Loading...
36 results
Search Results
Now showing 1 - 10 of 36
- Angle assessment for upper limb rehabilitation: a novel light detection and ranging (LiDAR)-based approachPublication . Klein, Luan C.; Chellal, Arezki Abderrahim; Grilo, Vinicius F.S.B.; Braun, João; Gonçalves, José; Pacheco, Maria F.; Fernandes, Florbela P.; Monteiro, Fernando C.; Lima, JoséThe accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.
- Three-dimensional scanning system based on a low-cost infrared sensorPublication . Braun, João; Lima, José; Pereira, Ana I.; Rocha, Cláudia; Costa, Paulo Gomes daNowadays, with the availability of 3D printers, the scanners for objects are becoming increasingly present since they allow to replicate objects by 3D printing, especially for small scale sizes. However, the majority of these technologies are expensive, due to the complexity of this task. Therefore, this work presents a prototype of a low-cost 3D scanning system for small objects using a point cloud to stereolithography approach where it was already validated in simulation in previous work. This concept has a restriction that the objects must have a uniform shape, i.e, without discontinuities. The architecture is composed of two stepper motors, due to their precision, a rotating plate to allow 360 degrees scans and another rotating structure that allows the infrared distance sensor to scan the object from bottom to top (90 degrees). The prototype was validated in the real scenario with good results. © 2021 IEEE.
- A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competitionPublication . Klein, Luan C.; Braun, João; Mendes, João; Pinto, Vítor H.; Martins, Felipe N.; Oliveira, Andre Schneider; Oliveira, Andre Schneider; Wörtche, Heinrich; Costa, Paulo Gomes da; Lima, JoséLocalization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.
- Using a mobile robot for hazardous substances detection in a factory environmentPublication . Braun, João; Lima, José; Costa, José Cerqueira Gomes da; Nakano, Alberto YoshihiroIndustries that work with toxic materials need extensive security protocols to avoid accidents. Instead of having fixed sensors, the concept of assembling the sensors on a mobile robot that performs the scanning through a defined path is cheaper, configurable and adaptable. This work describes a mobile robot, equipped with several gas sensors and LIDAR, that follows a trajectory based on waypoints, simulating a working Autonomous Guided Vehicle (AGV). At the same time, the robot keeps measuring for toxic gases. In other words, the robot follows the trajectory while the gas concentration is under a defined value. Otherwise, it starts the autonomous leakage search based on a search algorithm that allows to find the leakage position avoiding obstacles in real time. The proposed methodology is verified in simulation based on a model of the real robot. Therefore, three path plannings were developed and their performance compared. A Light Detection And Ranging (LIDAR) device was integrated with the path planning to propose an obstacle avoidance system with a dilation technique to enlarge the obstacles, thus, considering the robot’s dimensions. Moreover, if needed, the robot can be remotely operated with visual feedback. In addition, a controller was made for the robot. Gas sensors were embedded in the robot with Finite Impulse Response (FIR) filter to process the data. A low cost AGV was developed to compete in Festival Nacional de Robótica (Portuguese Robotics Open) 2019 - Gondomar, describing the robot’s control and software solution to the competition.
- A robot localization proposal for the RobotAtFactory 4.0: A novel robotics competition within the Industry 4.0 conceptPublication . Braun, João; Oliveira Júnior, Alexandre de; Berger, Guido; Pinto, Vítor H.; Soares, Inês N.; Pereira, Ana I.; Lima, José; Costa, Paulo Gomes daRobotic competitions are an excellent way to promote innovative solutions for the current industries’ challenges and entrepreneurial spirit, acquire technical and transversal skills through active teaching, and promote this area to the public. In other words, since robotics is a multidisciplinary field, its competitions address several knowledge topics, especially in the STEM (Science, Technology, Engineering, and Mathematics) category, that are shared among the students and researchers, driving further technology and science. A new competition encompassed in the Portuguese Robotics Open was created according to the Industry 4.0 concept in the production chain. In this competition, RobotAtFactory 4.0, a shop floor, is used to mimic a fully automated industrial logistics warehouse and the challenges it brings. Autonomous Mobile Robots (AMRs) must be used to operate without supervision and perform the tasks that the warehouse requests. There are different types of boxes which dictate their partial and definitive destinations. In this reasoning, AMRs should identify each and transport them to their destinations. This paper describes an approach to the indoor localization system for the competition based on the Extended Kalman Filter (EKF) and ArUco markers. Different innovation methods for the obtained observations were tested and compared in the EKF. A real robot was designed and assembled to act as a test bed for the localization system’s validation. Thus, the approach was validated in the real scenario using a factory floor with the official specifications provided by the competition organization.
