Context Classifier for Service Robots

In this paper a context classifier for service robots is presented. Independently of the application, service robots need to have the notion of their context in order to behave appropriately. A context classification architecture that can be integrated in service robots reliability calculation is proposed. Sensorial information is used as input. This information is then fused (using Fuzzy Sets) in order to create a knowledge base that is used as an input to the classifier. The classification technique used is Bayes Networks, as the object of classification is partially observable, stochastic and has a sequential activity. Although the results presented refer to indoor/outdoor classification, the architecture is scalable in order to be used in much wider and detailed context classification. A community of service robots, contributing with their own contextual experience to dynamically improve the classification architecture, can use cloud-based technologies.

[1]  Javier V. Gómez,et al.  Indoor Furniture and Room Recognition for a Robot Using Internet-Derived Models and Object Context , 2012, 2012 10th International Conference on Frontiers of Information Technology.

[2]  Craig Schlenoff,et al.  A robot ontology for urban search and rescue , 2005, KRAS '05.

[3]  Mo Li,et al.  IODetector: a generic service for indoor outdoor detection , 2012, SenSys '12.

[4]  Mohamed Medhat Gaber,et al.  Reasoning about Context in Uncertain Pervasive Computing Environments , 2008, EuroSSC.

[5]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[6]  Sea Ling,et al.  Improving situation awareness for intelligent on-board vehicle management system using context middleware , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[7]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[8]  Il Hong Suh,et al.  Bayesian robot localization using spatial object contexts , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Pedro A. C. Sousa,et al.  Review on Context Classification in Robotics , 2014, RSEISP.

[10]  Xiaofeng Meng,et al.  A Quadratic Nonlinear Prediction-Based Heart Motion Model following Control Algorithm in Robotic-Assisted Beating Heart Surgery , 2013 .

[11]  Dennis Nienhuser,et al.  A Situation context aware Dempster-Shafer fusion of digital maps and a road sign recognition system , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[12]  Li-Chen Fu,et al.  Context-aware assisted interactive robotic walker for Parkinson's disease patients , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Manuela Veloso,et al.  Heterogeneous Context-Aware Robots Providing a Personalized Building Tour , 2013 .

[14]  Jadwiga Indulska,et al.  A survey of context modelling and reasoning techniques , 2010, Pervasive Mob. Comput..

[15]  Fulvio Mastrogiovanni,et al.  Context assessment strategies for Ubiquitous Robots , 2009, 2009 IEEE International Conference on Robotics and Automation.

[16]  Gabriele Trovato,et al.  Generation of humanoid Robot's Facial Expressions for Context-Aware Communication , 2013, Int. J. Humanoid Robotics.

[17]  Pieter Abbeel,et al.  Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor , 2014 .

[18]  Il Hong Suh,et al.  Ontology Modeling and Storage System for Robot Context Understanding , 2005, KES.

[19]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[20]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.