Simultaneous Deployment and Tracking Multi-Robot Strategies with Connectivity Maintenance

Multi robot teams composed by ground and aerial vehicles have gained attention during the last years. We present a scenario where both types of robots must monitor the same area from different view points. In this paper we propose two Lloyd-based tracking strategies to allow the ground robots (agents) follow the aerial ones (targets), keeping the connectivity between the agents. The first strategy establishes density functions on the environment so that the targets acquire more importance than other zones, while the second one iteratively modifies the virtual limits of the working area depending on the positions of the targets. We consider the connectivity maintenance due to the fact that coverage tasks tend to spread the agents as much as possible, which is addressed by restricting their motions so that they keep the links of a Minimum Spanning Tree of the communication graph. We provide a thorough parametric study of the performance of the proposed strategies under several simulated scenarios. In addition, the methods are implemented and tested using realistic robotic simulation environments and real experiments

LightDepth: Single-View Depth Self-Supervision from Illumination Decline

Single-view depth estimation can be remarkably effective if there is enough ground-truth depth data for supervised training. However, there are scenarios, especially in medicine in the case of endoscopies, where such data cannot be obtained. In such cases, multi-view self-supervision and synthetic-to-real transfer serve as alternative approaches, however, with a considerable performance reduction in comparison to supervised case. Instead, we propose a single-view self-supervised method that achieves a performance similar to the supervised case. In some medical devices, such as endoscopes, the camera and light sources are co-located at a small distance from the target surfaces. Thus, we can exploit that, for any given albedo and surface orientation, pixel brightness is inversely proportional to the square of the distance to the surface, providing a strong single-view self-supervisory signal. In our experiments, our self-supervised models deliver accuracies comparable to those of fully supervised ones, while being applicable without depth ground-truth data

Estrategias Multi-Robot de Despliegue y Cobertura con Mantenimiento de la Conectividad

Los sistemas multi-robot son un foco de investigación en la sociedad actual debido a las numerosas ventajas que presentan. Son sistemas robustos, escalables al tamaño del problema y que permiten una especialización de los individuos. El principal tema que se aborda en este trabajo es la cobertura de una zona determinada por parte de un sistema multi-robot. Se trata de desarrollar un algoritmo de seguimiento de objetivos móviles partiendo de un algoritmo de cobertura existente. El algoritmo del que se parte realiza las labores de cobertura mediante divisiones de Voronoi iterativas, y mantiene la conectividad entre sus agentes por el método del Minimum Spanning Tree. A la hora de extender sus capacidades al seguimiento de objetivos móviles, se plantean dos alternativas. Una consiste en aplicar funciones de importancia con centro en los objetivos para que el cálculo ponderado de las divisiones de Voronoi acerque a la flota hacia su meta. La otra, por su parte, consiste en modificar los límites de la zona de trabajo en función de la posición de los objetivos y de los propios agentes del sistema. Una comparación entre las dos alternativas permite concluir que el primer método es más rápido y más adecuado para casos en los que hay objetivos sueltos, mientras que el segundo es más adecuado para casos en los que hay concentraciones de objetivos. También se realiza una implementación del sistema sobre el simulador Gazebo. Los robots se controlan mediante ROS y el algoritmo se ejecuta desde MATLAB. Además se desarrolla un sistema de visualización en Gazebo que permite comprender con mayor claridad el movimiento de cada uno de los robots del conjunto. Finalmente, se realizan varios experimentos sobre esta implementación en Gazebo para comprobar el comportamiento del algoritmo. También se realiza un estudio paramétrico acerca de un experimento de seguimiento de una formación. En él se varían factores como la velocidad de la formación, el radio de los sensores de los robots o el número de agentes. Se destaca la importancia de realizar estudios como éste con carácter previo a una implementación real, dado que puede evitar derrochar recursos en robots y dispositivos de visión

Laparoscopic surgery in 3D improves results and surgeon convenience in sleeve gastrectomy for morbid obesity

