New Strategy to Approach the Inverse Kinematics Model for Manipulators with Rotational Joints

The chapter describes a new strategy to approach the solution of the inverse kinematics problem for robot manipulators. A method to determine a polynomial model approximation for the joints positions is described by applying the divided differences with a new point of view for lineal path in the end-effector of the robot manipulator. Results of the mathematical approach are analysed by obtaining the kinematics inverse model and the approximate model for lineal trajectories of a manipulator for three degrees of freedom. Finally, future research approaches are commented

Kinematics modeling and simulation of an autonomous omni-directional mobile robot

Although robotics has progressed to the extent that it has become relatively accessible with low-cost projects, there is still a need to create models that accurately represent the physical behavior of a robot. Creating a completely virtual platform allows us to test behavior algorithms such as those implemented using artificial intelligence, and additionally, it enables us to find potential problems in the physical design of the robot. The present work describes a methodology for the construction of a kinematic model and a simulation of the autonomous robot, specifically of an omni-directional wheeled robot. This paper presents the kinematic model development and its implementation using several tools. The result is a model that follows the kinematics of a triangular omni-directional mobile wheeled robot, which is then tested by using a 3D model imported from 3D Studio® and Matlab® for the simulation. The environment used for the experiment is very close to the real environment and reflects the kinematic characteristics of the robot.Aunque la robótica ha avanzado hasta el punto de ser relativamente accesible con proyectos de bajo costo, aún cabe la necesidad de crear modelos que representen fielmente el comportamiento físico de un robot creando una plataforma completamente virtual. Esto nos permite, por un lado, probar algoritmos de comportamiento o de inteligencia artificial y, por otro lado, encontrar probables problemas en el diseño físico. El presente trabajo propone el modelado cinemático y la simulación de un robot móvil autónomo, particularmente de un robot omni-direccional con ruedas. Se plantea la metodología para la construcción del modelo cinemático así como su implementación utilizando diversas herramientas. El resultado es un modelo que representa a un robot móvil omni-direccional triangular con ruedas, que fue probado utilizando un modelo tridimensional importado de 3D Studio®, y utilizando Matlab® para la simulación del modelo. También se implementa el mismo modelo utilizando una herramienta dedicada a la simulación de robots, que ofrece un ambiente muy completo de simulación. El ambiente de simulación ofrece un comportamiento muy cercano al real y refleja las características cinemáticas del robot

Kinematics modeling and simulation of an autonomous omni-directional mobile robot

<p class="Abstractandkeywordscontent"><span lang="EN-US">Although robotics has progressed to the extent that it has become relatively accessible with low-cost projects, there is still a need to create models that accurately represent the physical behavior of a robot. Creating a completely virtual platform allows us to test behavior algorithms such as those implemented using artificial intelligence, and additionally, it enables us to find potential problems in the physical design of the robot. The present work describes a methodology for the construction of a kinematic model and a simulation of the autonomous robot, specifically of an omni-directional wheeled robot. This paper presents the kinematic model development and its implementation using several tools. The result is a model that follows the kinematics of a triangular omni-directional mobile wheeled robot, which is then tested by using a 3D model imported from 3D Studio</span><span lang="EN-US">®</span><span lang="EN-US"> and Matlab</span><span lang="EN-US">® for the simulation. The environment used for the experiment is very close to the real environment and reflects the kinematic characteristics of the robot.</span></p

Mechatronics methodology: 15 years of experience Metodología mecatrónica: 15 años de experiencia

This article presents a methodology to teach students to develop mechatronic projects. It was taught in higher education schools, in different universities in Mexico, in courses such as: Robotics, Control Systems, Mechatronic Systems, Artificial Intelligence, etc. The intention of this methodology is not only to achieve the integration of different subjects but also to accomplish synergy between them so that the final result may be the best possible in quality, time and robustness. Since its introduction into the educational area, this methodology was evaluated and modified for approximately five years, were substantial characteristics were adopted. For the next ten years, only minor alterations were carried out. Fifteen years of experience have proven that the methodology is useful not only for training but also for real projects. In this article, we first explain the methodology and its main characteristics, as well as a brief history of its teaching in different educational programs. Then, we present two cases were the methodology was successfully applied. The first project consisted in the design, construction and evaluation of a mobile robotic manipulator which aims to be used as an explosives ordnance device. In the second case, we document the results of a project assignment for robotics tasks carried out by students which were formerly taught with the methodology.En este artículo se presenta una metodología para enseñar a los estudiantes a desarrollar proyectos mecatrónicos. Se implementó en las escuelas de educación superior, en diferentes universidades, en México en cursos tales como: Robótica, Sistemas de Control, Sistemas mecatrónicos, Inteligencia Artificial, etc. La intención de esta metodología no es solo lograr la integración de las diferentes asignaturas, sino también realizar una sinergia entre ellas para así obtener un mejor resultado en términos de calidad, tiempo y ro-bustez. Desde su introducción en el ámbito educativo, esta metodología ha sido evaluada y modificada por aproximadamente cinco años, adoptando características sustanciales. Durante los siguientes diez años, sólo se realizaron pequeñas alteraciones. Quince años de experiencia han demostrado que la metodología es útil no sólo para el ámbito académico sino también para la realización de proyectos reales. En este artículo daremos a conocer, en primer lugar, la metodología y sus principales características, así como una breve historia de su enseñanza en los diferentes programas educativos. Luego, presentamos dos casos donde la metodología se aplicó con éxito. El primer proyecto consistió en el diseño, construcción y evaluación de un manipulador robótico móvil que preten-de ser utilizado como un dispositivo para desactivar explosivos. En el segundo caso, documentamos los resultados de un proyecto para la asignación de tareas robóticas llevadas a cabo por los estudiantes

