Héctor Daniel Patiño

Mobile robot self-localization system using IR-UWB sensor in indoor environments

Robot localization is a fundamental problem in mobile robotics. This paper proposes the use of a new type of sensor that permits accurate localization in indoor environments. An impulse radio (IR) ultra wide-band (UWB) sensor uses very short baseband pulses for transmission. This sensor facilitates the development of indoor location and tracking applications based on time of arrival estimation with great accuracy. There are many different sensors and techniques that have been proposed for indoor localization; however they usually have large errors in non line of sight (NLOS) conditions or with certain environment conditions. The objective of this paper is to propose a self-localization system based on time of arrival (TOA) estimation algorithm that permits precise localization in indoor environments with obstructed line of sight. This work proposes a new wavelet cyclic cross correlation strategy for time of arrival estimation based on non-coherent receiver structure and sliding correlation techniques for multiple user differentiation. The simulation results show that the propose localization system overcome the problems of NLOS conditions and makes possible the mobile robot localization with small number of base stations.

Short-term rainfall time series prediction with incomplete data

In order to predict short-term times series with incomplete data, a proposed approach is presented based on the energy associated of series. A benchmark of rainfall time series and Mackay Glass (MG) samples are used. An average smoothing technique is adopted to complete the dataset. The structure of the predictor filter is changed taking into account the energy associated of the short series. The H parameter is used to estimate the roughness of the complete series, the real and forecasted one. The next 15 values are used as validation and horizon of the time series presented by series of cumulative monthly historical rainfall from La Sevillana, Cordoba, Argentina and samples of the Mackay Glass (MG) differential equation. The performance of the proposed filter shows that even the short dataset is incomplete, besides a linear smoothing technique employed, the prediction is almost fair. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several samples of MG equations and, in particular, MG1.6 and SEV rainfall time series, this method provides a good estimation when the short-term series are taken from one point observations.

Wavelet correlation TOA estimation with dynamic threshold setting for IR-UWB localization system

Impulse Radio (IR) Ultra Wide-Band (UWB) has gained great interest recently for indoor location and tracking applications. Due to its high bandwidth and short pulses length, UWB potentially allows great accuracy in range measurements based on Time of Arrival (TOA) estimation. There are many different techniques that have been proposed for UWB localization; however they usually have big errors in Non Line of Sight (NLOS) conditions. The objective of this paper is to propose a TOA estimation algorithm that permits precise self-localization of mobile vehicles in indoor environments with obstructed line of sight. This paper presents a wavelet cyclic cross-correlation strategy for time of arrival estimation based on dynamic threshold selection, with multi source reference identification. The simulation results show that the proposed algorithm reaches good accuracy in NLOS situations and allows position estimation at low frequency bands.

Neuro-dynamic programming-based optimal control for crop growth in precision agriculture

The agricultural sector is one activity of the major importance in the Argentinean economy, and their production management and control systems are an important subject of research and development. A neuro-dynamic programming based optimal controller for crop-greenhouse systems is proposed. The neurocontroller drives the crop-growth development minimizing a predefined performance index, which considers minimization of the greenhouse operative costs and the final state errors under physical constraints on process variables and actuator signals. In particular, it is applied to guide the tomato seedling crop development through control of a greenhouse microclimate. In the neurocontroller design process nonlinear dynamic behavior of the crop greenhouse system and the July climate data of 1999 of San Juan, Argentina, are considered. The obtained control law is suboptimal due to the use of neural networks to approximate both the optimal cost-to-go function and optimal policy. In order to show the practical feasibility and performance of the proposed neurocontroller, simulation studies were carried out for the tomato-seedling crop development, which would ease the transition to experimentations on a scale model of a greenhouse available in the Instituto de Automatical Laboratory.

