Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.

TAO - A software platform for autonomous mobile robots

To date, different techniques of navigation for mobile robots have been developed. However, the experimentation of these techniques is not a trivial task because usually it is not possible to reuse the developed control software due to system incompabilities. This paper proposes a software platform that provides means for creating reusable software modules through the standardization of software interfaces, which represent the various robot modules. © 2012 ICROS.

Local data fusion algorithm for fire detection through mobile robot

Multisensor data fusion is a technique that combines the readings of multiple sensors to detect some phenomenon. Data fusion applications are numerous and they can be used in smart buildings, environment monitoring, industry and defense applications. The main goal of multisensor data fusion is to minimize false alarms and maximize the probability of detection based on the detection of multiple sensors. In this paper a local data fusion algorithm based on luminosity, temperature and flame for fire detection is presented. The data fusion approach was embedded in a low cost mobile robot. The prototype test validation has indicated that our approach can detect fire occurrence. Moreover, the low cost project allow the development of robots that could be discarded in their fire detection missions. © 2013 IEEE.

Mapeamento Mobile Art: propostas poéticas em telefones celulares : de 2001 a 2010

Pós-graduação em Artes - IA

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

Stable, mobile, dark-in-bright, dipolar Bose-Einstein-condensate solitons

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Stable and mobile excited two-dimensional dipolar Bose-Einstein condensate solitons

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bright dipolar Bose-Einstein-condensate soliton mobile in a direction perpendicular to polarization

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Think: guia de desenvolvimento mobile ágil em equipes reduzidas

Currently, applications for smartphones and tablets, called apps, are becoming increasingly relevant and attract more attention from users and finally the developers. With the Application Stores, services provided by the company that maintains the platform, access to such applications is as or more simplified than to web sites, with the advantage of anenhanced user experience and focused on the mobile device, and enjoy natives resources as camera, audio, storage, integration with other applications, etc. They present a great opportunity for independent developers, who can now develop an application and make it availabl e to all users of that platform, at free or at a cost that is usually low. Even students may create their applications in the intervals of their classes and sell them in stores. Making use of tools and services, free or at low cost, anyone can develop quality applications, that can be marketed and have a large number of users even in adverse situations in which the application is not the focus of developer productivity. However, such to ols do not seem to be well used, or are unknown, or its purpose is not considered important, and this paper tries to show the real importance of these tools in the rapid development of quality software. This project presents several tools, services and practices, which together make it possible to develop an application for various mobile platforms, independently and with a team of a few people, as demonstrated. However, this paper aims not to say that the development of software today it is easy and simple, but there are currently a large set of tools, for various platforms, that assists and enhances the work of the programmer

MiLoc-Sys Mobile: gestão em locação

Companies that invest in current technologies maintain themselves updated, improve their business rules and anticipate themselves against rivals providing a better service to their customers. This project aims to develop an ERP - Enterprise Resource Planning module for Android which complements an existing manager system and, that attends the needs of a rental equipment business for civil building, i.e., it improves the communication channel company-client and betters the identification and control of products. During the developing of this project, it was necessary to study the company business rules, analyze the requirements and the appropriate technologies. This project was organized in two parts, contemplating e ach of these needs. It were implemented specific modules for generate budgets and pre-orders in the first part and, the use of radiofrequency tags in the second one. Thus, it was possible to assign mobility to company business rules so that a better rental service can be provided and the equipments can be better managed

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Organization model for mobile wireless sensor networks inspired in artificial bee colony

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)