Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Soil compaction by machine traffic and least limiting water range related to soybean yield

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

Aquatic weed automatic classification using machine learning techniques

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

HOW FAR do WE GET USING MACHINE LEARNING BLACK-BOXES?

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

Analysis of surface integrity for minimum quantity lubricant - MQL in grinding

The quality of machined components is currently of high interest, for the market demands mechanical components of increasingly high performance, not only from the standpoint of functionality but also from that of safety. Components produced through operations involving the removal of material display surface irregularities resulting not only from the action of the tool itself, but also from other factors that contribute to their superficial texture. This texture can exert a decisive influence on the application and performance of the machined component. This article analyzes the behavior of the minimum quantity lubricant (MQL) technique and compares it with the conventional cooling method. To this end, an optimized fluid application method was devised using a specially designed nozzle, by the authors, through which a minimum amount of oil is sprayed in a compressed air flow, thus meeting environmental requirements. This paper, therefore, explores and discusses the concept of the MQL in the grinding process. The performance of the MQL technique in the grinding process was evaluated based on an analysis of the surface integrity (roughness, residual stress, microstructure and microhardness). The results presented here are expected to lead to technological and ecological gains in the grinding process using MQL. (c) 2006 Elsevier Ltd. All rights reserved.

Safety illusion and error trap in a collectively-operated machine accident

Workplace accidents involving machines are relevant for their magnitude and their impacts on worker health. Despite consolidated critical statements, explanation centered on errors of operators remains predominant with industry professionals, hampering preventive measures and the improvement of production-system reliability. Several initiatives were adopted by enforcement agencies in partnership with universities to stimulate production and diffusion of analysis methodologies with a systemic approach. Starting from one accident case that occurred with a worker who operated a brake-clutch type mechanical press, the article explores cognitive aspects and the existence of traps in the operation of this machine. It deals with a large-sized press that, despite being endowed with a light curtain in areas of access to the pressing zone, did not meet legal requirements. The safety devices gave rise to an illusion of safety, permitting activation of the machine when a worker was still found within the operational zone. Preventive interventions must stimulate the tailoring of systems to the characteristics of workers, minimizing the creation of traps and encouraging safety policies and practices that replace judgments of behaviors that participate in accidents by analyses of reasons that lead workers to act in that manner.

Effect of Artificial Aging on the Roughness and Microhardness of Sealed Composites

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

Influence of Investment, Disinfection, and Storage on the Microhardness of Ocular Resins

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

The Effect of Mechanical Cycling and Different Misfit Levels on Vicker's Microhardness of Retention Screws for Single Implant-Supported Prostheses

Purpose: The aim of this study was to evaluate the effect of mechanical cycling and different misfit levels on Vicker's microhardness of retention screws for single implant-supported prostheses.Materials and Methods: Premachined UCLA abutments were cast with cobalt-chromium alloy to obtain 48 crowns divided into four groups (n = 12). The crowns presented no misfit in group A (control group) and unilateral misfits of 50 mu m, 100 mu m, and 200 mu m in groups B, C, and D, respectively. The crowns were screwed to external hexagon implants with titanium retention screws (torque of 30 N/cm), and the sets were submitted to three different periods of mechanical cycling: 2 x 10(4), 5 x 10(4), and 1 x 10(6) cycles. Screw microhardness values were measured before and after each cycling period. Data were evaluated by two-way ANOVA and Tukey's test (p < 0.05).Results: Mechanical cycling statistically reduced microhardness values of retention screws regardless of cycling periods and groups. In groups A, B, and C, initial microhardness values were statistically different from final microhardness values (p < 0.05). There was no statistically significant difference for initial screw microhardness values (p > 0.05) among the groups; however, when the groups were compared after mechanical cycling, a statistically significant difference was observed between groups B and D (p < 0.05).Conclusions: Mechanical cycling reduced the Vicker's microhardness values of the retention screws of all groups. The crowns with the highest misfit level presented the highest Vicker's microhardness values.

Effect of weathering and thickness on the superficial microhardness of acrylic resin and ocular button

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

In Vitro Evaluation of Surface Roughness and Microhardness of Restorative Materials Submitted to Erosive Challenges

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