María Soledad Escudero

A Navigation System for Assistant Robots Using Visually Augmented POMDPs

Assistant robots have received special attention from the research community in the last years. One of the main applications of these robots is to perform care tasks in indoor environments such as houses, nursing homes or hospitals, and therefore they need to be able to navigate robustly for long periods of time. This paper focuses on the navigation system of SIRA, a robotic assistant for elderly and/or blind people based on a Partially Observable Markov Decision Process (POMDP) to global localize the robot and to direct its goal-oriented actions. The main novel feature of our approach is that it combines sonar and visual information in a natural way to produce state transitions and observations in the framework of Markov Decision Processes. Besides this multisensorial fusion, a two-level layered planning architecture that combines several planning objectives (such as guiding to a goal room and reducing locational uncertainty) improves the robustness of the navigation system, as it’s shown in our experiments with SIRA navigating corridors.

A Human–Robot Cooperative Learning System for Easy Installation of Assistant Robots in New Working Environments

One of the applications of service robots with a greater social impact is the assistance to elderly or disabled people. In these applications, assistant robots must robustly navigate in structured indoor environments such as hospitals, nursing homes or houses, heading from room to room to carry out different nursing or service tasks. Among the main requirements of these robotic aids, one that will determine its future commercial feasibility, is the easy installation of the robot in new working domains without long, tedious or complex configuration steps. This paper describes the navigation system of the assistant robot called SIRA, developed in the Electronics Department of the University of Alcalá, focusing on the learning module, specially designed to make the installation of the robot easier and faster in new environments. To cope with robustness and reliability requirements, the navigation system uses probabilistic reasoning (POMDPs) to globally localize the robot and to direct its goal-oriented actions. The proposed learning module fast learns the Markov model of a new environment by means of an exploration stage that takes advantage of human–robot interfaces (basically speech) and user–robot cooperation to accelerate model acquisition. The proposed learning method, based on a modification of the EM algorithm, is able to robustly explore new environments with a low number of corridor traversals, as shown in some experiments carried out with SIRA.

Detecting faces and recognizing facial features using color segmentation and 2DPCA in the normalized RG space

Face detection and recognition is very challenging due to the diverse variation of face appearance, facial expressions, variable recording conditions (changes in illumination, scale differences, varying face position...) and the complexity of image background. In this paper, we propose a new system which integrate color segmentation and two dimensional principal component analysis in the normalized RG space (2DPCA, to compress the red information as well as the green information) in order to detect faces and recognize facial features in color images that have not been preprocessed. We show some experimental results, using our own face database and the AR and PICS face databases. Then, we have compared results obtained with 2DPCA technique in the normalized RG space and other typical methods (2DPCA in the gray level space, PCA, Fisherfaces, Kernel PCA and Kernel Fisherfaces). Conclusions and future works have finally presented.

A planning architecture for topological robot navigation in uncertain domains

This paper presents a new navigation architecture for autonomous mobile robots working in uncertain domains. Partially Observable Markov Decision Processes (POMDPs) are suitable mathematical models for solving localization, planning and learning problems in uncertain navigation systems based on a topological representation of the environment. This paper focuses on the planning module, consisting of a two-level layered architecture (a local policy and a global policy) that simplifies the problem of finding optimal policies in POMDPs. The proposed system naturally integrates several planning objectives, such as guiding to a goal room, reducing location uncertainty, and exploring. Some experimental results are shown, carried out with an assistant robot developed in the Electronics Department of the University of Alcala.