Diego Rodriguez-Losada

An exact bit-parallel algorithm for the maximum clique problem

This paper presents a new exact maximum clique algorithm which improves the bounds obtained in state of the art approximate coloring by reordering the vertices at each step. Moreover, the algorithm can make full use of bit strings to sort vertices in constant time as well as to compute graph transitions and bounds efficiently, exploiting the ability of CPUs to process bitwise operations in blocks of size the ALU register word. As a result it significantly outperforms a current leading algorithm.

The fuzzy Kalman filter: State estimation using possibilistic techniques

A new method to implement fuzzy Kalman filters is introduced. The combination of possibilistic techniques and the extended Kalman filter has special application in fields where inaccurate information is involved. The novelty of this article comes from the fact that by using possibility distributions, instead of Gaussian distributions, a fuzzy description of the expected state and observation is sufficient to obtain a good estimation. Some characteristics of this approach are that uncertainty does not need to be symmetric, and that a wide region of possible values for the expectations is allowed. To implement the algorithm, this approach also contributes a method to propagate uncertainty through the process model and the observation model, based on trapezoidal possibility distributions. Finally, several examples of a real mobile robot moving through a localization process, while using qualitative landmarks, are shown.

The Evolution of Guido

Guido, a smart walker developed by Haptica (Dublin, Ireland), is a robotic walking aid that addresses the needs of the frail visually impaired, providing them with a safe means of taking exercise independently as well as navigation assistance in new environments. It was based on a program of research in the Trinity College Dublin from 1995 to 2000. Many similar devices have subsequently been developed, such as the RT-Walker from Kosuge and Hirata Laboratory, the walker developed by Medical Automation Research Center, and the Personal Aid for Mobility and Monitoring (PAMM) devices developed at the Massachusetts Institute of Technology (MIT). Guido has important distinctive characteristics such as its design, its commercialization, and its recently developed map-based navigation. Following the success with the sales of preproduction units of Guido to the U.S. Department of Veterans Affairs, it was withdrawn from the market in early 2002 following the aftereffects of 9/11 and the dot-com crash. Recently, we have initiated a program of research to update Guido, with the latest research results in simultaneous localization and mapping (SLAM). In this article, we review the design and development of Guido and go on to describe the updates that have been empirically evaluated in office and nursing home environments and compare then with the previous control strategy. Finally, the implications are discussed and its future work outlined.

Building geometric feature based maps for indoor service robots

Abstract This paper presents an efficient geometric approach to the Simultaneous Localization and Mapping problem based on an Extended Kalman Filter. The map representation and building process is formulated, fully implemented and successfully experimented in different indoor environments with different robots. The use of orthogonal shape constraints is proposed to deal with the inconsistency of the estimation. Built maps are successfully used for the navigation of two different service robots: an interactive tour guide robot, and an assistive walking aid for the frail elderly.

Consistency improvement for SLAM - EKF for indoor environments

The solution to the simultaneous localization and mapping (SLAM) problem using an extended Kalman filter (EKF) is the most extended despite the inconsistency of its estimation, a problem that has been largely avoided in the literature. We review current existing approaches and present novel solutions to this problem that let us to build large monolithic feature based maps of indoor environments

Local maps fusion for real time multirobot indoor simultaneous localization and mapping

This paper presents an implementation of the local maps fusion concept for the simultaneous localization and mapping (SLAM) problem within the extended Kalman filter (EKF) framework. Several problems never addressed before, arise while implementing the solution for indoor environments, and are successfully solved to obtain maps of quite large real indoor environments with more than one robot in real time.

Fast exact feature based data correspondence search with an efficient bit-parallel MCP solver

The problem of finding the optimal correspondence between two sets of geometric entities or features is known to be NP-hard in the worst case. This problem appears in many real scenarios such as fingerprint comparisons, image matching and global localization of mobile robots. The inherent complexity of the problem can be avoided by suboptimal solutions, but these could fail with high noise or corrupted data. The correspondence problem has an interesting equivalent formulation in finding a maximum clique in an association graph. We have developed a novel algorithm to solve the correspondence problem between two sets of features based on an efficient solution to the Maximum Clique Problem using bit parallelism. It outperforms an equivalent non bit parallel algorithm in a number of experiments with simulated and real data from two different correspondence problems. This article validates for the first time, to the best of our knowledge, that bit parallel optimization techniques can greatly reduce computational cost, thus making feasible the use of an exact solution in real correspondence search problems despite their inherent NP computational complexity.