Approximate Optimal Control-Based Neurocontroller with a State Observation System for Seedlings Growth in Greenhouse

In this paper, an approximate optimal control-based neurocontroller for guiding the seedlings growth in greenhouse is presented. The main goal of this approach is to obtain a close-loop operation with a state neurocontroller, whose design is based on approximate optimal control theory. The neurocontroller drives the progress of the crop growth development while minimizing a predefined cost function in terms of operative costs and final state errors under physical constraints on process variables and actuator signals. The aim is to find an approximate optimal control policy to guide the development of tomato seedlings from an initial to a desired state by controlling the greenhouses microclimate. In this paper we propose an indirect measuring of the seedlings growth state using artificial vision. In order to show the performance and practical feasibility of the proposed approach, an experiment was carried out for the development of tomato seedings

New Evolutionary Algorithm based on the Mathematical Modeling of the Evolution of a Species

This work presents an abstract approach of a mathematical analysis of the evolution of a species. A mathematical model is proposed as well that characterizes the natural evolutive phenomena. On the basis of this model, and setting the adequate constraints, it is developed an proved the convergency of a new computational evolutionary algorithm, applied to minimizing (or maximizing) a costs function that meets certain requirements. Simulation studies are carried out in order to validate the theoretical aspects and to evaluate the performance of the evolutionary algorithm.

An approach to benchmarking of loosely coupled low-cost navigation systems

New solutions to the navigation problem related to low-cost integrated navigation systems (INS) are often published. Since these new solutions are generally compared with ad hoc mathematical models that are not fully exposed, one cannot be sure of the relative improvements. In this work, complete mathematical model for a low-cost INS is suggested to be used as a benchmarking. As far as the authors’ knowledge, a benchmarking for low-cost INS has not been previously reported. Shown INS comprises a strapdown inertial navigation system, loosely coupled to a GPS receiver. The INS mathematical model is based upon classical navigation equations and classical sensor models, both from recognized authors. The algorithm that details the INS operation is also presented. The benchmarking is provided as an open-source toolbox for MATLAB. Additionally, this work can be taken as a starting point for new practitioners in the INS field. To validate the INS mathematical model, real-world data sets from three different Micro Electro-Mechanical Systems (MEMS) inertial measurement units (IMU) and a GPS receiver are processed. It is observed that obtained RMS errors from the three INS are coherent with the quality of corresponding MEMS IMU. This confirms that the proposed benchmarking is a suitable tool to evaluate objectively new solutions to low-cost INS.

A Neuro-Dynamic Programming-Based Optimal Controller for Tomato Seedling Growth in Greenhouse Systems

This work proposes a neuro-dynamic programming-based optimal controller to guide the growth of tomato seedling crops by manipulating its environmental conditions in a greenhouse. The neurocontroller manages the growth development of the crop, while minimizing a predefined cost function that considers the operative costs and the final state errors under physical constraints on process variables and actuator signals. The aim is to guide the growth of tomato seedlings by controlling the microclimate of the greenhouse. The design process of the neurocontroller considers the nonlinear dynamic behavior of the crop-greenhouse system model and the real climate data. Simulations of the proposed approach allow for contrasting its performance against those of other strategies for tomato seedling crop development subject to various climatic conditions.

Evolving hardware using a new evolutionary algorithm based on evolution of a species

This work presents the analysis of species evolution properties which are considered to design a new evolutionary algorithm for evolvable hardware. These properties reduce the risk of malfunctions in a physical system when it is evolving. A mathematical model, that characterises the natural evolution phenomena of a species, is proposed. A new evolutionary algorithm based on this model is proposed as well. This algorithm is designed to evolve hardware, e.g., to obtain the optimal control parameters of a real control system, while it is executing a repetitive task. The convergence of the proposed algorithm was proven by means of new theorems. Simulations and experimentation studies were carried out in order to validate the theoretical aspects and to evaluate the performance of the evolutionary algorithm.

Short time series prediction: Bayesian enhanced modified approach with application to cumulative rainfall series

This article contributes with short time series prediction with complete and incomplete datasets based on a new framework by means of Bayesian enhanced modified approach BEMA combining permutation entropy. The focus of the proposed filter with particularly interest in incomplete datasets or missing data is by changing the structure of the predictor filter according to data model selected, in which the Bayesian approach can be combined with entropic information of the series. The simplest method adopted to imputing the missing data on the dataset is by linear average smoothing, then computational results are evaluated on high roughness time series selected from benchmark series, in which they are compared with artificial neural networks ANN nonlinear filters such as Bayesian enhanced approach BEA and Bayesian approach BA proposed in recent work, in order to show a better performance of BEMA filter. These results support the applicability of permutation entropy in analysing the dynamic behaviour of chaotic time series for short series predictions.