Development of the multi-functional indoor service robot PSR systems

This paper discusses the development of the multi-functional indoor service robot PSR (Public Service Robots) systems. We have built three versions of PSR systems, which are the mobile manipulator PSR-1 and PSR-2, and the guide robot Jinny. The PSR robots successfully accomplished four target service tasks including a delivery, a patrol, a guide, and a floor cleaning task. These applications were defined from our investigation on service requirements of various indoor public environments. This paper shows how mobile-manipulator typed service robots were developed towards intelligent agents in a real environment. We identified system integration, multi-functionality, and autonomy considering environmental uncertainties as key research issues. Our research focused on solving these issues, and the solutions can be considered as the distinct features of our systems. Several key technologies were developed to satisfy technological consistency through the proposed integration scheme. Woojin Chung was born in Seoul, Korea, in 1970. He received the B.S. at the department of mechanical design and production engineering, Seoul National University in 1993. He received the M.S. degree in 1995 and Ph.D degree in 1998 at the department of Mechano-Informatics, the University of Tokyo. He was a senior research scientist at the Korea Institute of Science and Technology from 1998 to 2005. He joined the department of mechanical engineering, Korea University in 2005 as an assistant professor. He received an excellent paper award from the Robotics Society of Japan in 1996 and a best transactions paper award from the IEEE robotics and automation society in 2002. His research interests include the design and control of nonholonomic underactuated mechanical systems, trailer system design and control, mobile robot navigation, a dexterous robot hand and a system integration of intelligent robots. He is a member of the IEEE, the robotics society of Japan, the institute of control, automation and systems engineers and the Korea robotics society. Gunhee Kim received the B.S. and M.S. degrees at the department of mechanical engineering, Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1999 and 2001, respectively. He was a research scientist in Intelligent Robotics Research Center, at Korea Institute of Science and Technology (KIST), Korea, from 2001 to 2006. Currently, he is a graduate student in the Robotics Institute, Carnegie Mellon University. His research interests include computer vision, artificial intelligence, mobile robot navigation, and discrete event systems. He is a member of the IEEE. Munsang Kim received the B.S. and M.S degree in Mechanical Engineering from the Seoul National University in 1980 and 1982 respectively and the Dr.-Ing. degree in Robotics from the Technical University of Berlin, Germany in 1987. Since 1987 he has been working as a research scientist at Korea Institute of Science and Technology. He has led the Intelligent Robotics Research Center since 2000 and became the director of the “Intelligent Robot—The Frontier 21 Program” since Oct. 2003. His current research interests are design and control of novel mobile manipulation systems, haptic device design and control, and sensor application to intelligent robots.     

Central pattern generator parameter search for a biped walking robot using nonparametric estimation based Particle Swarm Optimization

A parameter search for a Central Pattern Generator (CPG) for biped walking is difficult because there is no methodology to set the parameters and the search space is broad. These characteristics of the parameter search result in numerous fitness evaluations. In this paper, nonparametric estimation based Particle Swarm Optimization (NEPSO) is suggested to effectively search the parameters of CPG. The NEPSO uses a concept experience repository to store a previous position and the fitness of particles in a PSO and estimated best position to accelerate a convergence speed. The proposed method is compared with PSO variants in numerical experiments and is tested in a three dimensional dynamic simulator for bipedal walking. The NEPSO effectively finds CPG parameters that produce a gait of a biped robot. Moreover, NEPSO has a fast convergence property which reduces the evaluation of fitness in a real environment. Recommended by Editorial Board member Euntai Kim under the direction of Editor Jae-Bok Song. Jeong-Jung Kim received the B.S. degree in Electronics and Information Engineering from Chonbuk National University in 2006 and the M.S. degree in Robotics from Korea Advanced Institute of Science and Technology in 2008. He is currently working toward a Ph.D. at the Korea Advanced Institute of Science and Technology. His research interests include biologically inspired robotics and machine learning. Jun-Woo Lee received the B.S. degree in Electronics, Electrical and Communication Engineering from Pusan National University in 2007. He is currently working toward an M.S. in the Korea Advanced Institute of Science and Technology. His research interests include swarm intelligence and machine learning. Ju-Jang Lee was born in Seoul, Korea, in 1948. He received the B.S. and M.S. degrees from Seoul National University, Seoul, Korea, in 1973 and 1977, respectively, and the Ph.D. degree in Electrical Engineering from the University of Wisconsin, in 1984. From 1977 to 1978, he was a Research Engineer at the Korean Electric Research and Testing Institute, Seoul. From 1978 to 1979, he was a Design and Processing Engineer at G. T. E. Automatic Electric Company, Waukesha, WI. For a brief period in 1983, he was the Project Engineer for the Research and Development Department of the Wisconsin Electric Power Company, Milwaukee. He joined the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, in 1984, where he is currently a Professor. In 1987, he was a Visiting Professor at the Robotics Laboratory of the Imperial College Science and Technology, London, U.K. From 1991 to 1992, he was a Visiting Scientist at the Robotics Department of Carnegie Mellon University, Pittsburgh, PA. His research interests are in the areas of intelligent control of mobile robots, service robotics for the disabled, space robotics, evolutionary computation, variable structure control, chaotic control systems, electronic control units for automobiles, and power system stabilizers. Dr. Lee is a member of the IEEE Robotics and Automation Society, the IEEE Evolutionary Computation Society, the IEEE Industrial Electronics Society, IEEK, KITE, and KISS. He is also a former President of ICROS in Korea and a Counselor of SICE in Japan. He is a Fellow of SICE and ICROS. He is an Associate Editor of IEEE Transactions on Industrial Electronics and IEEE Transactions on Industrial Informatics.     

Object-spatial layout-route based hybrid map and global localization for mobile robots

This paper presents new object-spatial layout-route based hybrid map representation and global localization approaches using a stereo camera. By representing objects as high-level features in a map, a robot can deal more effectively with different contexts such as dynamic environments, human-robot interaction, and semantic information. However, the use of objects alone for map representation has inherent problems. For example, it is difficult to represent empty spaces for robot navigation, and objects are limited to readily recognizable things. One way to overcome these problems is to develop a hybrid map that includes objects and the spatial layout of a local space. The map developed in this research has a hybrid structure that combines a global topological map and a local hybrid map. The topological map represents the spatial relationships between local spaces. The local hybrid map combines the spatial layout of the local space with the objects found in that space. Based on the proposed map, we suggest a novel coarse-to-fine global localization method that uses object recognition, point cloud fitting and probabilistic scan matching. This approach can accurately estimate robot pose with respect to the correct local space. Recommended by Editor Jae-Bok Song. This research was performed for the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. Soonyong Park received the B.S. and M.S. degrees from the Department of Mechanical Engineering, Kyunghee University, Seoul, Korea, in 2001 and 2003, respectively. He is currently working toward the Ph.D. degree in the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. Since 2001, he has been a student researcher in the Center for Cognitive Robotics Research, Korea Institute of Science and Technology (KIST), Seoul, Korea. His research interests include mobile robot navigation and computer vision. Mignon Park received the B.S. and M.S. degrees in Electronics from Yonsei University, Seoul, Korea, in 1973 and 1977, respectively. He received the Ph.D. degree in University of Tokyo, Japan, 1982. He was a researcher with the Institute of Biomedical Engineering, University of Tokyo, Japan, from 1972 to 1982, as well as at the Massachusetts Institute of Technology, Cambridge, and the University of California Berkeley, in 1982. He was a visiting researcher in Robotics Division, Mechanical Engineering Laboratory, Ministry of International Trade and Industry, Tsukuba, Japan, from 1986 to 1987. He has been a Professor in the Department of Electrical and Electronic Engineering in Yonsei University, since 1982. His research interests include fuzzy control and application, robotics, and fuzzy biomedical system. Sung-Kee Park is a principal research scientist for Korea Institute of Science and Technology (KIST). He received the B.S. and M.S. degrees in Mechanical Design and Production Engineering from Seoul National University, Seoul, Korea, in 1987 and 1989, respectively. He received the Ph.D. degree (2000) from Korea Advanced Institue of Science and Technology (KAIST), Korea, in the area of computer vision. Since then, he has been working for the center for cognitive robotics research at KIST. During his period at KIST, he held a visiting position at the Robotics Institute of Carnegie Mellon University in 2005, where he did research on object recognition. His recent work has been on cognitive visual processing, object recognition, visual navigation, and human-robot interaction.     

An enhanced force and contact position sensor for micro-manipulations

In micro-manipulations, force sensing devices play an important role in the control and the assembly of micro-objects. To protect these micro-objects from damage, we must have the ability to detect the value of the minute amount of interactive force (about a few μN) upon contact between the tip and the object. To detect this micro-force, we need an optimized design of force sensor to increase the strain values at the positions we place sensing components. Stress concentration can effectively amplify the strain values measured by the force sensors. This paper investigates the effect that the notches have on increasing the strain values at the positions we attach the sensing elements. In addition, the optimal design with a flexible structure improves the sensitivity of the sensor. An algorithm that can calculate both contact force and contact position on the sensor tip is also mentioned. Besides, an optimal location of strain gauges will ensure the accuracy and stability of the measurement. Finally, analysis and experiment are done to verify the proposed idea. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This research was supported by the Ministry of Knowledge Economy and Korean Industrial Technology Foundation through the Human Resource Training Project for Strategic Technology. Tri Cong Phung received the B.S. degree in Mechanical Engineering from the HCM University of Technology, Vietnam in 2004 and the M.S. degree in Mechanical Engineering from Sungkyunkwan University in 2007. He is currently working toward a Ph.D. degree in Intelligent Robotics and Mechatronic System Laboratory (IRMS Lab), Mechanical Engineering from Sungkyunkwan University. His research interests include dexterous manipulation and touch sensors. Seung Hwa Ha received the B.S. degree in Korean University of Technology and Education, Korea in 2004. He received the M.S. degree in Mechanical Engineering from Sungkyunkwan University in 2008. He is currently working in Samsung Electronic Co. Ltd. His research interests are about strain gauge and high precision control. Yong Seok Ihn received the B.S. degree in School of Mechanical Engineering from the Sungkyunkwan University, Korea in 2006. He received the M.S. degree in Mechanical Engineering from the Sungkyunkwan University, in 2008. He is currently working toward a Ph. D. degree in the Computer Aided Modeling & Simulation Laboratory (CAMAS Lab), School of Mechanical Engineering at the Sungkyunkwan University in Korea. His research interests are precision mechatronics, dynamic system modeling, and control. Byung June Choi received the B.S. degree in School of Mechanical Engineering from the Sungkyunkwan University, Korea in 2002. He received the M.S. degree in Mechanical Engineer-ing from the Sungkyunkwan University, in 2005. He is currently working toward a Ph.D. degree in the Intelligent Robotics and Mechatronic System Laboratory (IRMS Lab), School of Mechanical Engineering at the Sungkyunkwan University in Korea. His research interests are mechanisms design, multi-robot system control, cooperation, path planning and task allocation algorithm. Sang Moo Lee was born in Seoul, Korea and educated in Seoul. He received the Ph.D. degree from the Seoul National University in Korea, in 1999. He is currently a Principal Researcher of Division for Applied Robot Technology at Korean Institute of Industrial Technology. His research interests include high-precision robot control, motion field network, and location system in outdoor environment for robots. Ja Choon Koo is an Associate Professor of School of Mechanical Engineering in Sungkyunkwan University in Korea. His major researches are in the field of design, analysis, and control of dynamics systems, especially micro precision mechatronic systems and energy transducers. He was an Advisory Engineer for IBM, San Jose, California, USA and a Staff Engineer for SISA, San Jose, CA, USA. He received the Ph.D. and M.S. degrees from the University of Texas at Austin and the B.S. from Hanyang University, Seoul, Korea. Hyouk Ryeol Choi received the B.S. degree from Seoul National University, Seoul, Korea, in 1984, the M.S. degree from Korea Advanced Institute of Science and Technology (KAIST), Daejon, Korea, in 1986, and the Ph.D. degree from Pohang University of Science and Technology (POSTECH), Pohang, Korea, in 1994, all in Mechanical Engineering. From 1986 to 1989, he was an Associate Engineer at LG Electronics Central Research Laboratory, Seoul. From 1993 to 1995, he was at Kyoto University, Kyoto, Japan, as a Grantee of scholarship from the Japanese Educational Ministry. From 2000 to 2001, he visited Advanced Institute of Industrial Science Technology (AIST), Tsukuba, Japan, as a Japan Society for the Promotion of Sciences (JSPS) Fellow. Since 1995, he has been with Sungkyunkwan University, Suwon, Korea, where he is currently a Professor in the School of Mechanical Engineering. He is an Associate Editor of the Journal of Intelligent Service Robotics and International Journal of Control, Automation and Systems (IJCAS), and IEEE Transactions on Robotics. His current research interests include dexterous mechanism, field application of robots, and artificial muscle actuators.     

PAI: Automatic indexing for extracting asserted keywords from a document

This paper proposes an automatic indexing method named PAI (Priming Activation Indexing) that extracts keywords expressing the author’s main point from a document based on the priming effect. The basic idea is that since the author writes a document emphasizing his/her main point, impressive terms born in the mind of the reader could represent the asserted keywords. Our approach employs a spreading activation model without using corpus, thesaurus, syntactic analysis, dependency relations between terms or any other knowledge except for stop-word list. Experimental evaluations are reported by applying PAI to journal/conference papers. Naohiro Matsumura: He received his B.S. and M.S. in Engineering Science from Osaka University in 1998 and 2000. Currently, he is a Ph.D. candidate in Engineering at the University of Tokyo and a research staff of PRESTO of Japan Science and Technology Corporation (2000–). His research interests include chance discovery, computer-mediated communication, and user-oriented data mining/text mining. Yukio Ohsawa, Ph.D.: BS, U. Tokyo, 1990, MS, 1992, DS, 1995. Research associate Osaka U. (1995). Associate prof. Univ. of Tsukuba (1999–) and also researcher of Japan Science and Technology Corp (2000–). He has been working for the program com. of the Workshop on Multiagent and Cooperative Computation, Annual Conf. Japanese Soc. Artificial Intelligence, International Conf. MultiAgent Systems, Discovery Science, Pacific Asia Knowledge Discovery and Data Mining, International Conference on Web Intelligence, etc. He chaired the First International Workshop of Japanese Soc. on Artificial Intelligence, Chance Discovery International Workshop Series and the Fall Symposium on Chance Discovery from AAAI. Guest editor of Special Issues on Chance Discovery for the Journal of Contingencies and Crisis Management, Journal of Japan Society for Fuzzy Theory and intelligent informatics, regular member of editorial board for Japanese Society of Artificial Intelligence. Currently he is authoring book “Chance Discovery” from Springer Verlag, “Knowledge Managament” from Ohmsha etc. Mitsuru Ishizuka, Ph.D.: He is a professor at the Dept. of Infomation and Communication Eng., School of Information Science and Thechnology, the Univ. of Tokyo. Prior to this position, he worked at NTT Yokosuka Lab. and the Institute of Industrial Science, the Univ. of Tokyo. He earned his B.S., M.S. and Ph.D. in electronic engineering from the Univ. of Tokyo. His research interests include artificial intelligence, WWW intelligence, and multimodal lifelike agents. He is a member of IEEE, AAAI, IEICE Japan, IPS Japan, and Japanese Society for AI.     

Biologically inspired control of quadruped walking robot

In this paper, we present a control method for a quadruped walking robot inspired from the locomotion of quadrupeds. A simple and useful framework for controlling a quadruped walking robot is presented, which is obtained by observing the stimulus-reaction mechanism, the gravity load receptor and the manner of generating repetitive motions from quadrupeds. In addition, we propose a new rhythmic pattern generator that can relieve the large computational burden on solving the kinematics. The proposed method is tested via a dynamic simulation and validated by implementation in a quadruped walking robot, called AiDIN-I (Artificial Digitigrade for Natural Environment I). Recommended by Editorial Board member Sangdeok Park under the direction of Editor Jae-Bok Song. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2005-D00031). Ig Mo Koo received the B.S. degree in Mechanical Engineering from Myongji University, Yongin, Korea, in 2003, the M.S. degree in Mechanical Engineering from the Sungkyunkwan University, Suwon, Korea, in 2005, where he is currently working toward a Ph.D. degree in Mechanical Engineering from Sungkyunkwan University. His research interests include artificial muscle actuators, haptics, tactile display, biomimetics and quadruped walking robots systems. Tae Hun Kang received the B.S., M.S., and Ph.D. degrees in Mechanical Engineering from Sungkyunkwan University, Korea, in 2000, 2002, and 2006, respectively. His current research interests focus on biomimetics and quadruped walking robot. Gia Loc Vo received the B.S degree in Mechanical Engineering form Ha Noi University of Technology in Vietnam 2003, the M.S. degree Mechanical Engineering form Sungkyunkwan University, Suwon, Korea, in 2006, where he is currently working toward a Ph.D. degree in Mechanical Engineering from Sungkyunkwan University. His research interests include legged locomotion, walking and climbing robot. Tran Duc Trong received the B.S degree in Mechatronics from HoChiMinh City University of Technology in Vietnam in 2005, where he is currently working toward a M.S. degree in Mechanical Engineering from Sungkyunkwan University. His research interests include biological inspired control and adaptive control of quadruped walking robot. Young Kuk Song received the B.S. degree in Mechanical Engineering from Sungkyunkwan University, Suwon, Korea, in 2006, where he is currently working toward a M.S. degree in Mechanical Engineering from Sungkyunkwan University. His research interests include biomimetics, hydraulic robotics system and quadruped walking robot. Hyouk Ryeol Choi received the B.S. degree from Seoul National University, Seoul, Korea, in 1984, the M.S. degree from the Korea Advanced Technology of Science and Technology (KAIST), Daejeon, Korea, in 1986, and the Ph.D. degree from the Pohang University of Science and Technology (POSTECH), Pohang, Korea, in 1994. Since 1995, he has been with Sungkyunkwan University, Suwon, Korea, where he is currently a Professor in the School of Mechanical Engineering. He was an Associate Engineer with LG Electronics Central Research Laboratory, Seoul, Korea, from 1986 to 1989. From 1993 to 1995, he was with Kyoto University, Kyoto, Japan, as a grantee of scholarship funds from the Japanese Educational Administry. He visited the Advanced Institute of Industrial Science Technology (AIST), Tsukuba, Japan, as a JSPS Fellow from 1999 to 2000. He is now an Associate Editor in IEEE Transactions on Robotics, Journal of Intelligent Service Robotics, International Journal of Control, Automation and Systems (IJCAS). His interests includes dexterous mechanisms, field application of robots, and artificial muscle actua tors.     

Web information visualization method employing immune network model for finding topic stream from document-set sequence

A Web information visualization method based on the document set-wise processing is proposed to find the topic stream from a sequence of document sets. Although the hugeness as well as its dynamic nature of the Web is burden for the users, it will also bring them a chance for business and research if they can notice the trends or movement of the real world from the Web. A sequence of document sets found on the Web, such as online news article sets is focused on in this paper. The proposed method employs the immune network model, in which the property of memory cell is used to find the topical relation among document sets. After several types of memory cell models are proposed and evaluated, the experimental results show that the proposed method with memory cell can find more topic streams than that without memory cell. Yasufumi Takama, D.Eng.: He received his B.S., M.S. and Dr.Eng. degrees from the University of Tokyo in 1994, 1996, and 1999, respectively. From 1999 to 2002 he was with Tokyo Institute of Technology, Japan. Since 2002, he has been Associate Professor of Department of Electronic Systems and Engineering, Tokyo Metropolitan Institute of Technology, Tokyo, Japan. He has also been participating in JST (Japan Science and Technology Corporation) since October 2000. His current research interests include artificial intelligence, Web information retrieval and visualization systems, and artificial immune systems. He is a member of JSAI (Japanese Society of Artificial Intelligence), IPS J (Information Processing Society of Japan), and SOFT (Japan Society for Fuzzy Theory and Systems). Kaoru Hirota, D.Eng.: He received his B.E., M.E. and Dr.Eng. degrees in electronics from Tokyo Institute of Technology, Tokyo, Japan, in 1974, 1976, and 1979, respectively. From 1979 to 1982 and from 1982 to 1995 he was with the Sagami Institute of Technology and Hosei University, respectively. Since 1995, he has been with the Interdisciplinary Graduate School of Science and Technology, Tokyo Institute of Technology, Yokohama, Japan. He is now a department head professor of Department of Computational Intelligence and Systems Science. Dr.Hirota is a member of IFSA (International Fuzzy Systems Association (Vice President 1991–1993), Treasurer 1997–2001), IEEE (Associate Editors of IEEE Transactions on Fuzzy Systems (1993–1995) and IEEE Transactions on Industrial Electronics (1996–2000)) and SOFT (Japan Society for Fuzzy Theory and Systems (Vice President 1995–1997, President 2001–2003)), and he is an editor in chief of Int. J. of Advanced Computational Intelligence.     