Ten Years of Cybertech: The Educational Benefits of Bullfighting Robotics

After 10 years of organizing the Cybertech robotics competition, this paper presents this unique and innovative educational experience of teaching engineering at Universidad Politécnica de Madrid (UPM), Spain. Cybertech is not only a well-known robotic contest in Spain due to the Robotaurus bullfighting, but is also a whole academic activity spanning theory, laboratory practical lessons, seminars, tutoring, and a spectacular contest in which robots, developed by the students, compete. It is an open activity, for all students and grades, requiring knowledge of various subjects such as mechanics, microcontrollers, control, and electronics. The experience acquired has shown how this novel educational approach can boost the motivation of students, who in a real applied project effectively learn not only the particular subject matter, but also skills in teamwork, oral presentations, budget management, and so on. This is considered the flagship of innovation in education at UPM. This paper describes the evolution of Cybertech over the past 10 years, summarizes the educational experience, and provides some statistics and results as well as a perspective for future editions of the competition.

Urbano, an Interactive Mobile Tour-Guide Robot

Autonomous service robot applications can be divided in two main groups: outdoor and field robots, and indoor robots. Autonomous lawnmowers, de-mining and search and rescue robots, mars rovers, automated cargo, unmanned aerial and underwater vehicles, are some applications of field robotics. The term indoor robotics usually applies to autonomous mobile robots that move in a typical populated indoor environment. Robotic vacuum cleaners, entertainment and companion robots or security and surveillance applications are also some examples of successful indoor robot applications. Probably, one of the first real world applications of indoor service robots has been that of mobile robots serving as tour guides in museums or exhibitions. Such one is an extremely interesting application for researchers because allows them to advance in knowledge fields as autonomous navigation in dynamic environments, human robot interaction, indoor environment modelling with simultaneous localization and map building, etc., while also serving as a showcase for attracting the general public as well as possible investors. We have developed our own interactive mobile robot called Urbano, especially designed to be a tour guide in exhibitions. This chapter describes the Urbano robot system, its hardware, software and the experiences we have obtained through its development and use until its actual mature stage. This chapter doesn’t pretend to be an exhaustive technical description of algorithms, mathematical or implementation details, but just an overview of the system. The interested reader will be referred to more specific bibliography for these details. The rest of the chapter is structured as follows: This section presents the related work, other existing systems, as well as our motivation to develop our own robot. Section 2 presents an overview of Urbano, the description of its hardware and also the software components in which the robot control is structured. These components are afterwards described in subsequent sections: Section 3 describes the feature based mapping and navigation subsystem, while the interaction capabilities including our own proprietary voice recognition and synthesis engine will be described in section 4. Section 5 briefly describes the web based remote visit that Urbano is also able to perform. The integration of all these components is managed through a programmable kernel that allows a high level management of all modules, described in section 6. The chapter ends with the presentation of some successful real deployments of Urbano in section 7, and our conclusions in section 8.

BS-SLAM: Shaping the World

This paper presents BS-SLAM, a simultaneous localization and mapping algorithm for use in unstructured environments that is effective regardless of whether features correspond to simple geometric primitives such as lines or not. The coordinates of the control points defining a set of B-splines are used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman filter based SLAM algorithm. The proposed method is the first known EKF-SLAM implementation capable of describing both straight and curve features in a parametric way. Appropriate observation equation that allows the exploitation of virtually all observations from a range sensor such as the ubiquitous laser range finder is developed. Efficient strategies for computing the relevant Jacobians, perform data association, initialization and expanding the map are presented. The effectiveness of the algorithms is demonstrated using experimental data.