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

Fast single image defogging with robust sky detection

Haze is a source of unreliability for computer vision applications in outdoor scenarios, and it is usually caused by atmospheric conditions. The Dark Channel Prior (DCP) has shown remarkable results in image defogging with three main limitations: 1) high time-consumption, 2) artifact generation, and 3) sky-region over-saturation. Therefore, current work has focused on improving processing time without losing restoration quality and avoiding image artifacts during image defogging. Hence in this research, a novel methodology based on depth approximations through DCP, local Shannon entropy, and Fast Guided Filter is proposed for reducing artifacts and improving image recovery on sky regions with low computation time. The proposed-method performance is assessed using more than 500 images from three datasets: Hybrid Subjective Testing Set from Realistic Single Image Dehazing (HSTS-RESIDE), the Synthetic Objective Testing Set from RESIDE (SOTS-RESIDE) and the HazeRD. Experimental results demonstrate that the proposed approach has an outstanding performance over state-of-the-art methods in reviewed literature, which is validated qualitatively and quantitatively through Peak Signal-to-Noise Ratio (PSNR), Naturalness Image Quality Evaluator (NIQE) and Structural SIMilarity (SSIM) index on retrieved images, considering different visual ranges, under distinct illumination and contrast conditions. Analyzing images with various resolutions, the method proposed in this work shows the lowest processing time under similar software and hardware conditions.This work was supported in part by the Centro en Investigaciones en Óptica (CIO) and the Consejo Nacional de Ciencia y Tecnología (CONACYT), and in part by the Barcelona Supercomputing Center.Peer ReviewedPostprint (published version

DISEÑO, MODELO CINEMÁTICO Y SIMULACIÓN DE UN ROBOT NEUMÁTICO DE 4 DOF (DESIGN, KINEMATIC MODEL AND SIMULATION OF A 4 DOF PNEUMATIC ROBOT)

En el desarrollo de la tecnología industrial, los robots industriales juegan un papel muy importante para una gran variedad de tareas, como es traslado de materiales, aplicación de soldadura, aplicación de pintura, entre otros. Actualmente, gran parte de los sistemas robóticos son accionados mediante actuadores eléctricos debido a su fácil control. Sin embargo, los actuadores eléctricos tienen una relación potencia-peso baja. Esto es, para levantar poco peso, requieren de una gran cantidad de energía. Los actuadores eléctricos no son los únicos que pueden ser utilizados, también se tienen actuadores neumáticos pero en menor cantidad que los eléctricos. Los actuadores neumáticos tienen una alta relación potencia-peso, pues pueden levantar mayor peso con menos energía y sin riesgo de daños. La fuente de energía de un actuador neumático es el aire comprimido, por lo que no se genera contaminantes y en caso de que el actuador se sobrecargue, simplemente no se va a mover y no se genera daño alguno. En este trabajo se presenta el desarrollo de la simulación de la planeación de trayectoria de un robot neumático de 4 grados de libertad (DOF), a partir del desarrollo del modelo cinemático. Así mismo, se presenta el análisis cinemático de velocidad del robot y el análisis de posición de un mecanismo de 4 barras del manipulador presente en el diseño del robot neumático.Palabra(s) Clave: Cinemática, Planeación de trayectoria, Robots neumáticos. AbstractIn the development of industrial technology, industrial robots have a very important role in different tasks, such as, material displacement, welding processes, paint application, among others. Currently, most of the robotic systems are powered by electric actuators due to its easy control. However, electric actuators have a low power-to-weight ratio. In other words, in order to lift little weight, they require a large amount of energy. Not only electric actuators can be used, but also there are pneumatic actuators that are in fewer quantities than the electric ones. Pneumatic actuators have a high power-to-weight ratio, because they can lift more weight with less energy and without damage risk. The source of energy of a pneumatic actuator is compressed air, so contaminants are not generated and in case the actuator is overloaded, it would simply not move and no damage generated. This paper presents the development of the simulation of the planning trajectory of a pneumatic robot with 4 degrees of freedom (DOF), from the kinematic model development. Additionally, it presents the kinematic analysis of the robot's speed and the position analysis of a 4-bar mechanism from the manipulator in the design of the pneumatic robot.Keywords: pneumatic robot, kinematic model, planning trajectory

Perspective Chapter: Airborne Pollution (PM2.5) Forecasting Using Long Short-Term Memory Deep Recurrent Neural Network Optimized by Gaussian Process

Forecasting air pollution is a challenging problem today that requires special attention in large cities since they are home to millions of people who are at risk of respiratory diseases every day. At the same time, there has been exponential growth in the research and application of deep learning, which is useful to treat temporary data such as pollution levels, leaving aside the physical and chemical characteristics of the particles and only focusing on predicting the next levels of contamination. This work seeks to contribute to society by presenting a useful way to optimize recurrent neural networks of the short and long-term memory type through a statistical process (Gaussian processes) for the correct optimization of the processes

Modeling of Particulate Pollutants Using a Memory-Based Recurrent Neural Network Implemented on an FPGA

The present work describes the training and subsequent implementation on an FPGA board of an LSTM neural network for the modeling and prediction of the exceedances of criteria pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10 and PM2.5). Understanding the behavior of pollutants and assessing air quality in specific geographical regions is crucial. Overexposure to these pollutants can cause harm to both natural ecosystems and living organisms, including humans. Therefore, it is essential to develop a solution that can accurately evaluate pollution levels. One potential approach is to implement a modified LSTM neural network on an FPGA board. This implementation obtained an 11% improvement compared to the original LSTM network, demonstrating that the proposed architecture is able to maintain its functionality despite reducing the number of neurons in its initial layers. It shows the feasibility of integrating a prediction network into a limited system such as an FPGA board, but easily coupled to a different system. Importantly, this implementation does not compromise the prediction accuracy for both 24 h and 72 h time frames, highlighting an opportunity for further enhancement and refinement

Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA

Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of ±1.2 mm

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

Reduced Calibration Strategy Using a Basketball for RGB-D Cameras

RGB-D cameras produce depth and color information commonly used in the 3D reconstruction and vision computer areas. Different cameras with the same model usually produce images with different calibration errors. The color and depth layer usually requires calibration to minimize alignment errors, adjust precision, and improve data quality in general. Standard calibration protocols for RGB-D cameras require a controlled environment to allow operators to take many RGB and depth pair images as an input for calibration frameworks making the calibration protocol challenging to implement without ideal conditions and the operator experience. In this work, we proposed a novel strategy that simplifies the calibration protocol by requiring fewer images than other methods. Our strategy uses an ordinary object, a know-size basketball, as a ground truth sphere geometry during the calibration. Our experiments show comparable results requiring fewer images and non-ideal scene conditions than a reference method to align color and depth image layers