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Optical mouse sensor for non-contact tactile sensing

IEEJ Transactions on Sensors and Micromachines 2008年第9期128卷 358-363+3页

作者： Xin, Wang Shida, Katsunori Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University 1 Honjo-machi Saga 840-8502 Japan

Instead of functioning as part of a pointing device in computing, the optical mouse sensor is employed for non-contact tactile sensing in this study. The translation direction, the height and the category of an object surface can be sensed. It is an interesting idea to make an optical mouse sensor work in the manner of a human hand but in a non-contact way to "feel" the object surface. Based on the data table obtained from the experiments, the translation direction is calculated and the nonlinearity of the translation direction sensing is studied. It can be concluded that the resolution of an optical mouse sensor is direction-related. The feasibility of surface height measurement is analyzed and the optical mouse sensor can be used for crack detection on a plane surface. Height variation of 0.2mm can be detected. Besides, the feasibility of the optical mouse sensor for surface discrimination is proposed. © 2008 The Institute of Electrical Engineers of Japan.

关键词： Crack detection

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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Neural network learning from hint for the inverse kinematics problem of redundant arm subject to joint limits

Neural network learning from hint for the inverse kinematics...

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作者： Assal, Samy F.M. Watanabe, Keigo Izumi, Kiyotaka Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University Japan

ISBN: (纸本)0780389123

A novel online inverse kinematics solution of redundant manipulator to avoid joint limits is presented. A Widrow-Hoff neural network (NN) with a learning algorithm derived by applying Lyapunov approach is introduced for this problem. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlling each joint angle of the manipulator. The joint velocity limits as well as the joint limits are incorporated into this method. Experiments are conducted for the PA-10 redundant arm to demonstrate the efficacy of the proposed control system. A comparative study is made with the gradient projection method. © 2005 IEEE.

关键词： Redundant manipulators

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Approximate decision making by natural language commands for robots

Approximate decision making by natural language commands for...

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IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics

作者： Watanabe, Keigo Jayawardena, Chandimal Izumi, Kiyotaka Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University Japan

ISBN: (纸本)1424401364

Inferring the correct meaning of natural language commands, as judged by the person who issues commands, is mandatory for natural language commanded robotic systems. There have been some successful research on this;but one of the important and related aspects has not been addressed, i.e. the possibility of learning from natural language commands. Since natural language commands are generated by human users, they contain valuable information. Nevertheless, the learning from such commands, as well as the interpretation of them face many challenges due to the inherent subjectiveness of natural languages. In this paper, we propose a decision making process for natural language commanded robots which is influenced by certain characteristics of human decision making process. The proposed concept is demonstrated with an experiment conducted using a robotic manipulator. First, the robot is controlled with natural language commands to perform some pick and place operations during which the robot builds a knowledge base. After learning, the robot is capable of performing approximately similar tasks by making approximate decisions with the gained knowledge. For the decision making a probabilistic neural network is used. © 2006 IEEE.

关键词： Neural networks

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Teaching a tele-robot using natural language commands

Teaching a tele-robot using natural language commands

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2005 International Symposium on Micro-NanoMechatronics and Human Science, Eighth Symposium on Micro- and Nano-Mechatronics for Information-Based Society - The 21st Century COE Program

作者： Jayawardena, Chandimal Watanabe, Keigo Izumi, Kiyotaka Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University Japan

ISBN: (纸本)0780394828

For Internet-based tele operation systems, user-friendly natural interfaces are advantageous because those systems are intended to be used by non-experts. In developing user friendly interfaces, natural language communication is mandatory. This paper presents a system in which a sub-set of natural language is used to command a tele-robot manipulator doing an object sorting task. The paper discusses about referring to objects with natural language commands such as "pick the small red cube". This is achieved by learning individual lexical symbols that refer to colors, shapes, and sizes independently, and then inferring the meaning of a combination of them.

关键词： Robotics

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Bipedal locomotion control via CPGs with coupled nonlinear oscillators

Bipedal locomotion control via CPGs with coupled nonlinear o...