Purpose Advanced laparoscopic procedures are still challenging. One critical issue is the lack of stereoscopic vision. The aim of this surgical study is to evaluate whether 3D vision offers any advantages for surgical performance over 2D vision during sleeve gastrectomy for morbid obesity using a laparoscopic system that allows changing between 2D and 3D optics. Methods A total of 78 patients were analyzed, with 37 in the 2D group and 41 in the 3D group. Performance time, hospital stay, complications, and early outcomes were collected. To assess the quality of the 2D and 3D techniques, visual analog scales from 0 to 10 were designed, and image quality, depth of field, precision in performing tasks, and general ergonomics were measured. Results According to the vision system used, the mean duration of surgery was 85 ± 16.8 min for patients operated on with the 2D system and 69 ± 16.9 min for those operated on with the 3D system. There were no significant differences between the overall percentages of complications according to the type of vision used. However, postoperative complications were more severe in the 2D laparoscopy group. The average length of stay was shorter for patients in the 3D group. Regarding the differences perceived by the surgeon, the depth of field and the precision of tasks were better in the 3D vision group. Conclusion The 3D system provided greater depth perception and precision in more complex tasks, enabling safer surgery. This led to a reduction in the operative time and hospital stay. Moreover, the severity of complications was less

EndoMapper dataset of complete calibrated endoscopy procedures

Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on automatic detection of polyps or other pathologies, but localization and navigation of the endoscope is completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures are needed. This data will be used to build a 3D mapping and localization systems that can perform special task like, for example, detect blind zones during exploration, provide automatic polyp measurements, guide doctors to a polyp found in a previous exploration and retrieve previous images of the same area aligning them for easy comparison. These systems will provide an improvement in the quality and precision of the procedures while lowering the burden on the physicians. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, including slow and careful screening explorations, making secondary use of medical data. Its original purpose is to facilitate the development and evaluation of VSLAM (Visual Simultaneous Localization and Mapping) methods in real endoscopy data. The first release of the dataset is composed of 59 sequences with more than 15 hours of video. It is also the first endoscopic dataset that includes both the computed geometric and photometric endoscope calibration with the original calibration videos. Meta-data and annotations associated to the dataset varies from anatomical landmark and description of the procedure labeling, tools segmentation masks, COLMAP 3D reconstructions, simulated sequences with groundtruth and meta-data related to special cases, such as sequences from the same patient. This information will improve the research in endoscopic VSLAM, as well as other research lines, and create new research lines.Comment: 11 pages, 7 figures, 4 table

Predictive study of pharmacological reversal for residual neuromuscular blockade and postoperative pulmonary complications: a prospective, observational, cohort study

In recent years, some studies have generated controversy since they conclude that intraoperatively pharmacological reversal of neuromuscular blockade does not contribute to the reduction of postoperative residual neuromuscular blockade or pulmonary complications. Therefore, the main objective of this study was to assess the incidence of residual neuromuscular blockade and postoperative pulmonary complications according to spontaneous or pharmacological neuromuscular reversal. The secondary aim was to present a prognostic model to predict the probability of having postoperative residual neuromuscular blockade depending on a patient's comorbidities and intraoperative neuromuscular blocking agents management. A single-center, prospective, observational cohort study including patients undergoing surgical procedures with general anesthesia was designed. A total of 714 patients were analyzed. Patients were divided into four groups: cisatracurium with spontaneous reversal, cisatracurium with neostigmine antagonism, rocuronium with spontaneous reversal, and rocuronium with sugammadex antagonism. According to our binomial generalized linear model, none of the studied comorbidities was a predisposing factor for an increase in the residual neuromuscular blockade. However, in our study, pharmacological reversal of rocuronium with sugammadex and, particularly, neuromuscular monitoring during surgery were the factors that most effectively reduced the risk of residual neuromuscular blockade as well as early and late postoperative pulmonary complications