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

Journal of Intelligent and Robotic Systems (Impact Factor: 0.83). 01/2004; 40:233-265. DOI: 10.1023/B:JINT.0000038952.66083.d1
Source: DBLP

ABSTRACT 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.

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    ABSTRACT: The framework of this paper is robot localization inside buildings by means of wireless localization systems. Such kind of systems make use of the Wireless Fidelity (WiFi) signal strength sensors which are becoming more and more useful in the localization stage of several robotic platforms. Robot localization is usually made up of two phases: training and estimation stages. In the former, WiFi signal strength of all visible Access Points (APs) are collected and stored in a database or WiFi map. In the latter, the signal strengths received from all APs at a certain position are compared with the WiFi map to estimate the robot location. Hence, WiFi localization systems exploit the well-known path loss propagation model due to large-scale variations of WiFi signal to determine how closer the robot is to a certain AP. Unfortunately, there is another kind of signal variations called small-scale variations that have to be considered. They appear when robots move under the wavelength. In consequence, a chaotic noise is added to the signal strength measure yielding a lot of uncertainty that should be handled by the localization model. While lateral and orientation errors in the robot positioning stage are well studied and they remain under control thanks to the use of robust low-level controllers, more studies are needed when dealing with small-scale variations. Moreover, if the robot can not use a robust low-level controller because, for example, the environment is not organized in perpendicular corridors, then lateral and orientation errors can be significantly increased yielding a bad global localization and navigation performance. The main goal of this work is to strengthen the localization stage of our previous WiFi Partially Observable Markov Decision Process (POMDP) Navigation System with the aim of dealing effectively with small-scale variations. In addition, looking for the applicability of our system to a wider variety of environments, we relax the necessity of having a robust low-level controller. To do that, this paper proposes the use of a Soft Computing based system to tackle with the uncertainty related to both the small-scale variations and the lack of a robust low-level controller. The proposed system is actually implemented in the form of a Fuzzy Rule-based System and it has been evaluated in two real test-beds and robotic platforms. Experimental results show how our system is easily adaptable to new environments where classical localization techniques can not be applied since the AP physical location is unknown.
    Appl. Soft Comput. 01/2011; 11:4677-4691.
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    01/2011; , ISBN: 978-953-307-425-2

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