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- Acquisition and reconstruction of 3D objects for robotic machiningPublication . Zorawski, Matheus; Lima, José; Silva, Manuel Fernando Santos; Cunha, Márcio Rodrigues daWith 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.
- Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring systemPublication . Brito, Thadeu; Azevedo, Beatriz Flamia; Mendes, João; Zorawski, Matheus; Fernandes, Florbela P.; Pereira, Ana I.; Rufino, José; Lima, José; Costa, Paulo Gomes daDeveloping 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.
- An IoT approach for animals trackingPublication . Zorawski, Matheus; Brito, Thadeu; Castro, José; Castro, João Paulo; Castro, Marina; Lima, JoséPastoral 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.
- Optimizing data transmission in a wireless sensor network based on LoRaWAN protocolPublication . Brito, Thadeu; Zorawski, Matheus; Mendes, João; Azevedo, Beatriz Flamia; Pereira, Ana I.; Lima, José; Costa, Paulo Gomes daInternet 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.
- Map coverage of LoRaWAN signal’s employing GPS from mobile devicesPublication . Brito, Thadeu; Mendes, João; Zorawski, Matheus; Azevedo, Beatriz Flamia; Khalifeh, Ala'; Fernandes, Florbela P.; Pereira, Ana I.; Lima, José; Costa, Paulo Gomes daForests 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.
- Industrial robotic arm in machining process aimed to 3D objects reconstructionPublication . Zorawski, Matheus; Brito, Thadeu; Lima, José; Silva, Manuel F.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.
- A YOLO-Based Insect Detection: Potential Use of Small Multirotor Unmanned Aerial Vehicles (UAVs) MonitoringPublication . Berger, Guido; Mendes, João; Chellal, Arezki Abderrahim; Junior, Luciano Bonzatto; Silva, Yago M.R.; Zorawski, Matheus; Pereira, Ana I.; Pinto, Milena F.; Castro, João Paulo; Valente, António; Lima, JoséThis paper presents an approach to address the challenges of manual inspection using multirotor Unmanned Aerial Vehicles (UAV) to detect olive tree flies (Bactrocera oleae). The study employs computer vision techniques based on the You Only Look Once (YOLO) algorithm to detect insects trapped in yellow chromotropic traps. Therefore, this research evaluates the performance of the YOLOv7 algorithmin detecting and quantify olive tree flies using images obtained from two different digital cameras in a controlled environment at different distances and angles. The findings could potentially contribute to the automation of insect pest inspection by UAV-based robotic systems and highlight potential avenues for future advances in this field. In view of the experiments conducted indoors, it was found that the Arducam IMX477 camera acquires images with greater clarity compared to the TelloCam, making it possible to correctly highlight the set of Bactrocera oleae in different prediction models. The presented results in this research demonstrate that with the introduction of data augmentation and auto label techniques on the set of images of Bactrocera oleae, it was possible to arrive at a prediction model whose average detection was 256 Bactrocera oleae in relation to the corresponding ground truth value to 270 Bactrocera oleae.