Semantic Feature Extraction Using SBERT for Dementia Detection

Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elderly. This condition gradually impairs the patient&rsquo;s cognition, eventually leading to the inability to perform everyday tasks without assistance. Since dementia is an incurable disease, early detection plays an important role in delaying its progression. Because of this, tools and methods have been developed to help accurately diagnose patients in their early stages. State-of-the-art methods have shown that the use of syntactic-type linguistic features provides a sensitive and noninvasive tool for detecting dementia in its early stages. However, these methods lack relevant semantic information. In this work, we propose a novel methodology, based on the semantic features approach, by using sentence embeddings computed by Siamese BERT networks (SBERT), along with support vector machine (SVM), K-nearest neighbors (KNN), random forest, and an artificial neural network (ANN) as classifiers. Our methodology extracted 17 features that provide demographic, lexical, syntactic, and semantic information from 550 oral production samples of elderly controls and people with Alzheimer&rsquo;s disease, provided by the DementiaBank Pitt Corpus database. To quantify the relevance of the extracted features for the dementia classification task, we calculated the mutual information score, which demonstrates a dependence between our features and the MMSE score. The experimental classification performance metrics, such as the accuracy, precision, recall, and F1 score (77, 80, 80, and 80%, respectively), validate that our methodology performs better than syntax-based methods and the BERT approach when only the linguistic features are used

Dual U-Net-Based Conditional Generative Adversarial Network for Blood Vessel Segmentation with Reduced Cerebral MR Training Volumes

Segmenting vessels in brain images is a critical step for many medical interventions and diagnoses of illnesses. Recent advances in artificial intelligence provide better models, achieving a human-like level of expertise in many tasks. In this paper, we present a new approach to segment Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) images, relying on fewer training samples than state-of-the-art methods. We propose a conditional generative adversarial network with an adapted generator based on a concatenated U-Net with a residual U-Net architecture (UUr-cGAN) to carry out blood vessel segmentation in TOF-MRA images, relying on data augmentation to diminish the drawback of having few volumes at disposal for training the model, while preventing overfitting by using regularization techniques. The proposed model achieves 89.52% precision and 87.23% in Dice score on average from the cross-validated experiment for brain blood vessel segmentation tasks, which is similar to other state-of-the-art methods while using considerably fewer training samples. UUr-cGAN extracts important features from small datasets while preventing overfitting compared to other CNN-based methods and still achieve a relatively good performance in image segmentation tasks such as brain blood vessels from TOF-MRA

A Robust Sphere Detection in a Realsense Point Cloud by USING Z-Score and RANSAC

Three-dimensional vision cameras, such as RGB-D, use 3D point cloud to represent scenes. File formats as XYZ and PLY are commonly used to store 3D point information as raw data, this information does not contain further details, such as metadata or segmentation, for the different objects in the scene. Moreover, objects in the scene can be recognized in a posterior process and can be used for other purposes, such as camera calibration or scene segmentation. We are proposing a method to recognize a basketball in the scene using its known dimensions to fit a sphere formula. In the proposed cost function we search for three different points in the scene using RANSAC (Random Sample Consensus). Furthermore, taking into account the fixed basketball size, our method differentiates the sphere geometry from other objects in the scene, making our method robust in complex scenes. In a posterior step, the sphere center is fitted using z-score values eliminating outliers from the sphere. Results show our methodology converges in finding the basketball in the scene and the center precision improves using z-score, the proposed method obtains a significant improvement by reducing outliers in scenes with noise from 1.75 to 8.3 times when using RANSAC alone. Experiments show our method has advantages when comparing with novel deep learning method

A Fully Sensorized Cooperative Robotic System for Surgical Interventions

In this research a fully sensorized cooperative robot system for manipulation of needles is presented. The setup consists of a DLR/KUKA Light Weight Robot III especially designed for safe human/robot interaction, a FD-CT robot-driven angiographic C-arm system, and a navigation camera. Also, new control strategies for robot manipulation in the clinical environment are introduced. A method for fast calibration of the involved components and the preliminary accuracy tests of the whole possible errors chain are presented. Calibration of the robot with the navigation system has a residual error of 0.81 mm (rms) with a standard deviation of ±0.41 mm. The accuracy of the robotic system while targeting fixed points at different positions within the workspace is of 1.2 mm (rms) with a standard deviation of ±0.4 mm. After calibration, and due to close loop control, the absolute positioning accuracy was reduced to the navigation camera accuracy which is of 0.35 mm (rms). The implemented control allows the robot to compensate for small patient movements