Algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente

Este estudio está dedicado a los desafíos de la planificación del movimiento para robots móviles con sistemas inteligentes de visión artificial. La planificación del movimiento para robots móviles en un entorno con obstáculos es un problema con el que lidiar al crear robots adecuados para operar en condiciones del mundo real. Las soluciones que se encuentran en la actualidad son predominantemente privadas y altamente especializadas, lo que impide juzgar qué tan exitosas son para resolver el problema de la planificación eficaz del movimiento. Ya existen soluciones con un campo de aplicación estrecho y ya se están desarrollando durante mucho tiempo, sin embargo, aún no se han observado avances importantes. Solo se puede observar una mejora sistemática en las características de tales sistemas. El propósito de este estudio: desarrollar e investigar un algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente. El tema de investigación de este artículo es un algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente. Este estudio proporciona una revisión de robots móviles nacionales y extranjeros que resuelven el problema de planificación de movimiento en un entorno conocido con obstáculos desconocidos. Se consideran los siguientes métodos de navegación para robots móviles: local, global, individual. En el transcurso del trabajo e investigación se ha construido un prototipo de robot móvil, capaz de reconocer obstáculos de formas geométricas regulares, así como planificar y corregir la trayectoria del movimiento. Los objetos del entorno se identifican y clasifican como obstáculos mediante métodos y algoritmos de procesamiento de imágenes digitales. La distancia al obstáculo y el ángulo relativo se calculan mediante métodos de fotogrametría, la calidad de la imagen se mejora mediante la mejora del contraste lineal y el filtrado lineal óptimo utilizando la ecuación de Wiener-Hopf. Se han revisado las herramientas virtuales, relacionadas con las pruebas de algoritmos de movimiento de robots móviles, lo que nos llevó a seleccionar el paquete de software Webots para las pruebas de prototipos. Los resultados de las pruebas nos permitieron sacar las siguientes conclusiones. El robot móvil identificó con éxito el obstáculo, planificó una ruta de acuerdo con el algoritmo de evitación de obstáculos y continuó avanzando hacia el destino. Se han extraído conclusiones con respecto a la investigación concluida

Algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente

This study is devoted to the challenges of motion planning for mobile robots with smart machine vision systems. Motion planning for mobile robots in the environment with obstacles is a problem to deal with when creating robots suitable for operation in real-world conditions. The solutions found today are predominantly private, and are highly specialized, which prevents judging of how successful they are in solving the problem of effective motion planning. Solutions with a narrow application field already exist and are being already developed for a long time, however, no major breakthrough has been observed yet. Only a systematic improvement in the characteristics of such systems can be noted. The purpose of this study: develop and investigate a motion planning algorithm for a mobile robot with a smart machine vision system. The research subject for this article is a motion planning algorithm for a mobile robot with a smart machine vision system. This study provides a review of domestic and foreign mobile robots that solve the motion planning problem in a known environment with unknown obstacles. The following navigation methods are considered for mobile robots: local, global, individual. In the course of work and research, a mobile robot prototype has been built, capable of recognizing obstacles of regular geometric shapes, as well as plan and correct the movement path. Environment objects are identified and classified as obstacles by means of digital image processing methods and algorithms. Distance to the obstacle and relative angle are calculated by photogrammetry methods, image quality is improved by linear contrast enhancement and optimal linear filtering using the Wiener-Hopf equation. Virtual tools, related to mobile robot motion algorithm testing, have been reviewed, which led us to selecting Webots software package for prototype testing. Testing results allowed us to make the following conclusions. The mobile robot has successfully identified the obstacle, planned a path in accordance with the obstacle avoidance algorithm, and continued moving to the destination. Conclusions have been drawn regarding the concluded research.Este estudio está dedicado a los desafíos de la planificación del movimiento para robots móviles con sistemas inteligentes de visión artificial. La planificación del movimiento para robots móviles en un entorno con obstáculos es un problema con el que lidiar al crear robots adecuados para operar en condiciones del mundo real. Las soluciones que se encuentran en la actualidad son predominantemente privadas y altamente especializadas, lo que impide juzgar qué tan exitosas son para resolver el problema de la planificación eficaz del movimiento. Ya existen soluciones con un campo de aplicación estrecho y ya se están desarrollando durante mucho tiempo, sin embargo, aún no se han observado avances importantes. Solo se puede observar una mejora sistemática en las características de tales sistemas. El propósito de este estudio: desarrollar e investigar un algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente. El tema de investigación de este artículo es un algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente. Este estudio proporciona una revisión de robots móviles nacionales y extranjeros que resuelven el problema de planificación de movimiento en un entorno conocido con obstáculos desconocidos. Se consideran los siguientes métodos de navegación para robots móviles: local, global, individual. En el transcurso del trabajo e investigación se ha construido un prototipo de robot móvil, capaz de reconocer obstáculos de formas geométricas regulares, así como planificar y corregir la trayectoria del movimiento. Los objetos del entorno se identifican y clasifican como obstáculos mediante métodos y algoritmos de procesamiento de imágenes digitales. La distancia al obstáculo y el ángulo relativo se calculan mediante métodos de fotogrametría, la calidad de la imagen se mejora mediante la mejora del contraste lineal y el filtrado lineal óptimo utilizando la ecuación de Wiener-Hopf. Se han revisado las herramientas virtuales, relacionadas con las pruebas de algoritmos de movimiento de robots móviles, lo que nos llevó a seleccionar el paquete de software Webots para las pruebas de prototipos. Los resultados de las pruebas nos permitieron sacar las siguientes conclusiones. El robot móvil identificó con éxito el obstáculo, planificó una ruta de acuerdo con el algoritmo de evitación de obstáculos y continuó avanzando hacia el destino. Se han extraído conclusiones con respecto a la investigación concluida

