Acquisition and reconstruction of 3D objects for robotic machining

With the evolution of the techniques of acquisition of Three-Dimensional (3D) image it became possible to apply these in more and more areas, as well as to be used for research and hobbyists due to the appearance of low cost 3D scanners. Among the application of 3D acquisitions is the reconstruction of objects, which allows for example to redo or remodel an existing object that is no longer on the market. Another rise tech is industrial robot, that is highly present in the industry and can perform several tasks, even machining activities, and can be applied in more than one type of operation. The purpose of this work is to acquire a 3D scene with low-cost scanners and use this acquisition to create the tool path for roughing a workpiece, using an industrial robot for this machining task. For the acquisition, the Skanect software was used, which had satisfactory results for the work, and the exported file of the acquisition was worked on the MeshLab and Meshmixer software, which were used to obtain only the interest part for the milling process. With the defined work object, it was applied in Computer Aided Manufacturing (CAM) software, Fusion 360, to generate the tool path for thinning in G-code, which was converted by the RoboDK software to robot code, and this also allowed to make simulation of the machining with the desired robot. With the simulation taking place as expected, it was implemented in practice, performing the 3D acquisition machining, thus being able to verify the machining technique used. Furthermore, with the results of acquire, generation of toolpath and machining, was possible to validate the proposed solution and reach a conclusion of possible improvements for this project.Com a evolução das técnicas de aquisição de imagem 3D tornou-se possível aplicá-las em cada vez mais áreas, bem como serem utilizadas por pesquisadores e amadores devido ao surgimento de scanners 3D de baixo custo. Entre as aplicações de aquisições 3D está a reconstrução de objetos, o que permite, por exemplo, refazer ou remodelar um objeto existente que não está mais no mercado. Outra tecnologia em ascensão é o robô industrial, que está muito presente na indústria e pode realizar diversas tarefas, até mesmo atividades de fabrico, e ser aplicado em mais de um tipo de operação. O objetivo deste trabalho é adquirir uma cena 3D com scanners de baixo custo e utilizar esta aquisição para criar o caminho da ferramenta para o desbaste de uma peça, utilizando um robô industrial nesta tarefa de usinagem. Para a aquisição foi utilizado o software Skanect, que obteve resultados satisfatórios para o trabalho, e o arquivo exportado da aquisição foi trabalhado nos softwares MeshLab e Meshmixer, os quais foram utilizados para obter apenas a parte de interesse para o processo de fresagem. Com o objeto de trabalho defino, este foi aplicado em software CAM, Fusion 360, para gerar o caminho de ferramentas para o desbaste em G-code, o qual foi convertido pelo Software RoboDK para código de rôbo, e este também permitiu fazer simulação da maquinação com o rôbo pretendido. Com a simulação ocorrendo de acordo com o esperado, esta foi implementada em prática, realizando a maquinação da aquisição 3D, assim podendo verificar a técnica de maquinação utilizada. Além disso com os resultados de aquisição, geração de toolpath e maquinação, foi possível validar a solução proposta e chegar a uma conclusão de possíveis melhorias para este projeto

Industrial robotic arm in machining process aimed to 3D objects reconstruction

Industrial robots are a technology which is highly present in industry and can perform several tasks, namely machining activities. Different than CNC machines, which work with G-code and have available several software applications to generate the machine code, there is a lack of software for robotic arms, in addition to each application depending on its own language and software. This work studied a way to use different robotic arms for 3D part machining processes, to perform 3D objects reconstruction obtained through a low-cost 3D scanner. Dealing with the 3D reconstruction by integrating 3D acquisition and robotic milling with software available on the market, this paper presents a system that acquires and reconstructs a 3D object, in order to seek greater flexibility with lower initial investments and checking the applicability of robot arm in these tasks. For this, a 3D object is scanned and imported to a CAD/CAM software, to generate the machining toolpath, and a software application is used to convert the G-code into robot code. Several experiments were performed, using an ABB IRB 2600 robot arm, and the results of the machining process allowed to validate the G-code conversion and milling process using robotic arms, according to the proposed methodology. © 2021 IEEE.This work has been supported by FCT – Fundac¸ao para a ˜ Ciencia e Tecnologia within the Projects UIDB/50014/2020 ˆ and UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

Módulo de aprendizaje para diseñar placas de circuito impreso para dispositivos basados en el IoT

