Autonomous microhelicopters will soon play a major role in tasks like search and rescue, environment monitoring, security surveillance, and inspection. If they are further realized in small scale, they can also be used in narrow outdoor and indoor environments and represent only a limited risk for people. However, for such operations, navigating based only on global positioning system (GPS) information is not sufficient. Fully autonomous operation in cities or other dense environments requires microhelicopters to fly at low altitudes, where GPS signals are often shadowed, or indoors and to actively explore unknown environments while avoiding collisions and creating maps. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, and navigation, which still have to be solved. The Swarm of Micro Flying Robots (SFLY) project was a European Union-funded project with the goal of creating a swarm of vision-controlled microaerial vehicles (MAVs) capable of autonomous navigation, three-dimensional (3-D) mapping, and optimal surveillance coverage in GPS-denied environments. The SFLY MAVs do not rely on remote control, radio beacons, or motion-capture systems but can fly all by themselves using only a single onboard camera and an inertial measurement unit (IMU). This article describes the technical challenges that have been faced and the results achieved from hardware design and embedded programming to vision-based navigation and mapping, with an overview of how all the modules work and how they have been integrated into the final system. Code, data sets, and videos are publicly available to the robotics community. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3-D mapping and optimal surveillance coverage are presented.

Motivated by the recent explosion of interest around Educational Robotics (ER), this paper attempts to re-approach this area by suggesting new ways of thinking and exploring the related concepts. The contribution of the paper is fourfold. First, future readers can use this paper as a reference point for exploring the expected learning outcomes of educational robotics. From an exhaustive list of potential learning gains, we propose a set of six learning outcomes that can offer a starting point for a viable model for the design of robotic activities. Second, the paper aims to serve as a survey for the most recent ER platforms. Driven by the growing number of available robotics platforms, we have gathered the most recent ER kits. We also propose a new way to categorize the platforms, free from their manufacturers’ vague age boundaries. The proposed categories, including No Code, Basic Code, and Advanced Code, are derived from the prior knowledge and the programming skills that a student needs to use them efficiently. Third, as the number of ER competitions, and tournaments increases in parallel with ER platforms’ increase, the paper presents and analyses the most popular robotic events. Robotics competitions encourage participants to develop and showcase their skills while promoting specific learning outcomes. The paper aims to provide an overview of those structures and discuss their efficacy. Finally, the paper explores the educational aspects of the presented ER competitions and their correlation with the six proposed learning outcomes. This raises the question of which primary features compose a competition and achieve its’ pedagogical goals. This paper is the first study that correlates potential learning gains with ER competitions to the best of our knowledge.

The problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of complex and non-convex morphology is presented. In such a mission, the robots attempt to maximize the part of the terrain that is visible while keeping the distance between each point in the terrain and the closest team member as small as possible. A trade-off between these two objectives should be fulfilled given the physical constraints and limitations imposed at the particular application. As the terrain’s morphology is unknown and it can be quite complex and non-convex, standard algorithms are not applicable to the particular problem treated in this paper. To overcome this, a new approach based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated. A fundamental property of this approach is that it shares the same convergence characteristics as those of constrained gradient-descent algorithms (which require perfect knowledge of the terrain’s morphology and optimize surveillance coverage subject to the constraints the team has to satisfy). Rigorous mathematical arguments and extensive simulations establish that the proposed approach provides a scalable and efficient methodology that incorporates any particular physical constraints and limitations used to navigate the robots into an arrangement that (locally) optimizes surveillance coverage.

Educational robotics has gained a lot of attention in the past few years in K-12 education. Prior studies have shown enough shreds of evidence and highlight the benefits of educational robotics as being effective in providing impactful learning experiences. At the same time, today, the scientific subject of computer vision seems to dominate the field of robotics, leading to new and innovative ideas, solutions, and products. Several articles from the recent literature demonstrate how computer vision has also improved the general educational process. However, still, the number of articles that connect computer vision with educational robotics remains limited. This article aims to present a systematic mapping review, with three research questions, investigating the current status of educational robotics, focusing on the synergies and interdependencies with the field of computer vision. The systematic review outlines the research questions, presents the literature synthesis, and discusses findings across themes. More precisely, this study attempts to answer key questions related to the role, effectiveness and applicability of computer vision in educational robotics. After a detailed analysis, this paper focuses on a set of key articles. It analyzes the research methodology, the effectiveness and applicability of computer vision, the robot platform used, the related cost, the education level, and the educational area explored. Finally, the results observed are referred to as educational process benefits. The reviewed articles suggest that computer vision contributes to educational robotics learning outcomes enhancing the learning procedure. To the best of our knowledge, this is the first systematic approach that revises the educational robotics domain by considering computer vision as a key element.