The complexity of Markov decision processes
Mathematics of Operations Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Xavier: a robot navigation architecture based on partially observable Markov decision process models
Artificial intelligence and mobile robots
The saphira architecture for autonomous mobile robots
Artificial intelligence and mobile robots
Integrating topological and metroc maps for mobile robot navigation: a statistical approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
An open agent architecture for assisting elder independence
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Journal of Intelligent and Robotic Systems
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Experiences with a mobile robotic guide for the elderly
Eighteenth national conference on Artificial intelligence
Adaptive Probabilistic Networks
Adaptive Probabilistic Networks
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A Navigation System for Assistant Robots Using Visually Augmented POMDPs
Autonomous Robots
Low level controller for a POMDP based on WiFi observations
Robotics and Autonomous Systems
WiFi localization system based on fuzzy logic to deal with signal variations
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
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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.