The use of robots as educational tools provides a stimulating environment for students. Some robotics competitions focus on primary and secondary school aged children, and serve as motivation for students to get involved in educational robotics activities. Although very appealing, many students cannot participate on robotics competitions because they cannot afford robotics kits. Hence, several students have no access to educational robotics, especially on developing countries. To minimize this problem and contribute to education equality, we have created RoSoS Robot Soccer Simulator, in which students program virtual robots in a similar way that they would program their real ones. In this chapter we explain some technical details of RoSoS and discuss the implementation of a new league for the robotics competitions: Junior Soccer Simulation league (JSS). Because soccer is the most popular sport in the world, we believe JSS will be a strong motivator for students to get involved with robotics.
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For almost 25 years, the goal of the RoboCup has been to build soccer robots capable of winning against the FIFA World Champion of 2050. To foster the participation of the next generation of roboticists, the RoboCupJunior competition takes place in parallel and provides a similar challenge of appropriate difficulty for high school students. RoboCupJunior has three main categories: Soccer, Rescue and OnStage. For the Soccer category, participants need to design, build and program a team of autonomous robots to play soccer against an opponent team of robots. The competition is physical in nature, since it assumes physical robots playing against one another. In 2020 and 2021, the COVID-19 pandemic has made it difficult for a competition of this type to take place, due to obvious restrictions on physical gatherings. To allow for some sort of participation, and inspired by positive experience of the larger RoboCup community, the Organizing Committee of RoboCupJunior Soccer has explored porting a portion of the challenge to a simulated environment. Many of the existing environments, however, are built for higher education/research teams competitions or research, making them complex to deploy and generally unsuitable for high school students. In this paper we present the development of SoccerSim, a simulated environment for RoboCupJunior Soccer, based on the Webots open-source robotics simulator. We also discuss how the participation of students was key for its development and present a summary of the competition rules. We further describe the case study of utilizing SoccerSim first as a testbed for a Demo competition, and later as part of RoboCup Worldwide 2021. The participation of more than 60 teams from over 20 countries suggests that SoccerSim provides an affordable alternative to physical robotics platforms, while being stable enough to support a diverse userbase. The experience of using SoccerSim at RoboCupJunior Worldwide 2021 suggests that a simulated environment significantly lowers the barrier to entry, as evidenced by the participation of many teams that have not participated before. To make it easy for similar competitions to take place in the future, we made the code of SoccerSim available as open-source, as well as the associated tooling required for using it in a tournament.
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The use of robots as educational tools provide a stimulating environment for students. Some robotics competitions focus on primary and secondary school aged children, and serve as a motivation factor for students to get involved in educational robotics activities. But, in most competitions students are required to deal with robot design, construction and programming. Although very appealing, many students cannot participate on robotics competitions because they cannot afford robotics kits and their school do not have the necessary equipment. Because of that, several students have no access to educational robotics, especially on developing countries. To minimize this problem and contribute to education equality, we present a proposal for a new league for the robotics competitions: The Junior Soccer Simulation league (JSS). In such a league, students program virtual robots in a similar way that they would program their real ones. Because there is no hardware involved, costs are very low and participants can concentrate on software development and robot's intelligence improvement. Finally, because soccer is the most popular sport in the world, we believe JSS will be a strong motivator for students to get involved with robotics. In this paper we present the simulator that was developed (ROSOS) and discuss some ideas for the adoption of a Junior Soccer Simulation competition.
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De renovatie van de Amsterdamse Van Deysselbuurt is niet alleen een ruimtelijke transformatie, maar ook een kans voor maatschappelijke vernieuwing. CoFab-MR stelt een verkennend onderzoeksproject voor, waarin bewoners samen een houten constructie ontwerpen en te bouwen met behulp van Mixed Reality (MR) en digitale ontwerpsystemen. Het project brengt Community Builder Cascoland, materiaalexperts Amsterdamse Fijnhout, het Van Eesteren Museum en onderzoeksteams van AUAS (DPRG, RobotLab en VR/SimulationLab) samen. De Van Deysselbuurt is onderdeel van het 20-jarige programma ‘Samen Nieuw-West’, een van de 20 nationale programma's gericht op kwetsbare buurten, gericht op het verbeteren van het toekomstperspectief van huidige en toekomstige bewoners op het gebied van huisvesting, werk, vaardigheden en veiligheid. Door digitale tools in te bedden in een hands-on co-creatieproces, stelt coFAB-MR bewoners zonder eerdere technische expertise in staat om deel te nemen aan het ontwerp en de montage van (niet-standaard) houten constructies voor hun eigen buurt. In dit geval een zichtbare 'marker' voor het Makers Village, een tijdelijk ankerpunt voor gemeenschapsinitiatieven. coFab-MR onderzoekt ontwerp en technologie om nieuwe vaardigheden en gemeenschapsopbouw naar de Van Deysselbuurt te brengen. CoFAB-MR onderzoekt het potentieel van MR voor inclusief ontwerp en constructie, ruimtelijk inzicht en niet-deskundige samenwerking. Het sluit aan bij de Design Power Agenda en nationale ambities voor circulaire constructie, digitale transitie en inclusief ontwerp. De resultaten omvatten een gebouwd object, een open-source MR-toolkit, video-opnames van het participatieve proces en een reflectie op het gebruik van MR voor stedelijke transities. De resultaten zullen ten goede komen aan ontwerpprofessionals, docenten, gemeenten en andere gemeenschappen die inclusieve co-creatie in de gebouwde omgeving willen integreren. Het is de bedoeling dat CoFAB-MR zal leiden tot vervolgprojecten en vaardighedenontwikkeling in de Van Deysselbuurt, naast en voorbij het 20-jarige nationale programma.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
Automation is a key enabler for the required productivity improvement in the agrifood sector. After years of GPS-steering systems in tractors, mobile robots start to enter the market. Localization is one of the core functions for these robots to operate properly on fields and in orchards. GNSS (Global Navigation Satellite System) solutions like GPS provide cm-precision performance in open sky, but buildings, poles and biomaterial may reduce system performance. On top, certain areas do not provide a dependable grid communication link for the necessary GPS corrections and geopolitics lead to jamming activities. Other means for localization are required for robust operation. VSLAM (Visual Simultaneous Localization And Mapping) is a complex software approach that imitates the way we as humans learn to find our ways in unknown environments. VSLAM technology uses camera input to detect features in the environment, position itself in that 3D environment while concurrently creating a map that is stored and compared for future encounters, allowing the robot to recognize known environments and continue building a complete, consistent map of the environment covered by its movement. The technology also allows continuous updating of the map in environments that evolve over time, which is a specific advantage for agrifood use cases with growing crops and trees. The technology is however relatively new, as required computational power only recently became available in tolerable cost range and it is not well-explored for industrialized applications in fields and orchards. Orientate investigates the merits of open-source SLAM algorithms on fields - with Pixelfarming Robotics and RapAgra - and in an orchard - with Hillbird - preceded by simulations and initial application on a HAN test vehicle driving in different terrains. The project learnings will be captured in educational material elaborating on VSLAM technology and its application potential in agrifood.
