Positioning of a 3-degrees-of-freedom robot with pneumatic actuators

Interest in the use of pneumatic actuators has become increasingly significant, especially because of the potential benefits of employing clean energies. There are previous works related to this project; some made abroad that innovates in control methods but only in specific applications, and others in Mexico that progressively increase the complexity of the system. In this paper the difficulty of a generic threedegrees-of-freedom (DoF) robot arm control is tested, searching for the most efficient method to reproduce the positioning of the final effector (a griper) accurately. Furthermore, the ambitious goal of the project is to adequately develop an algorithm capable of being properly adjusted to any multipurpose robot system with pneumatic actuators. It is carefully considered the various non-linearities of the system. This work relied mostly on intelligent control techniques and modern programmable devices, the intention is to achieve similar or better results than previous projects. The possible combination of intelligent and even classical control can reasonably achieve the estimated conclusion, the first being flexible for developing robust design for non-linear systems

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Artificial Intelligence - Emerging Trends and Applications

Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area

Artificial Intelligence - Applications in Medicine and Biology

Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area

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

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

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

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder–Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too