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4th IEEE International Conference on Mechatronics, ICM 2007

作者： Habib, Maki K. Guang, Lei Liu Watanabe, Keigo Izumi, Kiyotaka Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University Japan

ISBN: (纸本)142441184X

This paper describes the design and analysis of a biologically inspired central pattern generator (CPG) using a network of mutually coupled nonlinear oscillators to generate rhythmic walking pattern for biped robots. The paper examines the characteristics of a CPG model composed of a network of nonlinear oscillators, and the effect of assigning symmetrical and asymmetrical coupling mechanism among oscillators within the network structure under different possibilities of inhibitions and excitations. The paper highlights the necessity to understand the targeted physical system and its functionalities before concluding the design parameters of the CPG. In addition, the paper considers the way in which the sensory feedback contributes to generate adaptive walking trajectory and enhance gait stability, and how the driving input and external perturbation affect the speed of locomotion and change the period of its own active phase. Modeling of bipedal robot using a CPG based controller and a musculo-skeletal system has been achieved for the purpose to realize the interaction with each other and to study the necessary conditions for stable dynamic walking on dynamic terrain that lead to stable and sustained response from the network. The kinematics and dynamics of a five-link biped robot has been modeled and its joints are actuated through simulation by the proportional torques output from the CPG to generate the trajectories for hip, knee, and ankle joints. The CPG based bipedal locomotion is carried out and evaluated through simulations using MATLAB. ©2007 IEEE.

关键词： Sensory feedback

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Neural-Network-Based control for Discrete-Time Nonlinear systems with Input Saturation Under Stochastic Communication Protocol

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 766-778页

作者： Xueli Wang Derui Ding Hongli Dong Xian-Ming Zhang Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Software and Electrical Engineering Swinburne University of TechnologyMelbourne 3122VictoriaAustralia Institute of Complex Systems and Advanced Control Northeast Petroleum UniversityDaqing 163318China

In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input *** save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient *** virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN ***,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical ***,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov ***,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

关键词： Adaptive dynamic programming(ADP) constrained inputs neural network(NN) stochastic communication protocols(SCPs) suboptimal control

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Cooperative fuzzy hint acquisition for avoiding joint limits of redundant manipulators

Cooperative fuzzy hint acquisition for avoiding joint limits...

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IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society

作者： Assal, Samy F.M. Watanabe, Keigo Izumi, Kiyotaka Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University Japan

In this paper, a back propagation neural network (NN) is presented for the inverse kinematics of redundant manipulator with joint limits. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlled each joint angle in the sense that it stops the motion on the critical axis at its limit in the expense of more compensation from the most relaxed joint to accomplish the task. The joint velocity limits as well as the joint limits are incorporated in this method. Simulations are implemented based on four-link redundant manipulator to show the effectiveness of the proposed control system. © 2004 IEEE.

关键词： Redundant manipulators

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Simultaneous Localization and Mapping (SLAM) based on pseudolinear measurement model with a bias reduction approach

Simultaneous Localization and Mapping (SLAM) based on pseudo...

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ICIIS 2007 - 2nd International Conference on Industrial and Information systems 2007

作者： Pathiranage, Chandima Dedduwa Watanabe, Keigo Izumi, Kiyotaka Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University Japan

ISBN: (纸本)1424411521

This paper describes an improved solution to mobile robot localization and map building problem based on pseudolinear measurement model through bias reduction approach. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. Direct linearization of nonlinear models has always come up with information loss of original nonlinear models. A state estimator which uses linearized models can be a solution to a problem where high accuracy is not expected. A state estimator which can be designed to use the nonlinearity as it is coming from the original model has always been invaluable in which high accuracy is expected. Thus to accomplish the above highlighted point, pseudolinear measurement model is directly applied to the Kalman filter equations without losing the original nonlinearity. We address the pseudolinear bias problem through simple geometry translation of system states. This system is simulated using Matlab for vehicle-landmarks system and results show that the new approach performs much accurately compared to that of well known Extended Kalman filter (EKF). ©2007 IEEE.

关键词： Extended Kalman filters

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Robust adaptive H∞ control for networked uncertain semi-Markov jump nonlinear systems with input quantization

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Science China(Information Sciences) 2022年第8期65卷 285-286页

作者： Shanling DONG Guanrong CHEN Meiqin LIU Zheng-Guang WU College of Electrical Engineering Zhejiang University Department of Electrical Engineering City University of Hong Kong National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University Institute for Advanced Study Chengdu University

Dear editor,Markov jump systems(MJSs), as one kind of switched systems, are a good candidate for modeling physical systems subject to abrupt variation in their structures or parameters caused by many reasons such as s...

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