ABSTRACT
The 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.
Subject(s)
Exercise Therapy , Physical Therapists , Humans , Exercise , Technology , Upper ExtremityABSTRACT
Localization 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.
ABSTRACT
Robotic 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.
ABSTRACT
EN 14103:2003 and EN 14103:2011 were developed in order to determine fatty acid methyl ester (FAME) content of biodiesel. The internal standards (IS) of biodiesel include methyl heptadecanoate (MHD) and methyl nonadecanoate (MND), respectively. However, since these ISs are also present in bovine tallow methyl esters (BTME) or overlapping peaks, they have not been efficient. This work proposes an improved BTME determination method by using hexadecyl propanoate (HDP) as an IS. For this purpose, an analytical methodology by Gas Chromatography-Flame Ionization Detector (GC-FID) was developed and validated, where HDP demonstrated selectivity in retention time between peaks C16:1 and C18:0 for coconut and soybeans methyl esters and BTME, as well as resolution >1.5 for the BTME in split mode 30:1. Trueness in the determination of BTME content using the HDP as an IS was statistically equivalent to confidence interval of 95% for the null hypothesis statistic test, even when only 20% of the HDP was utilized in comparison with the IS concentrations defined by EN 14103:2003 and EN 14103:2011. This allowed the biodiesel analysis to be performed five times more with 1â¯g of HDP. Furthermore, the method developed enabled us to reduce the analysis time by 21.6%, without prejudice to the integration of peaks (C6:0 to C24:1). Regarding the repeatability and intermediate precision tests, results of RSD (%)â¯≤â¯2% were reached. Additionally, the method developed has proved to be robust. HDP is a long-chain fatty alcohol ester absent from feedstocks used in biodiesel synthesis. It presents all of the characteristics for a good IS, ideal for application via internal standardization method, as recommended by EN 14103.
