Ricardo de A. Araújo

Programming‐by‐demonstration in the coworker scenario for SMEs

– The purpose of this paper is to report a collection of developments that enable users to program industrial robots using speech, several device interfaces, force control and code generation techniques., – The reported system is explained in detail and a few practical examples are given that demonstrate its usefulness for small to medium‐sized enterprises (SMEs), where robots and humans need to cooperate to achieve a common goal (coworker scenario). The paper also explores the user interface software adapted for use by non‐experts., – The programming‐by‐demonstration (PbD) system presented proved to be very efficient with the task of programming entirely new features to an industrial robotic system. The system uses a speech interface for user command, and a force‐controlled guiding system for teaching the robot the details about the task being programmed. With only a small set of implemented robot instructions it was fairly easy to teach the robot system a new task, generate the robot code and execute it immediately., – Although a particular robot controller was used, the system is in many aspects general, since the options adopted are mainly based on standards. It can obviously be implemented with other robot controllers without significant changes. In fact, most of the features were ported to run with Motoman robots with success., – It is important to stress that the robot program built in this section was obtained without writing a single line of code, but instead just by moving the robot to the desired positions and adding the required robot instructions using speech. Even the upload task of the obtained module to the robot controller is commanded by speech, along with its execution/termination. Consequently, teaching the robotic system a new feature is accessible for any type of user with only minor training., – This type of PbD systems will constitute a major advantage for SMEs, since most of those companies do not have the necessary engineering resources to make changes or add new functionalities to their robotic manufacturing systems. Even at the system integrator level these systems are very useful for avoiding the need for specific knowledge about all the controllers with which they work: complexity is hidden beyond the speech interfaces and portable interface devices, with specific and user‐friendly APIs making the connection between the programmer and the system.

Force/torque sensing applied to industrial robotic deburring

Force/torque sensing is very important for several automatic and industrial robotic applications. Basically, if precise control of the forces that arise from contact between tools and parts is required to successfully complete the automatic task, then a force/torque sensor is needed along with some force/torque control technique. In this paper we focus on force/torque sensing aspects applied to industrial robotic tasks. Concentrating on a particular type of force/torque sensor, we demonstrate how to use them and how to integrate them into force/torque control applications using robots. Finally, an industrial application is presented where force control was fundamental for the success of the task.

An intelligent hybrid morphological-rank-linear method for financial time series prediction

In this paper the morphological-rank-linear time-lag added evolutionary forecasting (MRLTAEF) method is proposed in order to overcome the random walk dilemma for financial time series prediction. It consists of an intelligent hybrid model composed of a morphological-rank-linear (MRL) filter combined with a modified genetic algorithm (MGA), which searches for the particular time lags capable of a fine tuned characterization of the time series and for the estimation of the initial (sub-optimal) parameters of the MRL filter. Each individual of the MGA population is trained by the averaged least mean squares (LMS) algorithm to further improve the parameters of the MRL filter supplied by the MGA. Initially, the proposed MRLTAEF method chooses the most tuned predictive model for time series representation, and then performs a behavioral statistical test in the attempt to adjust time phase distortions that appear in financial time series. Experiments are conducted with the proposed MRLTAEF method using six real-world financial time series according to a group of relevant performance metrics and the results are compared to multilayer perceptron (MLP) networks, MRL filters and the previously introduced time-delay added evolutionary forecasting (TAEF) method.

A class of hybrid morphological perceptrons with application in time series forecasting

In this work a class of hybrid morphological perceptrons, called dilation-erosion perceptron (DEP), is presented to overcome the random walk dilemma in the time series forecasting problem. It consists of a convex combination of fundamental operators from mathematical morphology (MM) on complete lattices theory (CLT). A gradient steepest descent method is presented to design the proposed DEP (learning process), using the back propagation (BP) algorithm and a systematic approach to overcome the problem of nondifferentiability of morphological operators. The learning process includes an automatic phase fix procedure that is geared at eliminating time phase distortions observed in some time series. Finally, an experimental analysis is conducted with the proposed DEP using five real world time series, where five well-known performance metrics and an evaluation function are used to assess the forecasting performance of the proposed model. The obtained results are compared with those generated by classical forecasting models presented in the literature.

High‐level robot programming based on CAD: dealing with unpredictable environments

Purpose – The purpose of this paper is to present a CAD‐based human‐robot interface that allows non‐expert users to teach a robot in a manner similar to that used by human beings to teach each other.Design/methodology/approach – Intuitive robot programming is achieved by using CAD drawings to generate robot programs off‐line. Sensory feedback allows minimization of the effects of uncertainty, providing information to adjust the robot paths during robot operation.Findings – It was found that it is possible to generate a robot program from a common CAD drawing and run it without any major concerns about calibration or CAD model accuracy.Research limitations/implications – A limitation of the proposed system has to do with the fact that it was designed to be used for particular technological applications.Practical implications – Since most manufacturing companies have CAD packages in their facilities today, CAD‐based robot programming may be a good option to program robots without the need for skilled robot ...

