 previously unseen maps, and assessed with various navigation metrics to quantify the navigability of the computed paths. The results demonstrate that using SkelUnet to construct a roadmap offers significant advantages, such as connecting all regions of free workspace, providing safer paths, and reducing processing times. These characteristics make this method particularly suitable for mobile service robots in structured environments.

2D Grid Map Generation for Deep-Learning-based Navigation Approaches

In the last decade, autonomous navigation for roboticshas been leveraged by deep learning and other approachesbased on machine learning. These approaches have demon-strated significant advantages in robotics performance. Butthey have the disadvantage that they require a lot of data toinfer knowledge. In this paper, we present an algorithm forbuilding 2D maps with attributes that make them useful fortraining and testing machine-learning-based approaches.The maps are based on dungeons environments where sev-eral random rooms are built and then those rooms are con-nected. In addition, we provide a dataset with 10,000 mapsproduced by the proposed algorithm and a description withextensive information for algorithm evaluation. Such infor-mation includes validation of path existence, the best path,distances, among other attributes. We believe that thesemaps and their related information can be very useful forrobotics enthusiasts and researchers who want to test deeplearning approaches. The dataset is available athttps://github.com/gbriel21/map2D_dataSet.git

Custom Distribution for Sampling-Based Motion Planning

Sampling-based algorithms are widely used in robotics because they are very useful in high dimensional spaces. However, the rate of success and quality of the solutions are determined by an adequate selection of their parameters such as the distance between states, the local planner, and the sampling method. For robots with large configuration spaces or dynamic restrictions selecting these parameters is a challenging task. This paper proposes a method for improving the results for a set of the most popular sampling-based algorithms, the Rapidly-exploring Random Trees (RRTs) by adjusting the sampling method. The idea is to replace the sampling function, traditionally a Uniform Probability Density Function (U-PDF) with a custom distribution (C-PDF) learned from previously successful queries of a similar task. With few samples, our method builds the custom distribution allowing a higher success rate and sparser trees in randomly new queries. We test our method in several common tasks of autonomous driving such as parking maneuvers or obstacle clearance and also in complex scenarios outperforming the base original and bias RRT. In addition, the proposed method requires a relative small set of examples, unlike current deep learning techniques that require a vast amount of examples.