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Annual Conference of Industrial Electronics Society

作者： Ren C. Luo Shih-Che Chou Xin-Yi Yang Norman Peng International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University Taipei TAIWAN Industrial Technology Research Institute Hsinchu TAIWAN

The objective of this paper is to develop conveyer object tracking and fetching system. The traditional visual servo system for parallel robot is based on Eye-to-hand method which suffers from the narrow field of view (FOV). The basic step of moving assembling is multiple objects tracking which needs broad work space and big FOV of the parallel robot. In this paper, a Hybrid Eye-to-hand and Eye-in-hand Visual Servo System for parallel robot is proposed. Via this hybrid system, the parallel robot can achieve (multiple) object tracking and fetching with priority planning we designed. The eye-to-hand visual system with low resolution camera provides the estimations of the velocities and poses of the workpieces. Also the fetching priority is planned with this system. All aforementioned information is transferred to the eye-in-hand visual servo system for further use. Once the object appeared in the FOV of eye-in-hand system in the estimated position, the high resolution camera of the system is employed to acquire the pose information of the object with high accuracy and track it frame by frame. The hybrid system is successfully demonstrated and implemented on parallel robot which not only broadens the working space of parallel robot but also achieves great efficiency and accuracy.

关键词： Parallel robots Cameras Estimation Robot vision systems Visualization Servomotors

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Multi-objects recognition using unsupervised learning and classification

Multi-objects recognition using unsupervised learning and cl...

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IEEE international Symposium on Industrial Electronics (ISIE)

作者： Ren C. Luo Po-Yu Chuang Xin-Yi Yang International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University Taipei Taiwan

ISBN: (纸本)9781467351942

The objective of this paper is to develop a real-time unsupervised learning method to detect multi-objects. Variance and gradient variance, as main texture feature, are compressed based on PCA (Principal Component Analysis) to get initial classifications of clusters via K-means algorithm in the image frame. The cluster-kernel of each class, the nucleus as we defined, is figured out through shifting the sampling area in each cluster. Based on the nucleus, the policy of cell expansion as we designed is operated to merge different classes into one object which turns the work from feature level into object level. All potential objects and cells of the objects are detected and labeled in the frame. The descriptors of each object are employed as hypothesis for the next frames object-classification. Our results demonstrate that it is possible and fast to recognize multi-objects without and training model or labeled data. The process of learning could be operated with initial clustering and automatic updating with information of the new classification.

关键词： Feature extraction Clustering algorithms Principal component analysis Algorithm design and analysis Merging Unsupervised learning Image color analysis

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Real time human motion imitation of anthropomorphic dual arm robot based on Cartesian impedance control

Real time human motion imitation of anthropomorphic dual arm...

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international Workshop on Robot Sensing (ROSE)

作者： Ren C. Luo Bo-Han Shih Tsung-Wei Lin International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University Taipei Taiwan

This paper presented a real-time human motion imitation approach to control an anthropomorphic dual arm robot by human demonstration. We use the processed positions of human skeleton joints from Kinect sensor as commands directly to control the robot arms by using Cartesian impedance control to follow the human motion without solving inverse kinematics problem. In order to avoid a jerky robot arm motion, we apply an on-line trajectory generator algorithm to obtain a smooth movement trajectory by imposing the limit of velocity and acceleration. Moreover, the self-collision problem has also been considered. When the distance between two parts of body is close enough, a repulsive force will automatically generate to prevent collision. Taking the robot capability and safe issue into account, the output force is restricted to ensure that the action of robot is stable. We demonstrate the feasibility of the approach by implementing the human motion imitation system on a humanoid dual arm robot developed in our lab. The experimental results show that the system is in good practice and flexible enough to imitate various human motions.

关键词： Robot sensing systems Force Trajectory Robot kinematics Joints Collision avoidance

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Resume navigation and re-localization of an autonomous mobile robot after being kidnapped

Resume navigation and re-localization of an autonomous mobil...

