O. Octavio Gutierrez-Frias

Gabriel O. Flores-Aquino, Jheison Duvier Díaz Ortega, Ricardo Yahir Almazan Arvizu et al.

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

Gabriel O. Flores-Aquino, J. Irving Vasquez-Gomez, O. Octavio Gutierrez-Frias

Sampling-based motion planning algorithms are widely used in robotics because they are very effective in high-dimensional spaces. However, the success rate 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 distribution. For robots with large configuration spaces or dynamic restrictions, selecting these parameters is a challenging task. This paper proposes a method for improving the performance to 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 uniform probability density function (U-PDF) with a custom distribution (C-PDF) learned from previously successful queries in similar tasks. With a few samples, our method builds a custom distribution that allows the RRT to grow to promising states that will lead to a solution. We tested our method in several autonomous driving tasks such as parking maneuvers, obstacle clearance and under narrow passages scenarios. The results show that the proposed method outperforms the original RRT and several improved versions in terms of success rate, tree density and computation time. In addition, the proposed method requires a relatively small set of examples, unlike current deep learning techniques that require a vast amount of examples.