Fernando Carreira

Exploiting the FAMOUSO middleware in multi-robot application development with Matlab/Simulink

We describe a framework for the development of distributed systems combining real and virtual components, sensors and actuators. We show the benefits of our approach for the development and validation of multi robot applications. Based on our middleware, which provides a flexible communication for distributed systems, virtual and real components are seamlessly exchangeable during different development steps. This modularity and compatibility allows appropriate adjustments for design, rapid prototyping and examination as soon as an opportunity to reduce the hardware effort for large scenarios.

Fault Detection Approach Based on Fuzzy Qualitative Reasoning Applied to the DAMADICS Benchmark Problem

Abstract A computer assisted fault detection methodology based on a fuzzy qualitative simulation algorithm is described. The adoption of fuzzy sets allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. The fuzzy representation of qualitative values is more general than ordinary interval representation, since it can represent not only the information stated by a well defined real interval but also the knowledge embedded in the soft boundaries of the interval. Such a methodology was applied to a pneumatic servomotor actuated control valve that is the benchmark problem of the EC RTN DAMADICS.

2D PCA-based localization for mobile robots in unstructured environments

In this paper a new PCA-based positioning sensor and localization system for mobile robots to operate in unstructured environments (e.g. industry, services, domestic...) is proposed and experimentally validated. The inexpensive positioning system resorts to principal component analysis (PCA) of images acquired by a video camera installed onboard, looking upwards to the ceiling. This solution has the advantage of avoiding the need of selecting and extracting features. The principal components of the acquired images are compared with previously registered images, stored in a reduced onboard image database, and the position measured is fused with odometry data. The optimal estimates of position and slippage are provided by Kalman filters, with global stable error dynamics. The experimental validation reported in this work focuses on the results of a set of experiments carried out in a real environment, where the robot travels along a lawn-mower trajectory. A small position error estimate with bounded co-variance was always observed, for arbitrarily long experiments, and slippage was estimated accurately in real time.

Sentient Objects for Designing and Controlling Service Robots

Abstract Services related to healthcare and the support for elderly people become more and more important. Autonomous or semi-autonomous robots may play an important role in this area. From a control system point of view these robots are networks of distributed smart components to perceive their environment and react on it in real time. The problem of developing or extending such a robot often is that the designer has to start from scratch struggling with low level issues, where reusability of already designed components would be highly desirable. The paper describes a robot application in the area of a meals distribution service that combines two design worlds. One is the conventional world of modelling the functional properties without any structural considerations, the other is the world of cooperating sentient objects. We explain how the notion of sentient objects will assist the design, simulation and also later extensions and adaptations of the robot.

A Bayesian grid method PCA-based for mobile robots localization in unstructured environments

This paper presents the experimental validation of a new method for mobile robot global self-localization in unstructured environments, i.e. that does not need any beacons or other artifacts structuring the environment. The method resorts to a PCA-based positioning sensor, filtered in a Bayesian probabilistic grid and combined with linear Kalman filters to estimate the global pose of mobile robots. In the implemented system, the information of the environment is captured only with onboard sensors installed in a differential drive robot: encoders, compass, and 2D depth sensor pointed to the ceiling. The use of PCA in a Bayesian probabilistic grid allows to fuse the highly compressed PCA database information, obtained with the low computational effort, in an environment where repetitive scenarios can occur. To avoid the negative impact in the localization estimate caused by the corrupted data existing in the 2D depth sensor, an extension to the classic PCA algorithm is suggested. Thus, the proposed method allows the self-localization of mobile robots in indoor environments with bounded accuracy and working in a wide range of illumination conditions.

Complementary Filter Design with Three Frequency Bands: Robot Attitude Estimation

This paper extents the by now classic sensor fusion complementary filter (CF) design, involving two sensors, to the case where three sensors that provide measurements in different bands are available. This paper shows that the use of classical CF techniques to tackle a generic three sensors fusion problem, based solely on their frequency domain characteristics, leads to a minimal realization, stable, sub-optimal solution, denoted as Complementary Filters3 (CF3). Then, a new approach for the estimation problem at hand is used, based on optimal linear Kalman filtering techniques. Moreover, the solution is shown to preserve the complementary property, i.e. the sum of the three transfer functions of the respective sensors add up to one, both in continuous and discrete time domains. This new class of filters are denoted as Complementary Kalman Filters3 (CKF3). The attitude estimation of a mobile robot is addressed, based on data from a rate gyroscope, a digital compass, and odometry. The experimental results obtained are reported.

Experimental validation of a visual odometry system for indoor unstructured environments

The main goal of this paper is to present a visual odometry system, for localization of mobile robot in indoor unstructured environments, using only ceiling depth images, captured by a Kinect sensor. The use of odometric sensors is a common practice for localization of mobile robots. The method proposed in this work exploits information from an independent source of depth data and thus allows to complement or substitute the use of classic odometric sensors, like wheels encoders, with well known limitations. The experimental validation of the proposed solution shows that the method is able to accurately compute the attitude and linear velocities that allow a more precise mobile robot localization, even in presence of corrupted data from the sensor. Furthermore, the method works in an extended range of lighting conditions, without the need to perform any specific feature extraction.

A Fuzzy Controller for a Health Services Mobile Robot

The purpose of this paper is to present the devolvement of a fuzzy control applied for a hospitals and health care centres mobile concept robot, the i-MERC. The robot and fuzzy controller models are presented as well as simulation results. The simulation application that was developed included the possibility to change de bending radius of the curves. To analyze the performance of the robot in following curves posing different dynamic demands simulations were performed considering curves with an abrupt change of direction and with 10, 20, 30, 40 and 50 cm of radius. A virtual reality resource allows demonstrating the service concept with the purpose of searching stakeholders interested in the project.

A complete frontier-based exploration method for Pose-SLAM

In this paper we propose a complete frontier-based exploration method for Pose SLAM. The goal of current frontier-based exploration methods is to eliminate all frontiers in the environment map. This approach, however, does not meet the requirements of Pose SLAM mapping: besides arriving at a frontier free map, there is the need to close the loops in the environment. In fact, loop closing is instrumental to Pose SLAM, since the major corrections to the map result from adding place revisiting constraints. The exploration method we propose is said to be complete in the sense that all loops are guaranteed to be closed. In this paper, we also describe a hybrid map that supports our exploration scheme and detail all the mechanisms required to maintain it during exploration. The validity of the approach is demonstrated on a widely used dataset.