In-pipe robot based on selective drive mechanism

This paper presents an in-pipe robot, called MRINSPECT V (Multifunctional Robotic crawler for In-pipe inSPECTion V), which is under development for the inspection of pipelines with a nominal 8-inch inside diameter. To travel freely in every pipeline element, the robot adopts a differential driving mechanism that we have developed. Furthermore, by introducing clutches in transmitting driving power to the wheels, MRINSPECT V is able to select the suitable driving method according to the shape of the pipeline and save the energy to drive in pipelines. In this paper, the critical points in the design and construction of the proposed robot are described with the preliminary results that yield good mobility and increased efficiency. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This work was supported by the Postdoctoral Research Program of Sungkyunkwan University (2008). Se-gon Roh received the B.S., M.S., and Ph.D degrees in Mechatronics Engineering from Sungkyunkwan University, Korea, in 1997, 1999, and 2006 respectively, and is currently a Researcher of the School of Mechanical Engineering also at Sungkyunkwan University. His research interests include mechanism design, applications of mobile robots, and in-pipe robots. Do Wan Kim received the B.S. degree in Mechanical Engineering from Sungkyunkwan University, Korea, in 2007. He is currently working toward a M.S. degree in Mechanical Engineering also at Sungkyunkwan University. His research interests include field robotics, in-pipe robots, and autonomous mobile robots. Jung-Sub Lee received the B.S. degree in Mechanical Engineering in 2008 from Sungkyunkwan University, Suwon, Korea, where he is currently working toward a M.S. degree in mechatronics engineering. His research interests include robot mechanism design, automation, and in-pipe robot. Hyungpil Moon received the B.S. and M.S. degrees in Mechanical Engineering from POSTECH in 1996 and 1998 respectively, and Ph.D. degree in Mechanical Engineering from University of Michigan in 2005. He joined the faculty of School of Mechanical Engineering in Sungkyunkwan University as a Full-time Lecturer in 2008. He was a Post-doctoral fellow at Carnegie Mellon University, Robotics Institute until November 2007. His research interests include distributed manipulation, multiple robot navigation, SLAM, and biomimetic robotics. Hyouk Ryeol Choi received the B.S. degree from Seoul National University in 1984, the M.S. degree from Korea Advanced Technology of Science and Technology (KAIST) in 1986, and the Ph.D. degree from Pohang University of Science and Technology (POSTECH) in 1994, Korea. Since 1995, he has been with Sungkyunkwan University, where he is currently a Professor of the School of Mechanical Engineering. He worked as an Associate Engineer with LG Electronics Central Research Laboratory from 1986 to 1989. From 1993 to 1995, he was with Kyoto University as a grantee of a scholarship from the Japanese Educational Ministry. He visited Advanced Institute of Industrial Science Technology (AIST), Japan as the JSPS Fellow, from 1999 to 2000. He is now an Associate Editor of IEEE Transactions on Robotics, International Journal of Control, System, Automation(IJCAS), and International Journal of Intelligent Service Robots (JISR). His interests include dexterous mechanisms, field applications of robots, and artificial muscle actuator.     

An algorithm for similar utterance section extraction for managing spoken documents

This paper proposes a new, efficient algorithm for extracting similar sections between two time sequence data sets. The algorithm, called Relay Continuous Dynamic Programming (Relay CDP), realizes fast matching between arbitrary sections in the reference pattern and the input pattern and enables the extraction of similar sections in a frame synchronous manner. In addition, Relay CDP is extended to two types of applications that handle spoken documents. The first application is the extraction of repeated utterances in a presentation or a news speech because repeated utterances are assumed to be important parts of the speech. These repeated utterances can be regarded as labels for information retrieval. The second application is flexible spoken document retrieval. A phonetic model is introduced to cope with the speech of different speakers. The new algorithm allows a user to query by natural utterance and searches spoken documents for any partial matches to the query utterance. We present herein a detailed explanation of Relay CDP and the experimental results for the extraction of similar sections and report results for two applications using Relay CDP. Yoshiaki Itoh has been an associate professor in the Faculty of Software and Information Science at Iwate Prefectural University, Iwate, Japan, since 2001. He received the B.E. degree, M.E. degree, and Dr. Eng. from Tokyo University, Tokyo, in 1987, 1989, and 1999, respectively. From 1989 to 2001 he was a researcher and a staff member of Kawasaki Steel Corporation, Tokyo and Okayama. From 1992 to 1994 he transferred as a researcher to Real World Computing Partnership, Tsukuba, Japan. Dr. Itoh's research interests include spoken document processing without recognition, audio and video retrieval, and real-time human communication systems. He is a member of ISCA, Acoustical Society of Japan, Institute of Electronics, Information and Communication Engineers, Information Processing Society of Japan, and Japan Society of Artificial Intelligence. Kazuyo Tanaka has been a professor at the University of Tsukuba, Tsukuba, Japan, since 2002. He received the B.E. degree from Yokohama National University, Yokohama, Japan, in 1970, and the Dr. Eng. degree from Tohoku University, Sendai, Japan, in 1984. From 1971 to 2002 he was research officer of Electrotechnical Laboratory (ETL), Tsukuba, Japan, and the National Institute of Advanced Science and Technology (AIST), Tsukuba, Japan, where he was working on speech analysis, synthesis, recognition, and understanding, and also served as chief of the speech processing section. His current interests include digital signal processing, spoken document processing, and human information processing. He is a member of IEEE, ISCA, Acoustical Society of Japan, Institute of Electronics, Information and Communication Engineers, and Japan Society of Artificial Intelligence. Shi-Wook Lee received the B.E. degree and M.E. degree from Yeungnam University, Korea and Ph.D. degree from the University of Tokyo in 1995, 1997, and 2001, respectively. Since 2001 he has been working in the Research Group of Speech and Auditory Signal Processing, the National Institute of Advanced Science and Technology (AIST), Tsukuba, Japan, as a postdoctoral fellow. His research interests include spoken document processing, speech recognition, and understanding.     

A selective attention-based contextual perception approach for a humanoid robot

A humanoid robot is always flooded by sensed information when sensing the environment, and it usually needs significant time to compute and process the sensed information. In this paper, a selective attention-based contextual perception approach was proposed for humanoid robots to sense the environment with high efficiency. First, the connotation of attention window (AW) is extended to make a more general and abstract definition of AW, and its four kinds of operations and state transformations are also discussed. Second, the attention control policies are described, which integrate intensionguided perceptual objects selection and distractor inhibition, and can deal with emergent issues. Distractor inhibition is used to filter unrelated information. Last, attention policies are viewed as the robot’s perceptual modes, which can control and adjust the perception efficiency. The experimental results show that the presented approach can promote the perceptual efficiency significantly, and the perceptual cost can be effectively controlled through adopting different attention policies.     

