Among the multitude of scenarios in which autonomous robots are intended to operate, natural environments present the most significant challenges in the context of traversability estimation compared to structured settings. To address these complexities without compromising urban performance, we propose TE-NeXt (Traversability Estimation Convolutional Network), a customized 3D sparse convolutional architecture tailored for unstructured terrain. This network is a customized and efficient architecture for traversability estimation from sparse LiDAR (Light Detection and Ranging) point clouds based on a encoder–decoder topology that includes several modifications regarding: (i) the input features; (ii) the structure of encoder–decoder resolution levels; and (iii) the constitution of the 3D (three-dimensional) convolutional block. Thus, the experimental results demonstrate superior performance in unstructured terrain (82% F1 score on Rellis-3D), high robustness in urban environments (SemanticKITTI), and strong generalization capabilities in mixed environments (SemanticUSL). Finally, we present a fully autonomous navigation framework utilizing this method and release the source code to ensure reproducibility.

Photovoltaic (PV) energy plays a key role in addressing the growing global energy demand. Organic solar cells (OSCs) represent a promising alternative to silicon-based PVs due to their low cost, lightweight, and sustainable production. Despite achieving power conversion efficiencies (PCEs) over 20 %, OSCs still face challenges in stability and efficiency. Recent advances in manufacturing, artificial intelligence and machine learning (ML) achieve optimized and screened OSCs for greater sustainability and commercial viability, thus potentially reducing costs while ensuring stable and long term performance. This work presents optimal ML models to represent the temporal degradation on the PCE of polymeric OSCs with structure ITO/PEDOT:PSS/P3HT:PCBM/Al. First, we generated a database with 166 entries with measurements of 5 OSCs, and up to 7 variables regarding the manufacturing and environmental conditions for more than 180 days. Then, we relied on a software framework that provides a conglomeration of automated ML protocols that execute sequentially against our database by simply command-line interface. This easily permits hyper-optimizing the ML models through exhaustive benchmarking so that optimal models are obtained. The accuracy for predicting PCE over time reaches values of the coefficient determination widely exceeding 0.90, whereas the root mean squared error, sum of squared error, and mean absolute error are significantly low. Additionally, we assessed the predictive ability of the models using an unseen OSC as an external set. For comparative purposes, classical Bayesian regression fitting are also presented, which only perform sufficiently for univariate cases of single OSCs.

The location of a robot is a key aspect in the field of mobile robotics. This problem is particularly complex when the initial pose of the robot is unknown. In order to find a solution, it is necessary to perform a global localization. In this paper, we propose a method that addresses this problem using a coarse‐to‐fine solution. The coarse localization relies on a probabilistic approach of the Monte Carlo localization (MCL) method, with the contribution of a robust deep learning model, the MinkUNeXt neural network, to produce a robust description of point clouds of a 3D LiDAR within the observation model. The MCL method has been approached from a topological perspective, considering that the particles are initialized on the map positions where LiDAR scans have been previously captured. For fine localization, global point cloud registration has been implemented. MinkUNeXt aids this by exploiting the outputs of its intermediate layers to produce deep local features for each point in a scan. These features facilitate precise alignment between the current sensor observation (query) and one of the point clouds on the map. The proposed MCL method incorporating deep local features for fine localization is termed MCL‐DLF. Alternatively, a classical ICP method has been implemented for this precise localization aiming at comparison purposes. This method is termed as MCL‐ICP. In order to validate the performance of the MCL‐DLF method, it has been tested on publicly available datasets such as the NCLT dataset, which provides seasonal large‐scale environments. In addition, tests have been also performed with our own data (UMH) that also include seasonal variations on large indoor/outdoor scenarios. The results, which were compared with established state‐of‐the‐art methodologies, demonstrate that the MCL‐DLF method obtains an accurate estimate of the robot localization in dynamic environments despite changes in environmental conditions. For reproducibility purposes, the code is publicly available.

An optimal solution to the localization problem is essential for developing autonomous robotic systems. Apart from autonomous vehicles, precision agriculture is one of the fields that can benefit most from these systems. Although LiDAR Place Recognition (LPR) is a widely used technique in recent years to achieve accurate localization, it is mostly used in urban settings. However, the lack of distinctive features and the unstructured nature of agricultural environments make place recognition challenging. This work presents a comprehensive review of state-of-the-art deep learning applications for agricultural environments and LPR techniques. We focus on the challenges that arise in these environments. We analyze the existing approaches, datasets, and metrics used to evaluate LPR system performance and discuss the limitations and future directions of research in this field. This is the first survey that focuses on LiDAR-based localization in agricultural settings, with the aim of providing a thorough understanding and fostering further research in this specialized domain.

