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Recent studies have shown that tropospheric ozone, fine particulate matter and nitrogen dioxide are the urban air pollutants of major concern regarding human health effects. Monitoring air quality is a challenge in several cities, such as Rio de Janeiro, where the number of fixed-site air quality monitoring stations and their spatial distribution are insufficient to assess the extent of atmospheric pollutants. However, despite this lack of resources, the data obtained by mobile stations are a valuable means of determining which areas are experiencing critical air quality conditions, and provide key information for an air quality management program. The main purpose of this study was to conduct a critical analysis of data obtained by the Municipal Department of Environment and Climate (SMAC) mobile station in the period 2010–2018. Concentrations determined for particulate matter with a diameter ≤2.5 μm (PM2.5), O3, NO2, SO2 and CO showed that PM2.5 and O3 are the pollutants of major concern, and that the north of the city has higher air quality indices for these compounds. In addition, the south-west district had relatively high ozone levels, probably owing to low concentrations of NO2 in a volatile organic compound (VOC)-limited ozone formation regime. These factors should be considered by the municipal government in future discussions of control strategies for managing the city’s air quality. This study also shows the value of mobile stations in making a preliminary survey of pollutant concentrations, mainly in countries with limited financial investment in air quality management.

Abstract In early childhood, air pollution leads to an increased risk of disease, premature death, and development of disruptions. Fine particulate matter (PM2.5) is considered the classical pollutant of major concern. However, in Brazil, fine particulate monitoring is still limited and restricted to a few cities. Particularly, in the city of Rio de Janeiro, only one automatic station obtains PM2.5 concentrations and publishes the air quality indexes daily. This study is part of the project “Clean air for a good start” managed by the Horizon Citizen Foundation, with the goal of obtaining evidence to set a plan to reduce children exposition to air pollution. Data is shared on a global web platform. Results showed that PM2.5 levels are high when compared with WHO guidelines and coordinated actions would be needed to decrease emissions of primary pollutants and secondary aerosol’s precursors.

Despite technological progress in instrumental diagnostic investigations of the last decade, prostate cancer remains one of the most frequent malignant tumors and the second leading cause of cancer death among men. Although prostate biopsy remains the reference among all diagnosis procedures, it still exposes patients to the risk of developing complications. In this paper, the authors present a novel robotic system for prostate cancer diagnosis aimed at improving the current diagnostic procedures and reducing their undesired effects. The purpose of this work is to validate the proposed methodology by considering experimental analysis on both phantom and ex-vivo prostate tissues.

Atmospheric processes in an urban scenario are very complex and depend on meteorological parameters, emission, transport and deposition processes and the chemical mechanism. Simple air quality models can be used to understand the main aspects of ozone formation and provide information to adopt pollution control strategies. In this work the detailed simulation of ozone concentrations using the air quality model OZIPR (Ozone Isopleth Package for Research) coupled with the SAPRC (Statewide Air Pollution Research Center) chemical model is presented. First, the methods to determine volatile organic compounds (VOCs) are described as well as the statistical treatment of experimental data: Meteorological parameters (temperature, humidity, pressure, wind speed and direction), concentrations of nitrogen oxides (NOx = NO2 + NO), carbon monoxide (CO) and ozone (O3), which are needed to characterize the problem. Then, the construction and adjust of the model (base case) is detailed using, as an example, data for a typical urban area in the city of Rio de Janeiro. Finally, the limitations and the potential of the model are discussed. Clearly the quality of experimental data, mainly NOx and COVs concentrations and the COVs speciation, are a key information to obtain a correct estimation of O3 levels.

Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.

With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications.

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments.

RoDyMan, acronym for Robotic Dynamic Manipulation, is a research project funded by the European Research Council to the CREATE Consortium and carried out at PRISMA Lab in the Department of Electrical Engineering and Information Technology of the University of Naples Federico II. The goal is the development of a service robot able to manipulate elastic and soft objects, which change continuously density and shape. These robots will be used not only in manufacturing but also as an aid to elderly or disabled people, for medical and surgical use, and in other manual activities. Preparing a pizza involves an extraordinary level of manual dexterity: for this reason a pizzaiolo robot has been conceived. It is also a tribute to Naples, at the forefront of technology, robotics and automation, but especially of culture and gastronomy, of which pizza is a symbol and tradition. Creating a robot able to manipulate objects like humans is one of the most sought and difficult challenges of robotics. It means to replicate skills that are the result of human biological and cultural evolution. This goal is arduous, mainly for two issues. First, we do not have a total knowledge of human nature, but this is the condition to be able to replicate the human functions in a machine. Second, there are many technical limits to implement a bio-inspired robot, not the least those making it user-friendly and aesthetically appreciated.

Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration.