此页面上的内容需要较新版本的 Adobe Flash Player。

获取 Adobe Flash Player

Current Status of Intelligent Space

 

Hideki Hashimoto, Takeshi Sasaki, Laszlo Attila Jeni

 

 

Institute of Industrial Science, University of Tokyo, Tokyo 153- 8505, Japan

 

 

Abstract-Latest advances in network sensor technology and sta te of the art of mobile robotics and artificial intelligence research can be app lied to develop autonomous and distributed monitoring systems. Intelligent Space  (iSpace) is an environmental system, which is able to support human in informat ive and physical ways. iSpace observing the space with distributed sensors, extr acts useful information from the obtained data and provides various services to  users. This means that essential functions of iSpace are “observation”, “reco gnition” and “actuation.” In this paper, we focus on the observation function  of iSpace. And we describe observation systems to get information of both huma n and mobile agents in the space to show new results.

 

Key words-current status; intelligent space; network se nsor technology

 

Manuscript Number: 1674-8042(2010)01-0086-07

 

dio: 10.3969/j.issn.1674-8042.2010.01.18

 

References

 

[1] H.H.Lee, H.Hashimoto, 2002. Intelligent space-concept and contents . Advanced Robotics, 16(3): 265-280.

[2] J.H.Lee, H.Hashimoto, 2003. Controlling mobile robots in distribute d intelligent sensor network. IEEE Trans. on Industrial Electronics, 50(5): 890-902.

[3] M.Niitsuma, H.Hashimoto, 2009. Observation of human activities base d on spatial memory in intelligent space. Journal of Robotics and Mech atronics, 21(4): 515-523.

[4] K.Kawaji, K.Yokoi, M.Niitsuma, H.Hashimoto, 2008. Observation Syste m of Human-Object Relations in Intelligent Space. 6th IEEE Int. Conf. on Indust rial Informatics, p.1475-1480.

[5] M.Niitsuma, K.Yokoi, H.Hashimoto, 2009. Describing Human-Object In t eraction in Intelligent Space. 2nd Int. Conf. on Human System Interaction, p.395 -399.

[6] Xsens. MTx. http://www.xsens.com/

[7] L.Palafox, H.Hashimoto, 2010. A Compressive Sensing Approach to the   4W1H Architecture. IEEE-ICIT 2010 Int. Conf. on Industrial Technology, p.1579- 1583.

[8] L.Palafox, H.Hashimoto, 2009. A Movement Profile Detection System U s ing Self Organized Maps in the Intelligent Space. IEEE Workshop on Advanced Robo tics and its Social Impacts.

[9] P.Ekman,  E.L.Rosenberg, 2005. What the Face Reveals: Basic and App l ied Studies of Spontaneous Expression Using the Facial Action Coding System (2nd  ed.). Oxford University Press, New York.

[10] L.A.Jeni, Gy.Florea, A.Lorincz, 2008. InfoMax bayesian learning of  the furuta pendulum. Acta Cybernetica, 18: 637-649.

[11] S.Srkk, A.Vehtari, J.Lampinen, 2007. Rao-blackwelli z ed particle filter for multiple target tracking. Information Fusion Jo urnal, 8(1): 2-15.

[12] R.Kurazume, H.Yamada, K.Murakami, Y.Iwashita, T.Hasegawa, 2008. Ta r get Tracking Using SIR and MCMC Particle Filters by Multiple Cameras and Laser R ange Finders. 2008 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.3838 -3844.

[13] K.Nakamura, H.Zhao, R.Shibasaki, K.Sakamoto, T.Ohga, N.Suzukawa, 2 006. Tracking pedestrians using multiple single-row laser range scanners and it s reliability evaluation. Systems and Computers in Japan, 37( 7): 1-11.

[14] X.Shao, H.Zhao, K.Nakamura, K.Katabira, R.Shibasaki, Y.Nakagawa, 2 0 07. Detection and Tracking of Multiple Pedestrians by Using Laser Range Scanners . 2007 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.2174-2179.

[15] H.Tamura, T.Sasaki, H.Hashimoto, F.Inoue, 2010. Circle Fitting Bas e d Position Measurement System Using Laser Range Finder in Construction Fields. 2 010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems.

[16] D.Brscic, H.Hashimoto, 2008. Comparison of Robot Localization Meth o ds Using Distributed and Onboard Laser Range Finders.IEEE/ASME Int. Conf. on Adv anced Intelligent Mechatronics, p.746-751.

 

 [full text view]