Exploring the use of Android devices and LEGO Mindstorms in Children Color Learning Process

Students are becoming less and less interested to learn about science and technology which causes alert to the education industry. The lack of usage of technology itself in education causes the students to be uninterested to learn as they can only learn the theory but cannot see how it is implemented in the real world. The refusal and slow implementation of incorporating technologies into the education system is also not helping the situation. The reason of the students, the teachers and the system are not really ready to incorporate such technologies worsen the problem.This project mainly aims at introducing a learning approach using interactive technology which is attractive to children and hopefully develops into a new exciting learning pattern that matches the future generation desires. Given the recentness of the approach, the project is also developed with an intention to introduce the new technique involved whilst at the same time paves the way for future work to be conducted related to the incorporation of advanced technology into education process. Using Lego Mindstroms as a learning tools incorporate with an Android device by using a Bluetooth connection will allow children to learn while using technology to interact with the environments. By testing the project to a number of target users, feedbacks are collected and the result and effectiveness of the project are recorded. The objectives of the project are meet and the respondent of the project which are children from age of 3 to 5 give good respond to the prototype. Out of the 10 children tested with the prototype, majority like the project and was able to learn effectively. Parents tend to thinks that it is better for their children to learn from the surrounding than learn with virtual item like learning only using apps that show colors at the screen only. This project will hopefully be the first step in attracting children interest back to the science and technology fields which have been a problem in the last few years

A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new method

Educational hands-on testbed using Lego robot for learning guidance, navigation, and control

The aim of this paper is to propose an educational hands-on testbed using inexpensive systems composed of a Lego Mindstorms NXT robot and a webcam and easy-to-deal-with tools especially for learning and testing guidance, navigation, and control as well as search and obstacle mapping, however the extendibility and applicability of the proposed approach is not limited to only the educational purpose. In order to provide navigation information of the Lego robot in an indoor environment, an vision navigation system is proposed based on a colour marker detection robust to brightness change and an Extended Kalman filter. Furthermore, a spiral-like search, a command-to-line-of-sight guidance, a motor control, and two-dimensional Splinegon approximation are applied to sensing and mapping of a complex-shaped obstacle. The experimental result shows that the proposed testbed can be viewed as an efficient tool for the education of image processing and estimation as well as guidance, navigation, and control with a minimum burden of time and cost. © 2011 IFAC

