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Telepresence and telemanipulation robotics are suitable solutions to relieve humans from direct health risks and repetitive or unhealthy work. Through demographic changes in western countries and the COVID-19 pandemic, this relief is also considered for healthcare workers, especially caregivers, as the demands for them rises. The requirements are intuitively usable telerobotic and telepresence systems for remote assistance, to cut the high physical strain in manual patient transfers and the reduction of contact with infected patients. To ensure this, key technologies like 3D imaging and perception systems are essential. In this work, we present a novel, lightweight telepresence and telemanipulation system, specialized for caregiving. It allows an operator, wearing a virtual reality headset, to immerse into a sensor system captured scene on a distant location in real-time, with low latency of 250 ms and up to 30 fps refresh rate. Extensive measurement shows that 97.1% of the relevant point cloud data is below 1 cm error and 99.5 % is below 1.6 cm, making the system suitable for the application.

This paper presents an overview of a smart city 4.0 framework in accelerating digital transformation, especially during the COVID-19 pandemic, using Jakarta as a case study. The findings of this study provide new insights how to translate a vision into a reality in the form of smart city 4.0 framework that offers a significant opportunity to advance the understanding of building a smart city ecosystem with technologies, innovations and collaborations. This paper applies four principles of the framework, namely mobile-first, system-and-data driven, digital experience, and smart collaboration in building a smart city 4.0 ecosystem platform. Part of the aim of this paper is to examine Jakarta's super-app called JAKI that is compatible with such principles as a use case in the time of the pandemic. It provides a better understanding of common elements in building a new concept of a smart city. The results will inspire and give a contribution to other cities to consider the framework in building a smart city 4.0 ecosystem platform to foster quality of life, economic growth and sustainability.

The advancements in Computer Vision with Deep Learning are playing an essential role in aiding Healthcare Organizations to offer better patient care while reducing costs and improving efficiencies. The medical industry has seen a sharp growth since the past few decades and it has benefited thousands of living beings. Skin being the largest part of human beings, it has never been given the proper care nor importance, hence widely resulting in the ignorance of skin infections or cancers. Despite the diagnosis of several such diseases being detected at the later or critical stages, there is a little to no scope of detection where the affected people can identify the symptoms immediately and could reach a physician. In this paper, we present the development of a model using deep learning approach to identify the specific dermatitis and a mobile application that functions in a way to detect the disease when a picture of the symptomatic part is taken and uploaded. This scheme assists the medical professionals and patients during pandamic situations like COVID 19. This application is user friendly and very much useful for the people living in rural areas and hilly area where it takes long time and expenditure to reach hospitals.

Due to the personal and material restrictions caused by the COVID-19 pandemic, the teaching of robotics has been greatly affected in recent months due to its practical nature. This work aims to explore possibilities for the continuation of this teaching remotely. A simple teaching methodology will be proposed, listed and compared simulators softwares on the most diverse platforms, and the possibility of using remote laboratories will be analyzed. The idea is to present a set of solutions, so that teachers can analyze and select the possibilities that best identify themselves and that best suit the specifics of their class.

The use of simulation and visualization tools to validate mechanical designs and programming for robots has become increasingly important. Being used in many scenarios, simulators have collaborated for research in machine learning, deep learning and training of professionals. Nowadays, with the Covid-19 pandemic and social distancing, simulators have great potential as a tool to help in the teaching of robotics. However, research indicates that the user may encounter differences between what happens in the simulator and in real life, this effect is called reality gap. This article aims to investigate the effects of this gap in an educational robotics simulator, named Virtual Robotics Toolkit (VRT), as well as its impacts on the testing of algorithms and to propose strategies to reduce the impact of this effect. Three batch tests were carried out on two different robots, both in the simulation and in the real world. By capturing data from sensors such as tachometer, infrared, gyroscope and a light sensor, it was possible to compare the performance in the two scenarios and their differences. Finally, the entire test field and models were made available along with videos of some tests.

There is a noticeable lag between Brazilian Teams and Robocup Division A teams in Small Size Soccer League (SSL). One of the main reasons is the difference in the mechanical project and how effective the dribbler and kicker systems conduct and kick the ball to other robots or the goal. This paper describes the process to develop both dribbler and kicker systems to an SSL robot, beginning with a review of Robocup Division A teams and the main Brazilian teams' projects. A 3D modeling of the dribbler and kicker systems is presented considering innovation effectiveness and space. The systems and models were evaluated via finite element simulation tests. The manufacturing process and its challenges are not in the scope of this paper due to Covid-19 limitations to lab access and will be part of future works. All parts designed are available for public use, allowing other teams to use them, modified or not, on their projects.

In this paper a visual system equipped with state of the art in Deep Learning is presented which could be employed in the robotics platforms for the pandemic situation where human – human contact needs to be limited in order to perform various detection and anomaly analysis tasks. The developed detection and anomaly analysis system deals with human and environmental hazards and disasters especially for pandemic prevention. The system could detect whether the person of interest is wearing mask or not, social distancing is followed, person or environment are in normal condition for example if a window is open to keep ventilation in a closed environment. This research is a part of our project to develop a specific robot for pandemics to use advanced in artificial intelligence to make systems which could keep us safe and healthy.

We developed a 3D-Enhanced Facility Management System for Indoors Navigation (3D-EFMS-IN) to assist visually impaired users (VIU). Additionally, the system aims to facilitate the management of estate property and provide support for future scenarios related to emergencies, security, and robotics devices. The system combines four main subsystems: Mapping, Navigation Paths, Indoor Localisation and Navigation, and a Visualisation. An Integration of the subsystems has been done and a pretest with one VIU was performed to obtain feedback and tune the critical characteristics of our development. We observed that the system offers an acceptable preliminary user experience for VIU and future tests require to improve the latency of the system and usability. Shortly, we aim to obtain qualitative and quantitative measurements in a significant pool of users once the COVID lockdown ends.

The world population currently counts more of 617 million people over 65 years old. COVID-19 has exposed this population group to new restrictions, leading to new difficulties in care and assistance by family members. New technologies can reduce the degree of isolation of these people, helping them in the execution of healthy activities such as performing periodic sports routines. NAO robots find in this a possible application; being able to alternate voice commands and execution of movements, they can guide elderly people in performing gymnastic exercises. Additional encouragement could come through demonstrations of the exercises and verbal interactions using the voice of loved ones (for example, grandchildren). These are transmitted in real time to the NAO which streams the video of older people exercising, bringing the two parties involved closer together. This proposal, realized with the robot NAO V6, allows to have a help at home ready to motivate, teach the exercises and train the elderly living alone at home.