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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： G. Carbone Y. Sugahara H.O. Lim A. Takanishi M. Ceccarelli Humanoid Robotics Institute – Waseda University Tokyo Japan Laboratory of Robotics and Mechatronics DiMSAT – University of Cassino Cassino Fr Italy Graduate School of Science and Engineering Waseda University Tokyo Japan Department of System Design Engineering Kanagawa Institute of Technology Atsugi Kanagawa Japan

In this paper a biped locomotor, named as WL-15 (Waseda Leg 15), is analyzed in terms of stiffness characteristics. Basic models and a formulation are proposed in order to deduce the stiffness matrix as a function of the most important design parameters of the WL-15 architecture. First numerical simulations and experimental tests are also carried out for stiffness performance estimation.

关键词： Legged locomotion Humans Leg Testing Humanoid robots Design engineering Wheels Payloads Algorithm design and analysis Prototypes

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Implementation of a neural network based system for estimating the strength of a board using mixed signals

Implementation of a neural network based system for estimati...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： H. Saboksayr A. Saravi P.D. Lawrence F. Lam IEEE Control System Society Vancouver Canada Robotics and Control Laboratoryratory Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Robotics and Control Laboratory Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Department of Wood Science University of British Columbia Vancouver Canada

An intelligent lumber grading system was developed to provide a new way for estimating the strength of a board by posing the estimation problem as an empirical learning problem. This system processed the X-ray image, extracted geometric features (of 1000 boards that eventually underwent destructive strength testing), and predicted the strength of the lumber by using a neural network. The X-ray image was passed through a threshold filter to separate the knots based on the fact that a denser knot produces a local maximum (as a rounded protrusion in an otherwise almost flat density surface) of the X-ray image. Each knot was modeled by a three-dimensional-cone with seven parameters. Information on all the detected knots such as volume, and knot-area-ratio were fed to a processor to generate 16 geometrical features (such as; the average of knot area ratio, and the number of knots detected in each board), which characterize each board. Then by using back-propagation as the training method, cross correlation as the measure of accuracy, and actual strength of a thousand boards as the empirical data set, a neural network was trained to estimate the strength of each board. The learning system consisted of three layers, with 1, 5, 16 neurons in output, hidden and input layer respectively. Ten-fold cross validation was used to produce an unbiased accuracy of the estimation problem. The learning and testing sets comprised of 900 and 100 boards respectively. By repeating the learning and testing for ten times and averaging the results, a coefficient of determination of 0.4059 was reached in this study for using X-ray images alone. The same methodology was applied to MOE (modulus of elasticity) and a coefficient of determination of 0.56 was reached. The results were improved by fusing the X-ray image and MOE using a learning system consisting of three layers, with 1, 5, 40 neurons in output, hidden and input layer respectively. Ten-Fold cross validation resulted in a coefficient o

关键词： Neural networks X-ray imaging Feature extraction Learning systems Neurons Intelligent systems system testing Filters Character generation Elasticity

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Affecting the accuracy of an active vision head 2nd

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2nd Hellenic Conference on Artificial Intelligence, SETN 2002

作者： Gasteratos, Antonios Sandini, Giulio Laboratory of Electronics Section of Electronics and Information Systems Technology Department of Electrical and Computer Engineering Democritus University of Thrace XanthiGR-671 00 Greece Laboratory for Integrated Advanced Robotics Department of Communication Computer and System Sciences University of Genoa Viale Causa 13 GenoaI-16145 Italy

ISBN: (纸本)3540434720

In any measuring system the categorization of the error generation factors leads to simplification of complex error problems and to higher suppression of the error. In this paper we categorize, quantify and analyze the errors that affect a binocular active vision head. Simulations have been made and experimental results on a high resolution pan-tilt-vergence mechanism are also proposed. As a conclusion it can be said that the system performs optimal when it is initialized so that the two cameras are perfectly aligned and perpendicular to the baseline. Small variations in the vergence angle or small horizontal deviations of the principal point alters the measurement dramatically. On the other hand, variations in pan and tilt and vertical deviations of the principal point, affect the measurement insignificantly. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Errors

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Artificial immune-based swarm behaviors of distributed autonomous robotic systems

Artificial immune-based swarm behaviors of distributed auton...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sang-Joon Sun Dong-Wook Lee Kwee-Bo Sim Robotics and Intelligent Information System Laboratory School of Electrical and Electronic Engineering Chung-Ang University Seoul South Korea

ISBN: (纸本)0780365763

We propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. For applying the immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy and its behavior strategy is stimulated and suppressed by other robots using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and the idiotopic network hypothesis. It is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

关键词： Immune system Intelligent robots Decision making Communication system control Protection Sun Intelligent systems Artificial intelligence Information systems Control systems

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Genetic programming of process decomposition strategies for evolvable hardware

Genetic programming of process decomposition strategies for ...

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NASA/DoD Workshop on Evolvable Hardware

作者： Ho-Sik Seok Kwang-Ju Lee Byoung-Tak Zhang Dong-Wook Lee Kwee-Bo Sim Artificial Intelligence Laboratory SCAI School of Computer Science and Engineering Seoul National University Seoul South Korea Robotics and Intelligent Information System Lab Electrical and Electronic Engineering Chung-Ang University Seoul South Korea

Evolvable hardware is able to offer considerably higher performance than general-purpose processors and significantly more flexibility than ASICs. In order to take the advantages of general-purpose processors and ASICs, dividing a complex process into subprocesses is essential. In this paper, we propose a evolutionary method called context switching that splits a task into a set of subtasks whose complexity is manageable on the given hardware. The method is based on genetic programming. Due to its expressive power generic program can represent flexible strategies for decomposing complex tasks. The effectiveness of context switching is demonstrated on the design of adaptive controllers for a team of autonomous mobile robots.

关键词： Genetic programming Hardware Genetic algorithms Artificial intelligence Intelligent robots Mobile robots Programmable control Adaptive control Computer architecture Application specific integrated circuits

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Team scheduling by genetic search 2

Team scheduling by genetic search

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2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999

作者： Zhang, Tiehua Graver, William A. Smith, Michael H. Intelligent Robotics and Manufacturing System Laboratory Simon Fraser University BurnabyBCV5A1S6 Canada University of California at Berkeley Department of Electrical Engineering and Computer Science BerkeleyCA United States

ISBN: (纸本)0780354893

We consider a photographic studio that must schedule multiple teams of photographers to a large number of elementary and secondary schools. The photographers' schedules are to be optimized so that time constraints are satisfied and each team is able to at least visit two schools daily. A multiple Travelling Salesman model is used where the total distance traveled and time consumed can be evaluated in a single cost function to achieve overall optimality. A genetic algorithm has been applied to solve the problem. The results show that this approach rapidly provides an effective means for solving the problem. © 1999 IEEE.

关键词： Cost functions

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The fuzzy classifier system using the implicit bucket brigade algorithm

The fuzzy classifier system using the implicit bucket brigad...

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IEEE International Workshop on Intelligent Robots and systems (IROS)

作者： Chi-Sun Joung Dong-Wook Lee Kwee-Bo Sim Robotics and Intelligent Information System Laboratory School of Electrical and Electronic Engineering Chung-Ang University Seoul South Korea

The fuzzy classifier system (FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The classifier system can evaluate the usefulness of rules represented by classifiers with repeated learning It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit bucket brigade algorithms. Also the FCS employs the genetic algorithms (GAs) to make new rules and modify rules when performance of the system needs to be improved.

关键词： Fuzzy systems Fuzzy control Input variables Intelligent robots Genetic algorithms Learning Intelligent systems Information systems Laboratories Electronic mail

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Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system

Realization of cooperative strategies and swarm behavior in ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jin-Hyung Jun Dong-Wook Lee Kwee-Bo Sim Robotics and Intelligent Information System Laboratory School of Electrical and Electronic Engineering Chung-Ang University Seoul South Korea

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in a distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. To apply the immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robots using communication. Finally, the most stimulated strategy is adopted as the swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. It is used for decision making of the optimal swarm strategy. By T-cell modeling, the adaptation ability of the robot is enhanced in dynamic environments.

关键词： Immune system Intelligent robots Decision making Communication system control Protection Artificial immune systems Intelligent systems Artificial intelligence Information systems Control systems

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Coevolutionary Algorithms for Realization of Intelligent systems

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Journal of Advanced Computational Intelligence and Intelligent Informatics 1999年第5期3卷 418-426页

作者： Jun, Hyo-Byung Sim, Kwee-Bo Robotics and Intelligent Information System Laboratory School of Electrical and Electronic Engineering Chung-Ang University 221 Huksuk-Dong Dongjak-Ku Seoul156-756 Korea Republic of

The simple genetic algorithm (SGA) proposed by J. H. Holland uses population-based optimization based on Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and Building Block Hypothesis. Although GA does well in many applications in optimization, it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, since there is no deterministic solution, heuristic trial-and error is usually used to determine system parameters. An alternative is a coevolutionary system, where 2 populations constantly interact and coevolve. We review coevolutionary algorithms and propose coevolutionary schemes designing intelligent systems based on the relationship between system components. © Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://***/licenses/by-nd/4.0/).

关键词： Intelligent systems

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Assist system for carrying a long object with a human-analysis of a human cooperative behavior in the vertical direction

Assist system for carrying a long object with a human-analys...

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IEEE International Workshop on Intelligent Robots and systems (IROS)

作者： Y. Hayashibara T. Takubo Y. Sonoda H. Arai K. Tanie Department of Control and System Engineering Toin University of Yokohama Yokohama Kanagawa Japan System Electronics Tokyo Engineering University Hachioji Tokyo Japan Institute of Engineering Mechanics University of Tsukuba Tsukuba Ibaraki Japan Robotics Department Mechanical Engineering Laboratory AIST-MITI Tsukuba Ibaraki Japan

Deals with an assist system for carrying a long object with a human operator. When we carry such an object, we often grasp both ends and move it cooperatively. Our purpose is to establish how to design the assist system which can achieve such a task. It is difficult to apply conventional control laws. On the other hand, humans can achieve such a task. Therefore, we measure the human cooperative behaviors and analyze them to find the cooperative rules. Based on the rules, we propose a control law of the assist system. Furthermore, we construct a prototype system and verify the validity of the control law.

关键词： Humans Control systems Anthropometry Force sensors Force measurement Computer displays systems engineering and theory Prototypes Robot sensing systems Torque

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