Geovany Araujo Borges

Line Extraction in 2D Range Images for Mobile Robotics

This paper presents a geometrical feature detection framework for use with conventional 2D laser rangefinders. This framework is composed of three main procedures: data pre-processing, breakpoint detection and line extraction. In data pre-processing, low-level data organization and processing are discussed, with emphasis to sensor bias compensation. Breakpoint detection allows to determine sequences of measurements which are not interrupted by scanning surface changing. Two breakpoint detectors are investigated, one based on adaptive thresholding, and the other on Kalman filtering. Implementation and tuning of both detectors are also investigated. Line extraction is performed to each continuous scan sequence in a range image by applying line kernels. We have investigated two classic kernels, commonly used in mobile robots, and our Split-and-Merge Fuzzy (SMF) line extractor. SMF employs fuzzy clustering in a split-and-merge framework without the need to guess the number of clusters. Qualitative and quantitative comparisons using simulated and real images illustrate the main characteristics of the framework when using different methods for breakpoint and line detection. These comparisons illustrate the characteristics of each estimator, which can be exploited according to the platform computing power and the application accuracy requirements.

A split-and-merge segmentation algorithm for line extraction in 2D range images

This paper presents a segmentation method for line extraction in 2D range images. It uses a prototype-based fuzzy clustering algorithm in a split-and-merge framework. The split-and-merge structure allows one to use the fuzzy clustering algorithm without any previous knowledge on the number of prototypes. This algorithm aims to be used in mobile robots navigation systems for dynamic map building. Simulation results show its good performance compared to some classical approaches.

A Systematization of the Unscented Kalman Filter Theory

In this paper, we propose a systematization of the (discrete-time) Unscented Kalman Filter (UKF) theory. We gather all available UKF variants in the literature, present corrections to theoretical inconsistencies, and provide a tool for the construction of new UKFs in a consistent way. This systematization is done, mainly, by revisiting the concepts of Sigma-Representation, Unscented Transformation (UT), Scaled Unscented Transformation (SUT), UKF, and Square-Root Unscented Kalman Filter (SRUKF). Inconsistencies are related to 1) matching the order of the transformed covariance and cross-covariance matrices of both the UT and the SUT; 2) multiple UKF definitions; 3) issue with some reduced sets of sigma points described in the literature; 4) the conservativeness of the SUT; 5) the scaling effect of the SUT on both its transformed covariance and cross-covariance matrices; and 6) possibly ill-conditioned results in SRUKFs. With the proposed systematization, the symmetric sets of sigma points in the literature are formally justified, and we are able to provide new consistent variations for UKFs, such as the Scaled SRUKFs and the UKFs composed by the minimum number of sigma points. Furthermore, our proposed SRUKF has improved computational properties when compared to state-of-the-art methods.

Robustified estimation algorithms for mobile robot localization based on geometrical environment maps

Abstract This paper presents an improved weighted least-squares algorithm used for optimal 2D pose estimation of mobile robots navigating in real environments represented by geometrical maps. Following this map representation paradigm, feature matching is an important step in pose estimation. In this process, false feature matches may be accepted as reliable. Thus, in order to provide reliable pose estimation even in the presence of a certain level of false matches, robust M-estimators are derived. We further apply some concepts of outlier rejection for deriving a robust Kalman filter-based pose estimator. Extensive comparisons of the proposed robust methods with classic Kalman filtering-based approaches were carried out in real environments.

Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses

The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the users intention. This work presents a feature extraction and pattern classification algorithm for estimation of the intended knee joint angle from SEMG signals acquired using two sets of electrodes placed on the upper leg. The proposed algorithm uses a combination of time-domain and frequency-domain approaches for feature extraction (signal amplitude histogram and auto-regressive coefficients, respectively), a self-organizing map for feature projection and a Levenberg-Marquardt multi-layer perceptron neural network for pattern classification. The new algorithm was quantitatively compared with the method proposed by Wang et al (2006 Med. Biol. Eng. Comput. 44 865-72), which uses wavelet packet feature extraction, principal component analysis and a multi-layer perceptron neural classifier. The proposed method provided lower error-to-signal percentage and peak error amplitudes, higher correlation and fewer error events. The algorithm presented in this work may be useful as part of a myoelectric controller for active leg prostheses designed for transfemoral amputees.

Robust kinematic control of manipulator robots using dual quaternion representation

This paper addresses the H∞ robust control problem for robot manipulators using unit dual quaternion representation, which allows an utter description of the end-effector transformation without decoupling rotational and translational dynamics. We propose three different H∞ control criteria that ensure asymptotic convergence, whereas reducing the influence of disturbances upon the system stability. Also, with a new metric of dual quaternion error in SE(3) we prove independence from robot coordinate changes. Simulation results highlight the importance and effectiveness of the proposed approach in terms of performance, robustness, and energy efficiency.

A robust extended Kalman filter for discrete-time systems with uncertain dynamics, measurements and correlated noise

This paper presents a robust filter for discrete-time nonlinear systems subject to uncertainties. The nonlinear functions are assumed to be uncertain but belonging to a conic region. This condition is characterized as a Lipschitz condition on the system state and control signal residuals. The proposed design also allows dynamic and measurement noises to have unknown time-varying expected values, covariances and cross-covariances. The filter furnishes estimations with the a priori and a posteriori variance errors bounded for all allowed uncertainties.

Semi-automatic needle steering system with robotic manipulator

This paper presents a semi-automatic system for robotically assisted 2D needle steering that uses duty-cycling to perform insertions with arcs of adjustable curvature radius. It combines image feedback manually provided by an operator with an adaptive path planning strategy to compensate for system uncertainties and changes in the workspace during the procedure. Experimental results are presented to validate the proposed platform.

An optimal pose estimator for map-based mobile robot dynamic localization: experimental comparison with the EKF

Theoretical solutions based on the matching of 2D range measurements with a map of the environment have been proposed to solve the robot localization problem. However most of them have not been experimented with in real conditions: the robot was stopped or it moved slowly during range data acquisition, and the environment was supposed to be static. We propose and evaluate a dynamic localization method based on feature matching. Experiments carried out in real cluttered indoor environments including people and unknown obstacles show the good performance of the proposed algorithm against the classical solution based on Kalman filtering.

Modeling device manufacturing uncertainty in Electromagnetic Simulations

This work presents a technique for modeling of the effects of manufacturing uncertainty into Electromagnetic Simulations of microwave devices. The procedure combines the Unscented Transform (UT) with EM Simulations. The use of the UT allows efficient use of computational resources for the characterization of the random variables modeling the uncertainty. The technique is validated with the modeling of tolerance errors of an assembled microstrip pseudo-elliptic filter into the response of the electromagnetic simulation.