Prof. Dr. Juan-Antonio Fernández-Madrigal

This paper introduces a new approach to simultaneous localization and mapping (SLAM) that pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation that can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid discrete-continuous state space, which naturally combines metric and topological maps. There are two fundamental characteristics that set this paper apart from previous ones: 1) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem and 2) the analytical formulation of belief distributions over hybrid maps, which allows us to maintain the spatial uncertainty in large spaces more accurately and efficiently than in previous works. We also describe a practical implementation that aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30 000 m², a 2 km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches.

1 November 2012 in Computers & Education

Journal Article

A LEGO Mindstorms NXT approach for teaching at Data Acquisition, Control Systems Engineering and Real-Time Systems undergraduate courses

Ana Cruz-Martín

Juan-Antonio Fernández-Madrigal

C. Galindo

Javier Gonzalez-Jimenez

C. Stockmans-Daou

Jose Luis Blanco-Claraco

1 January 2008 in The International Journal of Robotics Research

Journal Article

A Novel Measure of Uncertainty for Mobile Robot SLAM with Rao—Blackwellized Particle Filters

Jose Luis Blanco-Claraco

Juan-Antonio Fernández-Madrigal

Javier Gonzalez-Jimenez

Rao—Blackwellized particle filters (RBPFs) are an implementation of sequential Bayesian filtering that has been successfully applied to mobile robot simultaneous localization and mapping (SLAM) and exploration. Measuring the uncertainty of the distribution estimated by a RBPF is required for tasks such as information gain-guided exploration or detecting loop closures in nested loop environments. In this paper we propose a new measure that takes the uncertainty in both the robot path and the map into account. Our approach relies on the entropy of the expected map (EM) of the RBPF, a new variable built by integrating the map hypotheses from all of the particles. Unlike previous works that use the joint entropy of the RBPF for active exploration, our proposal is better suited to detect opportunities to close loops, a key aspect to reduce the robot path uncertainty and consequently to improve the quality of the maps being built. We provide a theoretical discussion and experimental results with real data that support our claims.

18 September 2006 in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

Journal Article

Control Architecture for Human–Robot Integration: Application to a Robotic Wheelchair

Cipriano Galindo

J. Gonzalez

Juan-Antonio Fernández-Madrigal

Completely autonomous performance of a mobile robot within noncontrolled and dynamic environments is not possible yet due to different reasons including environment uncertainty, sensor/software robustness, limited robotic abilities, etc. But in assistant applications in which a human is always present, she/he can make up for the lack of robot autonomy by helping it when needed. In this paper, the authors propose human-robot integration as a mechanism to augment/improve the robot autonomy in daily scenarios. Through the human-robot-integration concept, the authors take a further step in the typical human-robot relation, since they consider her/him as a constituent part of the human-robot system, which takes full advantage of the sum of their abilities. In order to materialize this human integration into the system, they present a control architecture, called architecture for human-robot integration, which enables her/him from a high decisional level, i.e., deliberating a plan, to a physical low level, i.e., opening a door. The presented control architecture has been implemented to test the human-robot integration on a real robotic application. In particular, several real experiences have been conducted on a robotic wheelchair aimed to provide mobility to elderly people

About Me

Born in Córdoba (Spain), 1970, I started my academic career at the University of Málaga in 1996, which was after obtaining my MSc degree in Computer Science (1994) and holding a short position as a real-state developer. I got a pre-doctoral grant by the Spanish government for the time interval [1996,1999], during which I worked on my thesis. In the summer of 1998, I did a stay under the supervision of Prof. Ben J. Kuipers in the Computer Science Dpt. of the University of Texas at Austin (US). I obtained the PhD degree in Computer Science in 2000, an assistant professor position a few months after that, and a tenure as associate professor three years later. In october 2016, I got a full professor position at the University of Málaga. During those years, I taught Real-Time Robotics and Control Systems Engineering, developing robotic software and exploring a number of lines of research mostly related to intelligent robotics (lately resuming my previous Cognitive Robotics work thanks to a stay in the CRNS, UK). I have supervised several PhD theses on Robotics; have participated in regional, national, and European research projects; and have a bunch of patents. I have at present around 110 scientific papers, three international books, and have acted as the principal researcher in some projects. My current personal interests include writing (10 sci-fi books and more than 100 short stories published in Spanish), programming, drawing, and building robots.

Prof. Dr. Juan-Antonio Fernández-Madrigal

Prof. Dr. Juan-Antonio Fernández-Madrigal

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