Public entities driven robotic innovation in urban areas

Cities present new challenges and needs to satisfy and improve lifestyle for their citizens under the concept “Smart City”. In order to achieve this goal in a global manner, new technologies are required as the robotic one. But Public entities unknown the possibilities offered by this technology to get solutions to their needs. In this paper the development of the Innovative Public Procurement instruments is explained, specifically the process PDTI (Public end Users Driven Technological Innovation) as a driving force of robotic research and development and offering a list of robotic urban challenges proposed by European cities that have participated in such a process. In the next phases of the procedure, this fact will provide novel robotic solutions addressed to public demand that are an example to be followed by other Smart Cities.Peer ReviewedPostprint (author's final draft

What is missing in autonomous discovery: Open challenges for the community

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social scientists, as evidenced by the development of the Acceleration Consortium and recent Accelerate Conference. Despite its strengths, this rapidly developing field presents numerous opportunities for growth, challenges to overcome, and potential risks of which to remain aware. This community perspective builds on a discourse instantiated during the first Accelerate Conference, and looks to the future of self-driving labs with a tempered optimism. Incorporating input from academia, government, and industry, we briefly describe the current status of self-driving labs, then turn our attention to barriers, opportunities, and a vision for what is possible. Our field is delivering solutions in technology and infrastructure, artificial intelligence and knowledge generation, and education and workforce development. In the spirit of community, we intend for this work to foster discussion and drive best practices as our field grows

Systematic mapping literature review of mobile robotics competitions

This paper presents a systematic mapping literature review about the mobile robotics competitions that took place over the last few decades in order to obtain an overview of the main objectives, target public, challenges, technologies used and final application area to show how these competitions have been contributing to education. In the review we found 673 papers from 5 different databases and at the end of the process, 75 papers were classified to extract all the relevant information using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. More than 50 mobile robotics competitions were found and it was possible to analyze most of the competitions in detail in order to answer the research questions, finding the main goals, target public, challenges, technologies and application area, mainly in education.info:eu-repo/semantics/publishedVersio

Systematic Mapping Literature Review of Mobile Robotics Competitions

[EN] This paper presents a systematic mapping literature review about the mobile robotics competitions that took place over the last few decades in order to obtain an overview of the main objectives, target public, challenges, technologies used and final application area to show how these competitions have been contributing to education. In the review we found 673 papers from 5 different databases and at the end of the process, 75 papers were classified to extract all the relevant information using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. More than 50 mobile robotics competitions were found and it was possible to analyze most of the competitions in detail in order to answer the research questions, finding the main goals, target public, challenges, technologies and application area, mainly in education.S

Development of an open access system for remote operation of robotic manipulators

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáExploring the realms of research, training, and learning in the field of robotic systems poses obstacles for institutions lacking the necessary infrastructure. The significant investment required to acquire physical robotic systems often limits access and hinders progress in these areas. While robotic simulation platforms provide a virtual environment for experimentation, the potential of remote robotic environments surpasses this by enabling users to interact with real robotic systems during training and research activities. This way, users, including students and researchers, can engage in a virtual experience that transcends geographical boundaries, connecting them to real-world robotic systems though the Internet. By bridging the gap between virtual and physical worlds, remote environments offer a more practical and immersive experience, and open up new horizons for collaborative research and training. Democratizing access to these technologies means empower educational institutions and research centers to engage in practical and handson learning experiences. However, the implementation of remote robotic environments comes with its own set of technical challenges: communication, security, stability and access. In light of these challenges, a ROS-based system has been developed, providing open access with promising results (low delay and run-time visualization). This system enables remote control of robotic manipulators and has been successfully validated through the remote operation of a real UR3 manipulator.Explorar as áreas de pesquisa, treinamento e aprendizado no campo de sistemas robóticos apresenta obstáculos para instituições que não possuem a infraestrutura necessária. O investimento significativo exigido para adquirir sistemas robóticos físicos muitas vezes limita o acesso e dificulta o progresso nessas áreas. Embora as plataformas de simulação robótica forneçam um ambiente virtual para experimentação, o potencial dos ambientes robóticos remotos vai além disso, permitindo que os usuários interajam com sistemas robóticos reais durante atividades de treinamento e pesquisa. Dessa forma, os usuários, incluindo estudantes e pesquisadores, podem participar de uma experiência virtual que transcende as fronteiras geográficas, conectando-os a sistemas robóticos do mundo real por meio da Internet. Ao estabelecer uma ponte entre os mundos virtual e físico, os ambientes remotos oferecem uma experiência mais prática e imersiva, abrindo novos horizontes para a pesquisa colaborativa e o treinamento. Democratizar o acesso a essas tecnologias significa capacitar instituições educacionais e centros de pesquisa a se envolverem em experiências práticas e de aprendizado prático. No entanto, a implementação de ambientes robóticos remotos traz consigo um conjunto próprio de desafios técnicos: comunicação, segurança, estabilidade e acesso. Diante desses desafios, foi desenvolvida uma plataforma baseada em ROS, oferecendo acesso aberto com resultados promissores (baixo delay e visualização em run-time). Essa plataforma possibilita o controle remoto de manipuladores robóticos e foi validada com sucesso por meio da operação remota de um manipulador UR3 real

Communication between men and robots

Tato práce, která byla psána na Strojní fakultě Vysokého učení technického v Brně, Ústav automatizace a informatiky, se zabývá problémem komunikace mezi lidmi a roboty z netradičního hlediska. Snahou bylo vyhodnotit a rozdělila způsoby této komunikace, najít problémy, které se této oblasti týkají a na jejich základě analyzovat jejich možná řešení. Cílem bylo poukázat na některá reálná řešení zmíněných problémů.This work written at the Faculty of Mechanical Engineering, Brno University of Technology, Institute of Automation and Computer Science, deals with the problem of communication between men and robots from nontraditional aspects. It is tried to analyse and divide the ways of the communication, find the problems that are related to this and on their basis analyse possible solutions. It was endeavoured to show some real solutions to these mentioned problems.

Cross-domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG

In this work, we show the success of unsupervised transfer learning between Electroencephalographic (brainwave) classification and Electromyographic (muscular wave) domains with both MLP and CNN methods. To achieve this, signals are measured from both the brain and forearm muscles and EMG data is gathered from a 4-class gesture classification experiment via the Myo Armband, and a 3-class mental state EEG dataset is acquired via the Muse EEG Headband. A hyperheuristic multi-objective evolutionary search method is used to find the best network hyperparameters. We then use this optimised topology of deep neural network to classify both EMG and EEG signals, attaining results of 84.76% and 62.37% accuracy, respectively. Next, when pre-trained weights from the EMG classification model are used for initial distribution rather than random weight initialisation for EEG classification, 93.82%(+29.95) accuracy is reached. When EEG pre-trained weights are used for initial weight distribution for EMG, 85.12% (+0.36) accuracy is achieved. When the EMG network attempts to classify EEG, it outperforms the EEG network even without any training (+30.25% to 82.39% at epoch 0), and similarly the EEG network attempting to classify EMG data outperforms the EMG network (+2.38% at epoch 0). All transfer networks achieve higher pre-training abilities, curves, and asymptotes, indicating that knowledge transfer is possible between the two signal domains. In a second experiment with CNN transfer learning, the same datasets are projected as 2D images and the same learning process is carried out. In the CNN experiment, EMG to EEG transfer learning is found to be successful but not vice-versa, although EEG to EMG transfer learning did exhibit a higher starting classification accuracy. The significance of this work is due to the successful transfer of ability between models trained on two different biological signal domains, reducing the need for building more computationally complex models in future research