Abstract
COVID pandemic has changed the way in which we carry out our daily life and also have affected educational processes. Teaching and learning have changed from a most common face to face context to a blended or online context. This implies changes in the way to carry out the activates and have an impact in research projects such as RoboSTEAM. Such project, that applies Challenge Based Learning methodologies with application of Robotics and Mechatronics, requires to change its approach to show how it is possible to succeed in the new situation. This paper describes how the project has evolved, how it has been affected by COVID and the possible changes to carry out. Regarding this last issue remote labs and online tools are presented as solutions to support changes in the application of challenge-based learning methodology. © 2020 ACM.

Abstract
After the computational thinking sessions in the previous 2016-2019 editions of TEEM Conference, the fifth edition of this track has been organized in the current 2020 edition. Computational thinking is still a very significant topic, especially, but not only, in pre-university education. In this edition, the robotic has a special role in the track, with a strength relationship with the STEM and STEAM education of children at the pre-university levels, seeding the future of our society. © 2020 ACM.

Abstract
This work presents multiple small robots in an unhealthy industrial environment responsible for detecting harmful gases to humans, avoiding possible harmful effects on the body. Mixed reality is widely used, considering that the environment and gases are virtual and real small robots. Essential components for the experiments are virtual, such as gases and BioCyber-Sensors. The results establish the great potential for applications in several areas, such as industrial, biomedical, and services. The entire system was developed based on ROS (Robot Operating System), thus the ease in diversifying different applications and approaches with multiple agents. The main objective of small robots is to guaranty a healthy work environment.

Abstract
Thin-film sensors surfaces are becoming popular to collect data in several specific and complex processes, namely plastic injection or metal stamping, allowing the digitization of such processes through the use of Internet of Things technologies. A particular challenge in such thin-film sensors surfaces is the data acquisition and signal conditioning system, which implementation is complex due to the characteristics of these sensors (e.g., low amplitude and noisy signals), but even more complex when implemented in real industrial processes, which are subject to harsh conditions, namely noise, dirt and aggressive elements. This work describes a modular data acquisition and signals conditioning system for thin-film sensors surfaces, meeting the requirements of scalability, robustness and low-cost, meaning that it can be easily expanded according to the number of sensors required for the application scenario.

Abstract
Autonomous inspection Unmanned Aerial Vehicle systems are an essential research area, including power line distribution inspection. Considerable efforts to solve the demanding presented in the autonomous UAV inspection process are present in technical and scientific research. One of these challenges is the precise positioning and fly control of the UAV around the energy structures, which is vital to assure the security of the operation. The most common techniques to achieve precise positioning in UAV fly are Global Positioning Systems with Real-Time Kinematic. This technique demands a proper satellite signal receiving to work appropriately, sometimes hard to achieve. The present work proposes a complementary position data system based on augmented reality tags (AR Tags) to increase the reliability of the UAV fly positioning system. The system application is proposed for energy power tower inspections as an example of use. The adaptation to other inspection tasks is possible whit some small changes. Experimental results have shown that an increase in the position accuracy is accomplished with the use of this schema.

Abstract
The collaboration between humans and machines, where humans can share the same work environment without safety equipment due to the collision avoidance characteristic is one of the research topics for the Industry 4.0. This work proposes a system that acquires the space of the environment through an RGB-Depth sensor, verifies the free spaces in the created Point Cloud and executes the trajectory of the collaborative manipulator avoiding collisions. It is demonstrated a simulated environment before the system in real situations, in which the movements of pick-and-place tasks are defined, diverting from virtual obstacles with the RGB-Depth sensor. It is possible to apply this system in real situations with obstacles and humans, due to the results obtained in the simulation. The basic structure of the system is supported by the ROS software, in particular, the MoveIt! and Rviz. These tools serve both for simulations and for real applications. The obtained results allow to validate the system using the algorithms PRM and RRT, chosen for being commonly used in the field of robot path planning.