[ES] El taller sobre Tecnologías Disruptivas de la Información y la Comunicación para la Innovación y la Transformación Digital, organizado en el ámbito del proyecto DISRUPTIVE (disruptive.usal.es) y celebrado el 12 de septiembre de 2022 en Valladolid, tiene como objetivo debatir sobre los problemas, retos y beneficios del uso de tecnologías digitales disruptivas, a saber, Internet de las Cosas, Big data, computación en la nube, sistemas multiagente, aprendizaje automático, realidad virtual y aumentada y robótica colaborativa, para apoyar la transformación digital en curso en la sociedad. El programa del taller incluyó 6 papers técnicos aceptados, 2 charlas de invitados y una sesión de networking. Este volumen contiene 6 de las ponencias presentadas en el taller sobre Tecnologías Disruptivas de la Información y la Comunicación para la Innovación y la Transformación Digital. Este taller fue organizado por ICE (Instituto para la Competitividad Empresarial de Castilla y León), UVa (Universidad de Valladolid) y apoyado principalmente por el Fondo Europeo de Desarrollo Regional (FEDER) a través del Programa Interreg España-Portugal V-A (POCTEP) bajo la subvención 0677_DISRUPTIVE_2_E (Dinamización de los Digital Innovation Hubs dentro de la región PocTep para el impulso de las TIC disruptivas y de última generación a través de la cooperación en la región transfronteriza).[EN] The workshop on Disruptive Information and Communication Technologies for Innovation and Digital transformation, organized under the scope of the DISRUPTIVE project (disruptive.usal.es) and held on September 12, 2022 in Valladolid, aims to discuss problems, challenges and benefits of using disruptive digital technologies, namely Internet of Things, Big data, cloud computing, multi-agent systems, machine learning, virtual and augmented reality, and collaborative robotics, to support the on-going digital transformation in society. The main topics included: Intelligent Manufacturing Systems; Industry 4.0 and digital transformation; Internet of Things; Cyber-security; Collaborative and intelligent robotics; Multi-Agent Systems; Industrial Cyber-Physical Systems; Virtualization and digital twins; Predictive maintenance; Virtual and augmented reality, Big Data and advanced data analytics; Edge and cloud Computing; Digital Transformation. The workshop program included 6 accepted technical papers, 2 invited talks and a networking session. This volume contains 6 of the papers presented at the Workshop on Disruptive Information and Communication Technologies for Innovation and Digital Transformation. This workshop was organized by ICE (Institute for Business Competitiveness of Castilla y León), UVa (University of Valladolid) and mainly supported by the European Regional Development Fund (ERDF) through the Interreg Spain-Portugal V-A Program (POCTEP) under grant 0677_DISRUPTIVE_2_E (Intensifying the activity of Digital Innovation Hubs within the PocTep region to boost the development of disruptive and last generation ICTs through cross-border cooperation)

Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.This work has been supported by SAFe Project through PROMOVE—Fundação La Caixa. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for finan cial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020, and Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021.info:eu-repo/semantics/publishedVersio

Map coverage of LoRaWAN signal’s employing GPS from mobile devices

Forests are remote areas with uneven terrain, so it is costly to map the range of signals that enable the implementation of systems based on wireless and long-distance communication. Even so, the interest in Internet of Things (IoT) functionalities for forest monitoring systems has increasingly attracted the attention of several researchers. This work demonstrates the development of a platform that uses the GPS technology of mobile devices to map the signals of a LoRaWAN Gateway. Therefore, the proposed system is based on concatenating two messages to optimize the LoRaWAN transmission using the Global Position System (GPS) data from a mobile device. With the proposed approach, it is possible to guarantee the data transmission when finding the ideal places to fix nodes regarding the coverage of LoRaWAN because the Gateway bandwidth will not be fulfilled. The tests indicate that different changes in the relief and large bodies drastically affect the signal provided by the Gateway. This work demonstrates that mapping the Gateway’s signal is essential to attach modules in the forest, agriculture zones, or even smart cities.This work has been supported by Fundação La Caixa and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/5757/2020. Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020. Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021.info:eu-repo/semantics/publishedVersio

Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.This work has been supported by SAFe Project through PROMOVE—Fundação La Caixa. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020, and Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021info:eu-repo/semantics/publishedVersio

An IoT approach for animals tracking

Pastoral activities bring several benefits to the ecosystem and rural communities. These activities are already carried out daily with goats, cows and sheep in Portugal. Still, they could be better applied to take advantage of their benefits. Most of these pastoral ecosystem services are not remunerated, indicating a lack of making these activities more attractive to bring returns to shepherds, breeders and landowners. The monitoring of these activities provides data to value these services, besides being able to indicate directly to the shepherds’ routes to drive their flocks and the respective return. There are devices in the market that perform this monitoring, but they are not adaptable to the circumstances and challenges required in the Northeast of Portugal. This work addresses a system to perform animals tracking, and the development of a test platform, through long-range technologies for transmission using LoRaWAN architecture. The results demonstrated the use of LoRaWAN in tracking services, allowing to conclude about the viability of the proposed methodology and the direction for future works.info:eu-repo/semantics/publishedVersio

Optimizing data transmission in a wireless sensor network based on LoRaWAN protocol

Internet of Things, IoT, is a promising methodology that has been increasing over the last years. It can be used to allow the connection and exchange data with other devices and systems over the Internet. One of the IoT connection protocols is the LoRaWAN, which has several advantages but has a low bandwidth and limited data transfer. There is a necessity of optimising the data transfer between devices. Some sensors have a 10 or 12 bits resolution, while LoRaWAN owns 8 bits or multiples slots of transmission remaining unused bits. This paper addresses a communication optimisation for wireless sensors resorting to encoding and decoding procedures. This approach is applied and validated on the real scenario of a wildfire detection system.This work has been supported by Fundação La Caixa and FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/5757/2020.info:eu-repo/semantics/publishedVersio