A Sampling-Based Motion Planning Approach to Maintain Visibility of Unpredictable Targets

This paper deals with the surveillance problem of computing the motions of one or more robot observers in order to maintain visibility of one or several moving targets. The targets are assumed to move unpredictably, and the distribution of obstacles in the workspace is assumed to be known in advance. Our algorithm computes a motion strategy by maximizing the shortest distance to escape—the shortest distance the target must move to escape an observer's visibility region. Since this optimization problem is intractable, we use randomized methods to generate candidate surveillance paths for the observers. We have implemented our algorithms, and we provide experimental results using real mobile robots for the single target case, and simulation results for the case of two targets-two observers. Rafael Murrieta-Cid received the B.S degree in Physics Engineering (1990), and the M.Sc. degree in Automatic Manufacturing Systems (1993), both from “Instituto Tecnológico y de Estudios Superiores de Monterrey” (ITESM) Campus Monterrey. He received his Ph.D. from the “Institut National Polytechnique” (INP) of Toulouse, France (1998). His Ph.D research was done in the Robotics and Artificial Intelligence group of the LAAS/CNRS. In 1998–1999, he was a postdoctoral researcher in the Computer Science Department at Stanford University. From January 2000 to July 2002 he was an assistant professor in the Electrical Engineering Department at ITESM Campus México City, México. In 2002–2004, he was working as a postdoctoral research associate in the Beckman Institute and Department of Electrical and Computer Engineering of the University of Illinois at Urbana-Champaign. Since August 2004, he is director of the Mechatronics Research Center in the ITESM Campus Estado de México, México. He is mainly interested in sensor-based robotics motion planning and computer vision. Benjamin Tovar received the B.S degree in electrical engineering from ITESM at Mexico City, Mexico, in 2000, and the M.S. in electrical engineering from University of Illinois, Urbana-Champaign, USA, in 2004. Currently (2005) he is pursuing the Ph.D degree in Computer Science at the University of Illinois. Prior to M.S. studies he worked as a research assistant at Mobile Robotics Laboratory at ITESM Mexico City. He is mainly interested in motion planning, visibility-based tasks, and minimal sensing for robotics. Seth Hutchinson received his Ph. D. from Purdue University in West Lafayette, Indiana in 1988. He spent 1989 as a Visiting Assistant Professor of Electrical Engineering at Purdue University. In 1990 Dr. Hutchinson joined the faculty at the University of Illinois in Urbana-Champaign, where he is currently a Professor in the Department of Electrical and Computer Engineering, the Coordinated Science Laboratory, and the Beckman Institute for Advanced Science and Technology. Dr. Hutchinson is currently a senior editor of the IEEE Transactions on Robotics and Automation. In 1996 he was a guest editor for a special section of the Transactions devoted to the topic of visual servo control, and in 1994 he was co-chair of an IEEE Workshop on Visual Servoing. In 1996 and 1998 he co-authored papers that were finalists for the King-Sun Fu Memorial Best Transactions Paper Award. He was co-chair of IEEE Robotics and Automation Society Technical Committee on Computer and Robot Vision from 1992 to 1996, and has served on the program committees for more than thirty conferences related to robotics and computer vision. He has published more than 100 papers on the topics of robotics and computer vision.     

Development of a future Intelligent Sweet Home for the disabled

This article introduces a new smart house, the Intelligent Sweet Home, developed at the Korea Advanced Institute of Science and Technology (KAIST), Korea, for testing advanced concepts for independent living for elderly and physically handicapped individuals. The work focuses on human-friendly technical solutions for motion/mobility assistance, health monitoring, and advanced human–machine interfaces that provide easy control of both assistive devices and home-installed appliances. The smart house behaves in accordance with the user's commands, the user's intentions, and the user's current health status. Various interfaces based on hand gestures, voice, body movement and posture, and the health monitoring system were studied and tested. This article emphasizes the structure of the system. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

A Fuzzy set based approach to generalized landscape theory of aggregation

In this paper, we firstly reformulate the landscape theory of aggregation (Axelrod and Bennett, 1993) in terms of an optimization problem, and then straightforwardly propose a fuzzy-set-theoretic based extension for it. To illustrate efficiency of the proposal, we make a simulation with the proposed framework for the international alignment of the Second World War in Europe. It is shown that the obtained results are essentially comparable to those given by the original theory. Consequently, the fuzzy-set-theoretic based extension of landscape theory can allow us to analyze a wide variety of aggregation processes in politics, economics, and society in a more flexible manner. Shigemasa Suganuma: He received the M.S. degree in knowledge science from Japan Advanced Institute of Science and Technology,, Ishikawa, Japan in 2000. He currently takes a doctor's course in School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST). His research interest includes agent based simulation and its application to social and political concerns, industry and environmental behavior. Van-Nam Huynh, Ph.D.: He received the B.S. in Mathematics (1990) and Ph.D. (1999) from University of Quinhon, Vietnam and Institute of Information Technology, Vietnam Academy of Science and Technology, respectively. From April 2001 to March 2002, he was a postdoctoral fellow awarded by INOUE Foundation for Science at JAIST. He is currently a Research Associate in School of Knowledge Science, JAIST, Japan. His current research interests include fuzzy logic and approximate reasoning, uncertainty formalisms in knowledge-based systems, decision making. Yoshiteru Nakamori, Ph.D.: He received the B.S., M.S., and Ph.D. degrees all in applied mathematics and physics from Kyoto University, Kyoto, Japan. He is currently a Professor in School of Knowledge Science, JAIST. His research interests include development of modeling methodology based on hard as well as soft data, and support systems for soft thinking around hard data. Shouyang Wang, Ph.D.: He received the Ph.D. degree in Operations Research from Chinsese Academy of Sciences (CAS), Beijing in 1986. He is currently a Bairen distinguished professor of Management Science at Academy of Mathematics and Systems Sciences of CAS and a Lotus chair professor of Hunan University in Changsha. He is the editor-in-chief or a co-editor of 12 journals. He has published 120 journal articles. His current research interest includes decision analysis, system engineering and knowledge management.     