- Kabsch marker estimation algorithm - a multi-robot marker-based localization algorithm within the industry 4.0 contextPublication . Braun, João; Lima, José; Pereira, Ana I.; Costa, Paulo Gomes daThis paper introduces the Kabsch Marker Estimation Algorithm (KMEA), a new, robust multi-marker localization method designed for Autonomous Mobile Robots (AMRs) within Industry 4.0 (I4.0) settings. By integrating the Kabsch Algorithm, our approach significantly enhances localization robustness by aligning detected fiducial markers with their known positions. Unlike conventional methods that rely on a limited subset of visible markers, the KMEA uses all available markers, without requiring the camera's extrinsic parameters, thereby improving robustness. The algorithm was validated in an I4.0 automated warehouse mockup, with a four-stage methodology compared to a previously established marker estimation algorithm for reference. On the one hand, the results have demonstrated the KMEA's similar performance in standard controlled scenarios, with millimetric precision across a set of error metrics and a mean relative error (MRE) of less than 1%. On the other hand, KMEA, when faced with challenging test scenarios with outliers, showed significantly superior performance compared to the baseline algorithm, where it maintained a millimetric to centimetric scale in error metrics, whereas the other suffered extreme degradation. This was emphasized by the average reduced results of error metrics from 86.9% to 92% in Parts III and IV of the test methodology, respectively. These results were achieved using low-cost hardware, indicating the possibility of even greater accuracy with advanced equipment. The paper details the algorithm's development, theoretical framework, comparative advantages over existing methods, discusses the test results, and concludes with comments regarding its potential for industrial and commercial applications by its scalability and reliability.
- Using machine learning approaches to localization in an embedded system on RobotAtFactory 4.0 competition: a case studyPublication . Klein, Luan C.; Braun, João; Martins, Felipe N.; Wörtche, Heinrich; Oliveira, Andre Schneider; Mendes, João; Pinto, Vítor H.; Costa, Paulo Gomes daThe use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
- Smart systems for monitoring buildings - an IoT applicationPublication . Kalbermatter, Rebeca B.; Brito, Thadeu; Braun, João; Pereira, Ana I.; Ferreira, Ângela P.; Valente, António; Lima, JoséLife in society has initiated a search for comfort and security in social centers. This search generated revolutions within the knowledge about the technologies involved, making the environments automated and integrated. Along with this increase, ecological concerns have also arisen, which have been involved since the design of intelligent buildings, remaining through the years of their use. Based on these two pillars, the present study aims to monitor three central systems inside the apartments of the Apolo Building (Bragan¸cacity, Portugal). The electrical energy consumption, water flow, and waste disposal systems are integrated through a single database. The data is sent remotely via WiFi through the microcontroller. For better visualization and analytics of the data, a web application is also developed, which allows for real-time monitoring. The obtained results demonstrate to the consumer his behavior regarding household expenses. The idea of showing the consumer their expenditure is to create an ecological awareness. Through the data collected and the environmental alternatives found, it is possible to observe whether there was a behavior change when receiving this data, either in the short or long term.
- RobotAtFactory 4.0: a ROS framework for the SimTwo simulatorPublication . Braun, João; Oliveira Júnior, Alexandre de; Berger, Guido; Lima, José; Pereira, Ana; Costa, Paulo Gomes daRobotics competitions encourage the development of solutions to new challenges that emerge in sync with the rise of Industry 4.0. In this context, robotic simulators are employed to facilitate the development of these solutions by disseminating knowledge in robotics, Education 4.0, and STEM. The RobotAtFactory 4.0 competition arises to promote improvements in industrial challenges related to autonomous robots. The official organization provides the simulation scene of the competition through the open-source SimTwo simulator. This paper aims to integrate the SiwTwo simulator with the Robot Operating System (ROS) middleware by developing a framework. This integration facilitates the design of robotic systems since ROS has a vast repository of packages that address common problems in robotics. Thus, competitors can use this framework to develop their solutions through ROS, allowing the simulated and real systems to be integrated.
- Data analysis of workplace accidents - a case studyPublication . Sena, Inês; Braun, João; Pereira, Ana I.The welfare and safety of the employees of an enterprise is a great concern and priority in a responsible and successful organization. The identification of patterns of work-related accidents is important to reduce and prevent further mishaps and injuries. To improve the safety of the work environment, accidents related data must be analyzed to identify the possible risk factors and their effects on the type of accident and its level of severity. Thus, data related to workplace accidents in fishmonger stores were collected from a Portuguese retail company where it was analyzed with statistical, clustering, and classification techniques to identify potential underlying correlation and patterns between the data, and in this way, collecting important information to prevent future accident or lesions.