Beatriz L. Boada

Fuzzy-logic applied to yaw moment control for vehicle stability

In this paper, we propose a new yaw moment control based on fuzzy logic to improve vehicle handling and stability. The advantages of fuzzy methods are their simplicity and their good performance in controlling non-linear systems. The developed controller generates the suitable yaw moment which is obtained from the difference of the brake forces between the front wheels so that the vehicle follows the target values of the yaw rate and the sideslip angle. The simulation results show the effectiveness of the proposed control method when the vehicle is subjected to different cornering steering manoeuvres such as change line and J-turn under different driving conditions (dry road and snow-covered).

Symbolic Place Recognition in Voronoi-Based Maps by Using Hidden Markov Models

This article presents a new algorithm to recognize natural distinctive places such as corridors, halls, narrowings, corridors with doors opening on the left side, etc., from indoor environments using Hidden Markov Models (HMM). HMM give a stochastic solution which can be used to make decisions on localization, navigation and path-planning. The environment is modeled as a topo-geometric map which combines topological and geometric information. This map is obtained from a Voronoi diagram using measurements of a laser telemeter. The characteristics of topo-geometric map (nodes, number of edges adjacent to nodes, slope of edges, etc.) are used to learn and to recognize the different places typical of indoor environments. This map can be used in order to resolve several problems in robotics such as localization, navigation and path-planning. Our method of place recognition is a fast and effective way for a robot to recognize typical places of indoor environments from a topo-geometric map.

Uncertainties Associated with Strain-Measuring Systems Using Resistance Strain Gauges:

Resistance strain gauges have been used for the measurement of strain for more than 50 years; however, research to quantify the inherent uncertainty in a strain-measuring system has been scarce hitherto. Nevertheless, resistive strain gauges are the most widely used tool to measure strain owing to their simplicity, apparent accuracy, low cost, and ease of use. In spite of this, at times they are used improperly, and the sources of error are neglected. Every type of measurement has an uncertainty associated with it. As it is impossible to eliminate error completely, the goal must be to quantify it and to reduce it to a value that is acceptable for the purposes of the measurement being taken. The novelty of the present research is to put forward a new methodology for determining the uncertainty in a strain gauge measuring system. To achieve this, the principal sources of error that influence the measuring system are formulated in order to develop an error model. Subsequently, the law of propagation of uncertainty is applied, together with a type A and B evaluation approach to determine the combined uncertainty of the entire measuring system, taking into consideration the correlation between variables, when applicable. The new methodology is then applied to a series of strain measurements taken on an aluminium flat bar subject to a bending load, and the results are discussed.

Active roll control using reinforcement learning for a single unit heavy vehicle

In this paper, a reinforcement learning algorithm using neural networks to improve the roll stability in a single unit heavy vehicle is proposed. The controller, consisting of active anti-roll bars, provides the adequate roll moments to the vehicle to improve its performance. The main advantages of the implemented neuro control are its good performance to control non-linear systems, it is a free-model control and it learns online, so that the system can adapt to changes produced in the environment. In this case, it is only necessary to measure a unique variable (the sprung mass roll angle) to control the vehicle so both the number of sensors and vehicle cost are reduced. Simulation results show the effectiveness of the proposed control system during different manoeuvres such as J-turn and lane-change.

Localization by Voronoi diagrams correlation

Sensor-based localization is one of the fundamental problems in mobile robots. We present a technique for online robot localization in an a priori known indoor environment. Our approach uses the local Voronoi diagram, generated from a laser range scan, to match it against the global Voronoi diagram of the robots workspace. The result from this process is used to estimate the robots position in the map or to correct the robots odometry. Experiments with real data are presented to validate this algorithm.

Traversable Region Modeling for Outdoor Navigation

This article presents a new methodology to build, in real-time, compact local and global maps for outdoor navigation. The environment information is obtained from a 3D laser range. The navigation model, called Traversable Regions Model (TRM), is based on Voronoi diagram technique but adapted to large outdoor environments, that is, the model is built from 3D data. In the manuscript we also present a novel contribution to the regions modeling field in robotics. The method allows to calculate the roughness degree of an unknown terrain based on the normal vector deviation. The parameter which measure the roughness degree is called spherical variance and it will be useful to determine the traversable areas. The model built allows defining safe trajectories depending on the robots capabilities and the terrain properties and will represent, in a topo-geometric way, the environment as local and global maps. The methodology presented is validated in real outdoor environments with an outdoor robot developed in our lab, called

A fuzzy-based suspension vehicle depending on terrain

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Yaw moment control for vehicle stability in a crosswind

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