Abstract Full spherical views provide advantages in many applications that use visual information. Dual back-to-back fisheye cameras are receiving much attention to obtain this type of view. However, obtaining a high-quality full spherical view is very challenging. In this paper, we propose a correction step that models the relation between the pixels of the pair of fisheye images in polar coordinates. This correction is implemented during the mapping from the unit sphere to the fisheye image using the equidistant fisheye projection. The objective is that the projections of the same point in the pair of images have the same position on the unit sphere after the correction. In this way, they will also have the same position on the equirectangular coordinate system. Consequently, the discontinuity between the spherical views for blending is minimized. Throughout the manuscript, we show that the angular polar coordinates of the same scene point in the fisheye images are related by a sine function and the radial distance coordinates by a linear function. Also, we propose employing a polynomial as a geometric transformation between the pair of spherical views during the image alignment since the relationship between the matching points of pairs of spherical views is not linear, especially in the top/bottom regions. Quantitative evaluations demonstrate that using the correction step improves the quality of the full spherical view, i.e. IQ MS-SSIM, up to 7%. Similarly, using a polynomial improves the IQ MS-SSIM up to 6.29% with respect to using an affine matrix.

Redundant manipulators offer several advantages, including improved manipulability, singularity avoidance, and obstacle evasion. However, kinematic redundancy also introduces additional challenges, such as the need to solve an underdetermined inverse kinematic problem to control the manipulator. This paper introduces a novel approach for motion planning of redundant manipulators, based on the exploration of feasibility maps. The proposed method is an extension of the RRT algorithm, modified to explore the redundant space in order to find a suboptimal feasible path in the joint space, sacrificing optimality for scalability to higher degrees of redundancy. The method is able to follow a given task trajectory while considering other constraints, such as joint limits, self-collisions, and obstacles.

Feature matching is a key technique for a wide variety of computer vision and image processing applications such as visual localization. It permits finding correspondences of significant points within the environment that eventually determine the localization of a mobile agent. In this context, this work evaluates an Adaptive Probability-Oriented Feature Matching (APOFM) method that dynamically models the visual knowledge of the environment in terms of the probability of existence of features. Several improvements are proposed to achieve a more robust matching in a visual odometry framework: a study on the classification of the matching candidates, enhanced by a nearest neighbour search policy; a dynamic weighted matching that exploits the probability of feature existence in order to tune the matching thresholds; and an automatic false positive detector. Additionally, a comparison of performance is carried out, considering a publicly available dataset composed of two kinds of wide field-of-view images: catadioptric and fisheye. Overall, the results validate the appropriateness of these contributions, which outperform other well-recognized implementations within this framework, such as the standard visual odometry, a visual odometry method based on RANSAC, as well as the basic APOFM. The analysis shows that fisheye images provide more visual information of the scene, with more feature candidates. Contrarily, omnidirectional images produce fewer feature candidates, but with higher ratios of feature acceptance. Finally, it is concluded that improved precision is obtained when the location problem is solved by this method.

Nowadays education programs in engineering degrees have evolved towards advanced learning models and methodologies, which are either partially or entirely sustained by ICT (Information, Communication, and Technology) resources, and blended approaches. In this sense, electronics courses have become of paramount importance in most education plans within engineering degrees at university. Therefore the adaption to such novel methodologies is increasingly demanded. According to this, we propose an improved teaching program concentrated on the use of an online simulation tool, amongst other digital resources. The program is addressed to students in first levels of engineering degrees, within the framework of the Spanish public university system. In particular, the methodology has been devised through the use of an online circuit simulation applet in Java, which does not require any software installation. The main purpose is to enhance the general achievement of the students, particularizing on their practical competences, digital skills, engagement and motivation towards the learning of electronics, sustained by digital resources such as simulation. A population of 258 students enrolled during the academic year 2017/2018 has been established as a sample for presenting achievement results, surveys data and comparison statistics with other digital resources. Additionally, test groups of roughly 50% out of the total population of students have been established in order to confirm the success of the approach, in contrast to the former teaching methodology. As a result, the approach proves to be an active model which allows the students to develop long-term and autonomous skills in electronics and simulation.

This work presents a purely visual topologic odometry system for robot navigation. Our system is based on a Multi-Scale analysis that allows us to estimate the relative displacement between consecutive omnidirectional images. This analysis uses global appearance techniques to describe the scenes. The visual odometry system also makes use of global appearance descriptors of panoramic images to estimate the phase lag between consecutive images and to detect loop closures. When a previous mapped area is recognized during the navigation, the system re-estimates the pose of the scenes included in the map, reducing the error of the path. The algorithm is validated using our own database captured in an indoor environment under real dynamic conditions. The results demonstrate that our system permits estimating the path followed by the robot with accuracy comparing to the real route.