Multi-layer situation model for robot integration systems

This paper discusses about the Multi-Layer Situation Map to estimate the relationship between the objects and the storage places in cleanup task. In recent years, a service robot has to estimate, decide and act automatically in various situations to provide services to human beings. Now, we are developing a robot integration system that estimates the storage place where a robot should carry the objects. An interaction robot talks a cleanup service, if a storage place is decided only one place. On the other hand, an interaction robot has to communicate with human about cleanup service in fuzzy situations. For an interaction robot talks suitable contents according to the fuzzy situations, we propose an estimation system which is called a Multi-Layer Situation Map, and express the relationship between the object and the storage place. The multi-layer situation map can move each component according to multi-layer spring model when an environmental situation is changed. We verify the multi-layer situation map is able to express the relationship of the components in the cleanup service situation.

[1]  Takenori Obo,et al.  Localization of human based on fuzzy spiking neural network in informationally structured space , 2010, International Conference on Fuzzy Systems.

[2]  Takashi Suehiro,et al.  RT-middleware: distributed component middleware for RT (robot technology) , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Toshio Fukuda,et al.  An intelligent robotic system based on a fuzzy approach , 1999, Proc. IEEE.

[4]  Pavol Návrat,et al.  Multiple developing news stories identified and tracked by social insects and visualized using the new galactic streams and concurrent streams metaphors , 2015, Int. J. Hybrid Intell. Syst..

[5]  K. Thammi Reddy,et al.  An Efficient Semantic Ranked Keyword Search of Big Data Using Map Reduce , 2015 .

[6]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..

[7]  Hun-ok Lim,et al.  Self-Organized Map Based Learning System for Estimating the Specific Task by Simple Instructions , 2013, J. Adv. Comput. Intell. Intell. Informatics.

[8]  David Lane.  Web Database Applications with PHP and MySQL , 2002 .

[9]  L. A. Zadeh,et al.  Fuzzy logic and approximate reasoning , 1975, Synthese.

[10]  Yasufumi Takama,et al.  Interactive Visualization System for Decision Making Support in Online Shopping , 2008, PAKDD Workshops.

[11]  Tomoo Inoue,et al.  Tabletop dish recommendation system for social dining: Group FDT design based on the investigations of dish recommendation , 2013, J. Inf. Process..

[12]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[13]  Yasufumi Takama,et al.  Visualization of News Distribution in Blog Space , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops.

[14]  T. Hasegawa,et al.  A structured environment with sensor networks for intelligent robots , 2008, 2008 IEEE Sensors.

[15]  Dejan Pangercic,et al.  Robotic roommates making pancakes , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[16]  Horst-Michael Groß,et al.  I'll keep an eye on you: Home robot companion for elderly people with cognitive impairment , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.