Evolutionary control of bipedal locomotion in a high degree-of-freedom humanoid robot: first steps

This article describes a methodology, together with an associated series of experiments employing this methodology, for the evolution of walking behavior in a simulated humanoid robot with up to 20 degrees of freedom. The robots evolved in this study learn to walk smoothly in an upright or near-upright position and demonstrate a variety of different locomotive behaviors, including “skating,” “limping,” and walking in a manner curiously reminiscent of a mildly or heavily intoxicated person. A previous study demonstrated the possible potential utility of this approach while evolving controllers based on simulated humanoid robots with a restricted range of movements. Although walking behaviors were developed, these were slow and relied on the robot walking in an excessively stooped position, similar to the gait of an infirm elderly person. This article extends the previous work to a robot with many degrees of freedom, up to 20 in total (arms, elbows, legs, hips, knees, etc.), and demonstrates the automatic evolution of fully upright bipedal locomotion in a humanoid robot using an accurate physics simulator. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Further explorations in evolutionary humanoid robotics

The field of evolutionary humanoid robotics is a branch of evolutionary robotics specifically dealing with the application of evolutionary principles to humanoid robot design. Previous studies demonstrated the possible future potential of this approach by evolving walking behaviors for simulated humanoid robots with up to 20 degrees of freedom. In this paper we examine further the evolutionary process by looking at the changes in diversity over time. We then investigate the effect of the immobilization of an individual joint or joints in the robot. The latter study may be of potential future use in prosthetic design. We also explore the possibility of the evolution of humanoid robots which can cope with different environmental conditions. These include reduced ground friction (ice) and modified gravitation (moon walking). We present initial results on the implementation of our simulated humanoid robots in hardware using the Bioloid robotic platform, using a model of this robot in order to evolve the desired motion patterns, for subsequent transfer to the real robot. We finish the article with a summary and brief discussion of future work. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Relative localization using path odometry information

All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder’s information is integrated to derive the robot’s position; this is called odometry. A combination of external and internal sensors will ultimately solve the localization error problem, but this paper focuses only on processing the odometry information. We solve the localization problem by forming a new odometry error model for the synchro-drive robot then use a novel procedure to accurately estimate the error parameters of the odometry error model. This new procedure drives the robot through a known path and then uses the shape of the resulting path to estimate the model parameters. Experimental results validate that the proposed method precisely estimates the error parameters and that the derived odometry error model of the synchro-drive robot is correct. Nakju Lett Doh received his BS, his MS, and his Ph.D. degree in Mechanical Engineering from Pohang University of Science and Technology (POSTECH), KOREA, in 1998, 2000, and 2005, respectively. Since then, he is a senior researcher in Intellgient Robot Reserarch Division, Electronics and Telecommunications Research Institute (ETRI), KOREA. He received the glod prize in Intelligent Robot Contest hosted by Northern KyoungSang Province at 2000 and the gold prize in Humantech Thesis Competition hosted by Samsung Electronics at 2005. In 2003, he got the best student paper award in IEEE International Conference on Robotics and Automation held in Taiwan. His research interests are the localization and navigation of mobile robots and ubiquitous robotic space for intelligent robot navigation. Howie Choset is an Associate Professor of Robotics at Carnegie Mellon University where he conducts research in motion planning and design of serpentine mechanisms, coverage path planning for de-mining and painting, mobile robot sensor based exploration of unknown spaces, and education with robotics. In 1997, the National Science Foundation awarded Choset its Career Award to develop motion planning strategies for arbitrarily shaped objects. In 1999, the Office of Naval Research started supporting Choset through its Young Investigator Program to develop strategies to search for land and sea mines. Recently, the MIT Technology Review elected Choset as one of its top 100 innovators in the world under 35. Choset directs the Undergraduate Robotics Minor at Carnegie Mellon and teaches an overview course on Robotics which uses series of custom developed Lego Labs to complement the course work. Professor Choset’s students have won best paper awards at the RIA in 1999 and ICRA in 2003. Finally, Choset is a member of an urban search and rescue response team using robots with the Center for Robot Assisted Search and Rescue. Now, he is active in extending the mechanism design and path planning work to medical mechatronics. Wan Kyun Chung received his BS degree in Mechanical Design from Seoul National University in 1981, his MS degree in Mechanical Engineering from KAIST in 1983, and his Ph.D. in Production Engineering from KAIST in 1987. He is Professor in the school of Mechanical Engineering, POSTECH (he joined the faculty in 1987). In 1988, he was a visiting professor at the Robotics Institute of Carnegie-Mellon University. In 1995 he was a visiting scholar at the university of California, Berkeley. His research interests include the localization and navigation for mobile robots, underwater robots and development of robust controller for precision motion control. He is a director of National Research Laboratory for Intelligent Mobile Robot Navigation. He is serving as an Associate Editor for IEEE Tr. on Robotics, international editorial board for Advanced Robotics.     

Robovie: Communication technologies for a social robot

ATR Media Information Science Laboratories has developed a humanoid-type robot that can work in our daily life and fit naturally into human society. This robot will be a platform for developing various types of service robots, such as cleaning robots, security patrol robots, and entertainment robots, based on a rich communication ability. This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, Japan, January 15–17, 2001     

Preemptive RANSAC for live structure and motion estimation

A system capable of performing robust live ego-motion estimation for perspective cameras is presented. The system is powered by random sample consensus with preemptive scoring of the motion hypotheses. A general statement of the problem of efficient preemptive scoring is given. Then a theoretical investigation of preemptive scoring under a simple inlier–outlier model is performed. A practical preemption scheme is proposed and it is shown that the preemption is powerful enough to enable robust live structure and motion estimation.Prepared through collaborative participation in the Robotics Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0012. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. David Nistér received PhD degree in computer vision, numerical analysis and computing science from the Royal Institute of Technology (KTH), Stockholm, Sweden, with the thesis ‘Automatic Dense Reconstruction from Uncalibrated Video Sequences’. He is currently an assistant professor at the Computer Science Department and the Center for Visualization and Virtual Environments, University of Kentucky, Lexington. Before joining UK, he was a researcher in the Vision Technologies Laboratory, Sarnoff Corporation, Princeton, and Visual Technology, Ericsson Research, Stockholm, Sweden. His research interests include computer vision, computer graphics, structure from motion, multiple view geometry, Bayesian formulations, tracking, recognition, image and video compression. He is a member of the IEEE and American Mensa.     