A Morphological-Rank-Linear evolutionary method for stock market prediction

This work presents an evolutionary morphological-rank-linear approach in order to overcome the random walk dilemma for financial time series forecasting. The proposed Evolutionary Morphological-Rank-Linear Forecasting (EMRLF) method consists of an intelligent hybrid model composed of a Morphological-Rank-Linear (MRL) filter combined with a Modified Genetic Algorithm (MGA), which performs an evolutionary search for the minimum number of relevant time lags capable of a fine tuned characterization of the time series, as well as for the initial (sub-optimal) parameters of the MRL filter. Then, each individual of the MGA population is improved using the Least Mean Squares (LMS) algorithm to further adjust the parameters of the MRL filter, supplied by the MGA. After built the prediction model, the proposed method performs a behavioral statistical test with a phase fix procedure to adjust time phase distortions that can appear in the modeling of financial time series. An experimental analysis is conducted with the method using four real world stock market time series according to a group of performance metrics and the results are compared to both MultiLayer Perceptron (MLP) networks and a more advanced, previously introduced, Time-delay Added Evolutionary Forecasting (TAEF) method.

A shift-invariant morphological system for software development cost estimation

Abstract: This work presents a shift-invariant morphological system to solve the problem of software development cost estimation (SDCE). It consists of a hybrid morphological model, which is a linear combination between a morphological-rank (MR) operator (nonlinear) and a Finite Impulse Response (FIR) operator (linear), referred to as morphological-rank-linear (MRL) filter. A gradient steepest descent method to adjust the MRL filter parameters (learning process), using the Least Mean Squares (LMS) algorithm, and a systematic approach to overcome the problem of non-differentiability of the morphological-rank operator are used to improve the numerical robustness of the training algorithm. Furthermore, an experimental analysis is conducted with the proposed system using the NASA software project database, and in the experiments, two relevant performance metrics and an evaluation function are used to assess its performance. The results obtained are compared to models recently presented in literature, showing superior performance of this kind of morphological systems for the SDCE problem.

A hybrid model for high-frequency stock market forecasting

A model to overcome the random walk dilemma for high-frequency financial time series.A gradient-based training algorithm with automatic time phase adjustment.An experimental analysis using time series from Brazilian high-frequency stock market. Several models have been presented to solve the financial time series forecasting problem. However, even with sophisticated techniques, a dilemma arises from all these models, called the random walk dilemma (RWD). In this context, the concept of time phase adjustment was proposed to overcome such dilemma for daily frequency financial time series. However, the evolution of trading platforms had increased the frequency for performing operations on the stock market to fractions of seconds, which makes needed the analysis of high-frequency financial time series. In this way, this work presents a model, called the increasing decreasing linear neuron (IDLN), for high-frequency stock market forecasting. Also, a descending gradient-based method with automatic time phase adjustment is presented for the proposed model design. Besides, an experimental analysis is conduced using a set of high-frequency financial time series from the Brazilian stock market, and the achieved results overcame those obtained by establishing forecasting models in the literature.

Hybrid morphological methodology for software development cost estimation

In this paper we propose a hybrid methodology to design morphological-rank-linear (MRL) perceptrons in the problem of software development cost estimation (SDCE). In this methodology, we use a modified genetic algorithm (MGA) to optimize the parameters of the MRL perceptron, as well as to select an optimal input feature subset of the used databases, aiming at a higher accuracy level for SDCE problems. Besides, for each individual of MGA, a gradient steepest descent method is used to further improve the MRL perceptron parameters supplied by MGA. Finally, we conduct an experimental analysis with the proposed methodology using six well-known benchmark databases of software projects, where two relevant performance metrics and a fitness function are used to assess the performance of the proposed methodology, which is compared to classical machine learning models presented in the literature.

A quantum-inspired hybrid methodology for financial time series prediction

In this work a quantum-inspired hybrid methodology is proposed to overcome the random walk dilemma for financial time series prediction. It consists of a hybrid model composed of a Qubit Multilayer Perceptron (QuMLP) with a Quantum-Inspired Evolutionary Algorithm (QIEA), which searches for the best particular time lags able to characterize the time series phenomenon, as well as to evolve the complete QuMLP architecture and parameters. Each individual of the QIEA population is adjusted by the Complex Back-Propagation (CBP) algorithm to further improve the QuMLP parameters supplied by the QIEA. After the prediction model search procedure, it uses a behavioral statistical test and a phase fix procedure to adjust time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed methodology through four real world financial time series, and the obtained results are discussed and compared to results found with Multilayer Perceptiron (MPL) networks and the previously introduced Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) method.