Roberto Iglesias

A fuzzy temporal rule-based velocity controller for mobile robotics

This paper describes a velocity controller implemented on a Nomad 200 mobile robot. The controller has been developed for wall-following behaviour, and its design is modularized into two blocks: angular and linear velocity control. A simple design and implementation was made for the former, with the aim of focusing the design efforts on the linear velocity control block, in order to remark the usefulness of this task. The latter has been implemented using an explicit model for knowledge representation and reasoning called fuzzy temporal rules (FTRs). This model enables to explicitly incorporate time as a variable, due to which the evolution of variables in a temporal reference can be described. Using this mechanism we obtain linear velocity values that are adapted to each different circumstance, and thus a higher average velocity as well as smoother and more robust behaviours are achieved.

Fuzzy temporal rules for mobile robot guidance in dynamic environments

The paper describes a fuzzy control system for the avoidance of moving objects by a robot. The objects move with no type of restriction, varying their velocity and making turns. Due to the complex nature of this movement, it is necessary to realize temporal reasoning with the aim of estimating the trend of the moving object. A new paradigm of fuzzy temporal reasoning, which we call fuzzy temporal rules (FTRs), is used for this control task. The control system has over 117 rules, which reflects the complexity of the problem to be tackled. The controller has been subjected to an exhaustive validation process and examples are shown of the results obtained.

Feature analysis for human recognition and discrimination: Application to a person-following behaviour in a mobile robot

One of the most important abilities that personal robots need when interacting with humans is the ability to discriminate amongst them. In this paper, we carry out an in-depth study of the possibilities of a colour camera placed on top of a robot to discriminate between humans, and thus get a reliable person-following behaviour on the robot. In particular we have reviewed and analysed the possibility of using the most popular colour and texture features used in object and texture recognition, to identify and model the target (person being followed). Nevertheless, the real-time restrictions make necessary the selection of a reduced subset of these features to reduce the computational burden. This subset of features was selected after carrying out a redundancy analysis, and considering how these features perform when discriminating amongst similar human torsos. Finally, we also describe several scoring functions able to dynamically adjust the relevance of each feature considering the particular conditions of the environment where the robot moves, together with the characteristics of the clothes worn by the persons that are in the scene. The results of this in-depth study have been implemented in a novel and adaptive system (described in this paper), which is able to discriminate between humans to get reliable person-following behaviours in a mobile robot. The performance of our proposal is clearly shown through a set of experimental results obtained with a real robot working in real and difficult scenarios.

Rapid Method for Finding Faulty Elements in Antenna Arrays Using Far Field Pattern Samples

A simple and fast technique that allows a diagnosis of faulty elements in antenna arrays, that only needs to consider a small number of samples of its degraded far-field pattern is described. The method tabulates patterns radiated by the array with 1 faulty element only. Then, the pattern corresponding to the configuration of failed/unfailed elements under test is calculated using the error-free pattern and the patterns with 1 faulty element. The configuration with the lowest difference between the calculated and the degraded patterns is selected. Comparison of the performance of this method using an exhaustive search and a genetic algorithm for an equispaced linear array of 100 lambda/2-dipoles is shown. Mutual coupling as well as noise/measurement errors in the pattern samples were considered in the numerical analysis.

Fast Array Thinning using Global Optimization Methods

A simple and fast method to accelerate the global optimization approaches used in array thinning is described. This method tabulates the contribution of every array element to the far-field pattern in order to improve the numerical efficiency of the optimization algorithm employed. Experiments using our proposal alongside with a genetic algorithm reduce the search computation time about 90%.

Element failure detection in linear antenna arrays using case-based reasoning

The present work proposes a novel case-based reasoning system for fault diagnosis in moderate or large linear antenna arrays. This system identifies the set of elements that are most likely to be defective, helping to significantly reduce the computational costs of their detection (e.g., using an optimization technique such as a genetic algorithm).

Visual task identification and characterization using polynomial models

Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour based on theoretical models. Instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. This paper proposes a method of dealing with these issues by (a) establishing task-achieving sensor-motor couplings through robot training, and (b) representing these couplings through transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour. We demonstrate the viability of this approach by teaching a mobile robot to track a moving football and subsequently modelling this task using the NARMAX system identification technique.

Particle filter robot localisation through robust fusion of laser, WiFi, compass, and a network of external cameras

Particle filter robot localisation fusing 2D laser, WiFi, compass and external cameras.Works with any sensor combination (even if unsynchronized or different data rates).Experiments in controlled situations and real operation in social events.Analysis and discussion of performance of each sensor and all sensor combinations.Best results obtained from the fusion of all the sensors (statistical significance). In this paper, we propose a multi-sensor fusion algorithm based on particle filters for mobile robot localisation in crowded environments. Our system is able to fuse the information provided by sensors placed on-board, and sensors external to the robot (off-board). We also propose a methodology for fast system deployment, map construction, and sensor calibration with a limited number of training samples. We validated our proposal experimentally with a laser range-finder, a WiFi card, a magnetic compass, and an external multi-camera network. We have carried out experiments that validate our deployment and calibration methodology. Moreover, we performed localisation experiments in controlled situations and real robot operation in social events. We obtained the best results from the fusion of all the sensors available: the precision and stability was sufficient for mobile robot localisation. No single sensor is reliable in every situation, but nevertheless our algorithm works with any subset of sensors: if a sensor is not available, the performance just degrades gracefully.

Supervised Reinforcement Learning: Application to a Wall Following Behaviour in a Mobile Robot

In this work we describe the design of a control approach in which, by way of supervised reinforcement learning, the learning potential is combined with the previous knowledge of the task in question, obtaining as a result rapid convergence to the desired behaviour as well as an increase in the stability of the process. We have tested the application of our approach in the design of a basic behaviour pattern in mobile robotics, such as that of wall following. We have carried out several experiments obtaining goods results which confirm the utility and advantages derived from the use of our approach.

Accurate robot simulation through system identification

Robot simulators are useful tools for developing robot behaviour. They provide a fast and efficient means for testing robot control code at the convenience of the office desk. In all but the simplest cases though, due to complexities of physical systems modelled in the simulator, there are considerable differences between the behaviour of the robot in the simulator and that in the real world environment. In this paper we present a novel method to create a robot simulator using real sensor data. Logged sensor data are used to construct a mathematically explicit model (in the form of a NARMAX polynomial) of the robots environment. The advantage of such a transparent model -in contrast to opaque modelling methods such as artificial neural networks -is that it can be analysed to characterise the modelled system, using established mathematical methods. In this paper we compare the behaviour of the robot running a particular task in both the simulator and the real-world using qualitative and quantitative measures including statistical methods to investigate the faithfulness of the simulator.