Pitch-Dependent Identification of Musical Instrument Sounds

This paper describes a musical instrument identification method that takes into consideration the pitch dependency of timbres of musical instruments. The difficulty in musical instrument identification resides in the pitch dependency of musical instrument sounds, that is, acoustic features of most musical instruments vary according to the pitch (fundamental frequency, F0). To cope with this difficulty, we propose an F0-dependent multivariate normal distribution, where each element of the mean vector is represented by a function of F0. Our method first extracts 129 features (e.g., the spectral centroid, the gradient of the straight line approximating the power envelope) from a musical instrument sound and then reduces the dimensionality of the feature space into 18 dimension. In the 18-dimensional feature space, it calculates an F0-dependent mean function and an F0-normalized covariance, and finally applies the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments shows that the proposed method improved the recognition rate from 75.73% to 79.73%. This research was partially supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Grant-in-Aid for Scientific Research (A), No.15200015, and Informatics Research Center for Development of Knowledge Society Infrastructure (COE program of MEXT, Japan). Tetsuro Kitahara received the B.S. from Tokyo University of Science in 2002 and the M.S. from Kyoto University in 2004. He is currently a Ph.D. course student at Graduate School of Informatics, Kyoto University. Since 2005, he has been a Research Fellow of the Japan Society for the Promotion of Science. His research interests include music informatics. He recieved IPSJ 65th National Convention Student Award in 2003, IPSJ 66th National Convention Student Award and TELECOM System Technology Award for Student in 2004, and IPSJ 67th National Convention Best Paper Award for Young Researcher in 2005. He is a student member of IPSJ, IEICE, JSAI, ASJ, and JSMPC. Masataka Goto received his Doctor of Engineering degree in Electronics, Information and Communication Engineering from Waseda University, Japan, in 1998. He then joined the Electrotechnical Laboratory (ETL; reorganized as the National Institute of Advanced Industrial Science and Technology (AIST) in 2001), where he has been engaged as a researcher ever since. He served concurrently as a researcher in Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Corporation (JST) from 2000 to 2003, and an associate professor of the Department of Intelligent Interaction Technologies, Graduate School of Systems and Information Engineering, University of Tsukuba since 2005. His research interests include music information processing and spoken language processing. Dr. Goto received seventeen awards including the IPSJ Best Paper Award and IPSJ Yamashita SIG Research Awards (MUS and SLP) from the Information Processing Society of Japan (IPSJ), Awaya Prize for Outstanding Presentation and Award for Outstanding Poster Presentation from the Acoustical Society of Japan (ASJ), Award for Best Presentation from the Japanese Society for Music Perception and Cognition (JSMPC), WISS 2000 Best Paper Award and Best Presentation Award, and Interaction 2003 Best Paper Award. He is a member of the IPSJ, ASJ, JSMPC, Institute of Electronics, Information and Communication Engineers (IEICE), and International Speech Communication Association (ISCA). Hiroshi G. Okuno received the B.A. and Ph.D from the University of Tokyo in 1972 and 1996, respectively. He worked for Nippon Telegraph and Telephone, Kitano Symbiotic Systems Project, and Tokyo University of Science. He is currently a professor at the Department of Intelligence Technology and Science, Graduate School of Informatics, Kyoto University. He was a visiting scholar at Stanford University, and a visiting associate professor at the University of Tokyo. He has done research in programming languages, parallel processing, and reasoning mechanism in AI, and he is currently engaged in computational auditory scene analysis, music scene analysis and robot audition. He received the best paper awards from the Japanese Society for Artificial Intelligence and the International Society for Applied Intelligence, in 1991 and 2001, respectively. He edited with David Rosenthal “Computational Auditory Scene Analysis” from Lawrence Erlbaum Associates in 1998 and with Taiichi Yuasa “Advanced Lisp Technology” from Taylor and Francis Inc. in 2002. He is a member of IPSJ, JSAI, JSSST, JSCS, ACM, AAAI, ASA, and IEEE.     

Chance discovery and learning minority classes

Chances are viewed in chance discovery as events/situations with significant impact on human decision making. In this research context we are particularly interested in a subset of chances that are unexpected or contradictory with human common knowledge, and the human role that we consider as an essential factor in finding such chances. We first introduce the method LUPC that can learn minority classes from large unbalanced datasets. With its visualization tools as well its exclusive and inclusive constraints, LUPC allows the user to actively participate in and to incorporate background knowledge in the chance discovery process. We then present case studies in which LUPC is used to support the user in discovering significant unexpected chances from stomach cancer and hepatitis databases. Tu Bao Ho: He received a B. Eng. degree from Hanoi University of Technology in 1978, M.S. and Ph.D. degrees from University Paris 6, in 1984 and 1987. He is currently professor at School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST). His research interests include machine learning, knowledge-based systems and knowledge discovery and data mining. Duc Dung Nguyen: He received a B.A. degree from Hanoi University of Pedagogy in 1993. He is currently a master student at School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST). His research interests include pattern recognition and knowledge discovery and data mining.