This paper presents a robotic platform, PiBot, which was developed to improve the teaching of robotics with vision to secondary students. Its computational core is the Raspberry Pi 3 controller board, and the greatest novelty of this prototype is the support developed for the powerful camera mounted on board, the PiCamera. An open software infrastructure written in Python language was implemented so that the student may use this camera as the main sensor of the robotic platform. Furthermore, higher-level commands were provided to enhance the learning outcome for beginners. In addition, a PiBot 3D printable model and the counterpart for the Gazebo simulator were also developed and fully supported. They are publicly available so that students and schools without the physical robot or that cannot afford to obtain one, can nevertheless practice, learn and teach Robotics using these open platforms: DIY-PiBot and/or simulated-PiBot.

In western countries, robotics is becoming increasingly common in primary and secondary education, both as a specific discipline and a tool to make science, technology, engineering, and mathematics (STEM) subjects more appealing to children. The impact of robotics on society is also growing yearly, with new robotics applications in such things as autonomous cars, vacuum cleaners, and the area of logistics. In addition, the labor market is constantly demanding more professionals with robotics skills. This paper presents the PyBoKids framework for teaching robotics in secondary school, where its aim is to improve pre-university robotics education. It is based on the Python programming language and robots using an Arduino microprocessor. It includes a software infrastructure and a collection of practical exercises directed at pre-university students. The software infrastructure provides support for real and simulated robots. Moreover, we describe a pilot teaching project based on this framework, which was used by more than 2000 real students over the last two years.

Cameras are one of the most relevant sensors in autonomous robots. However, two of their challenges are to extract useful information from captured images, and to manage the small field of view of regular cameras. This paper proposes implementing a dynamic visual memory to store the information gathered from a moving camera on board a robot, followed by an attention system to choose where to look with this mobile camera, and a visual localization algorithm that incorporates this visual memory. The visual memory is a collection of relevant task-oriented objects and 3D segments, and its scope is wider than the current camera field of view. The attention module takes into account the need to reobserve objects in the visual memory and the need to explore new areas. The visual memory is useful also in localization tasks, as it provides more information about robot surroundings than the current instantaneous image. This visual system is intended as underlying technology for service robot applications in real people's homes. Several experiments have been carried out, both with simulated and real Pioneer and Nao robots, to validate the system and each of its components in office scenarios.

This paper presents the robotic platform, PiBot, that has been developed and that is aimed at improving the teaching of Robotics with vision to secondary students. Its computational core is the Raspberry Pi 3 controller board, and the greatest novelty of this prototype is the support developed for the powerful camera mounted on board, the PiCamera. An open software infrastructure written in Python language was implemented so that the student may use this camera, or even a WebCam, as the main sensor of this robotic platform. Also, higher level commands have been provided to enhance the learning outcome for beginners. In addition, a PiBot 3D printable model and the counterpart for the Gazebo simulator were also developed and fully supported. They are publicly available so that students and educational centers that do not have the physical robot or can not afford the costs of these, can nevertheless practice and learn or teach Robotics using these open platforms: DIY-PiBot and/or simulated-PiBot.

This article presents a full course for autonomous aerial robotics inside the RoboticsAcademy framework. This “drone programming” course is open-access and ready-to-use for any teacher/student to teach/learn drone programming with it for free. The students may program diverse drones on their computers without a physical presence in this course. Unmanned aerial vehicles (UAV) applications are essentially practical, as their intelligence resides in the software part. Therefore, the proposed course emphasizes drone programming through practical learning. It comprises a collection of exercises resembling drone applications in real life, such as following a road, visual landing, and people search and rescue, including their corresponding background theory. The course has been successfully taught for five years to students from several university engineering degrees. Some exercises from the course have also been validated in three aerial robotics competitions, including an international one. RoboticsAcademy is also briefly presented in the paper. It is an open framework for distance robotics learning in engineering degrees. It has been designed as a practical complement to the typical online videos of massive open online courses (MOOCs). Its educational contents are built upon robot operating system (ROS) middleware (de facto standard in robot programming), the powerful 3D Gazebo simulator, and the widely used Python programming language. Additionally, RoboticsAcademy is a suitable tool for gamified learning and online robotics competitions, as it includes several competitive exercises and automatic assessment tools.