A.I. LEGO Sorter

The goal of this Major Qualifying Project is to develop a robotic system to autonomously separate, identify, and sort a multitude of LEGO pieces. The solution developed is a three-part sorting apparatus which utilizes complex mechanical design, computer vision, and convolutional neural networks to serialize, classify, and distribute hundreds of unique part combinations. The completed mechanism is capable of processing a large input of unsorted components and fully sorting them by user-defined metrics

New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics

Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive machines and improved data analysis algorithms led to a constant expansion of its fields of applications. Recently, MS was introduced into clinical proteomics with the prospect of early disease detection using proteomic pattern matching. Analyzing biological samples (e.g. blood) by mass spectrometry generates mass spectra that represent the components (molecules) contained in a sample as masses and their respective relative concentrations. In this work, we are interested in those components that are constant within a group of individuals but differ much between individuals of two distinct groups. These distinguishing components that dependent on a particular medical condition are generally called biomarkers. Since not all biomarkers found by the algorithms are of equal (discriminating) quality we are only interested in a small biomarker subset that - as a combination - can be used as a fingerprint for a disease. Once a fingerprint for a particular disease (or medical condition) is identified, it can be used in clinical diagnostics to classify unknown spectra. In this thesis we have developed new algorithms for automatic extraction of disease specific fingerprints from mass spectrometry data. Special emphasis has been put on designing highly sensitive methods with respect to signal detection. Thanks to our statistically based approach our methods are able to detect signals even below the noise level inherent in data acquired by common MS machines, such as hormones. To provide access to these new classes of algorithms to collaborating groups we have created a web-based analysis platform that provides all necessary interfaces for data transfer, data analysis and result inspection. To prove the platform's practical relevance it has been utilized in several clinical studies two of which are presented in this thesis. In these studies it could be shown that our platform is superior to commercial systems with respect to fingerprint identification. As an outcome of these studies several fingerprints for different cancer types (bladder, kidney, testicle, pancreas, colon and thyroid) have been detected and validated. The clinical partners in fact emphasize that these results would be impossible with a less sensitive analysis tool (such as the currently available systems). In addition to the issue of reliably finding and handling signals in noise we faced the problem to handle very large amounts of data, since an average dataset of an individual is about 2.5 Gigabytes in size and we have data of hundreds to thousands of persons. To cope with these large datasets, we developed a new framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that allows to integrate thousands of computing resources (e.g. Desktop Computers, Computing Clusters or specialized hardware, such as IBM's Cell Processor in a Playstation 3)

Emission-Line Galaxies from the HST PEARS Grism Survey I: The South Fields

We present results of a search for emission-line galaxies in the Southern Fields of the Hubble Space Telescope PEARS (Probing Evolution And Reionization Spectroscopically) grism survey. The PEARS South Fields consist of five ACS pointings (including the Hubble Ultra Deep Field) with the G800L grism for a total of 120 orbits, revealing thousands of faint object spectra in the GOODS-South region of the sky. Emission-line galaxies (ELGs) are one subset of objects that are prevalent among the grism spectra. Using a 2-dimensional detection and extraction procedure, we find 320 emission lines orginating from 226 galaxy "knots'' within 192 individual galaxies. Line identification results in 118 new grism-spectroscopic redshifts for galaxies in the GOODS-South Field. We measure emission line fluxes using standard Gaussian fitting techniques. At the resolution of the grism data, the H-beta and [OIII] doublet are blended. However, by fitting two Gaussian components to the H-beta and [OIII] features, we find that many of the PEARS ELGs have high [OIII]/H-beta ratios compared to other galaxy samples of comparable luminosities. The star-formation rates (SFRs) of the ELGs are presented, as well as a sample of distinct giant star-forming regions at z~0.1-0.5 across individual galaxies. We find that the radial distances of these HII regions in general reside near the galaxies' optical continuum half-light radii, similar to those of giant HII regions in local galaxies.Comment: 15 pages, 13 figures; Accepted for publication in A