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international Workshop on Robot Sensing (ROSE)

作者： Ren C. Luo Keng C. Yeh Kuan H. Huang International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University Taipei Taiwan

The kidnapped robot problem is one of the essential issues in Human Robot Interaction research fields. This work addresses the problem of the position and orientation (pose) recovery after the robot being kidnapped, based on Laser Range Finder (LRF) sensor. By now the Monte Carlo Localization (MCL) has been introduced as a useful localization method. However the computational load of MCL is extremely large and not efficient at the initial few steps, which causes the localization process to take long computation time after the robot has been kidnapped and resets the particles. This paper provides a methodology to solve it by fusing MCL with Fast Library for Approximate Nearest Neighbors (FLANN) machine learning technique. We design a feature for LRF data called Geometric Structure Feature Histogram (GSFH).The feature GSFH encodes the LRF data to use it as the descriptor in FLANN. By building the database previously and FLANN searching technique, we filter out the most impossible area and reduce the computation load of MCL. Both in simulation and real autonomous mobile robot experiments show the effectiveness of our method.

关键词： Load modeling Robots Computational efficiency Convergence

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Cartesian position and force control with adaptive impedance/compliance capabilities for a humanoid robot arm

Cartesian position and force control with adaptive impedance...

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IEEE international Conference on robotics and automation (ICRA)

作者： Ren C. Luo Yi-Wen Perng Bo-Han Shih Yun-Hsuan Tsai International Center of Excellence on Intelligent Robotics and Automation Research (iCeiRA) National Taiwan University Taipei Taiwan

This paper describes Cartesian position and force control with adaptive impedance/compliance capabilities for a humanoid robot arm including velocity control incorporate with End-Effector Fixation Control (EEFC). We successfully demonstrated the end-effector positioning to reach the target point in Cartesian coordinate frame based on force control without solving the inverse kinematic problem. The advantage of this function is obvious in moving the end-effector to go to the target point safely because the robot arm can be stopped or disturbed during the movement. The impedance gain can be adjusted adaptively according to the moving trajectory conditions. As the result, the end-effector can reach the presetting target point. We also have experimentally demonstrated the effect of adaptive impedance/compliance control, velocity control and EEFC which are useful in many applications, such as laparoscopic surgery, industrial taper cutting compensation and feeding food to the impaired person.

关键词： Impedance Force Joints Robot kinematics Torque Velocity control

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Confidence Fusion Based Emotion Recognition of Multiple Persons for Human-Robot Interaction

Confidence Fusion Based Emotion Recognition of Multiple Pers...

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IEEE/RSJ international Conference on intelligent robotics and Systems

作者： Ren C. Luo Pei Hsien Lin Li Wen Chang International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University

ISBN: (纸本)9781467317375

Emotional interaction with human beings is desirable for robots. In this study, we propose an integrated system which has ability to track multiple people at the same time, to recognize their facial expressions, and to identify social atmosphere. Consequently, robots can easily recognize facial expression, emotion variations of different people, and can respond properly. In our facial expression recognition scheme, we fuse Feature Vectors based Approach (FVA) and Differential-Active Appearance Model Features based Approach (DAFA) to obtain not only apposite positions of feature points, but also more information about texture and appearance. With the obtained useful information, FVA can classify the emotions according to comparison with the distances and ratios of feature points, and DAFA can distinguish emotions from classical machine learning on a low dimensional manifold space. Furthermore, emotion recognition of multiple people at the same time is extended. Based on the proposed algorithm, multiple person emotion analysis and social atmosphere identification can be achieved, which makes the relationship between people and robots much closer. Experimental results demonstrate that the proposed algorithms can recognize facial expressions accurately and robustly. The ambient atmosphere identification system is implemented with a young Einstein robot head in our laboratory.

关键词： Alignment and Tracking of Facial Features Active Appearance Model (AAM) Differential-AAM features (DAFs) Facial Expression Recognition Human-Computer Interactions Ambient Atmosphere Identification

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Dynamic face recognition system in recognizing facial expressions for service robotics

Dynamic face recognition system in recognizing facial expres...

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IEEE/ASME (AIM) international Conference on Advanced intelligent Mechatronics

作者： Ren C. Luo Pei. H. Lin Yen. C. Wu C. Y. Huang International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University Taiwan

ISBN: (纸本)9781467325752

Facial expression recognition is an essential issue in the field of human and computer interactions. The objective of this paper is to deal with the problem of failure for the feature points on face based on active appearance model (AAM) in fitting with different users. We train the fitting model for different users before recognition. A high-quality AAM alignment results rely on appropriate selections of initial positions. In our method, we apply partial AAM fitting on mouth and eyes. Therefore we could have a better alignment for facial features; as a result, it can implement for tracking real-world video and perform real-time alignment. To obtain stable partial AAM, we use multi-level optical flow to determine initial positions of facial feature models. Then we use the Eigen-faces dynamic face recognition system to recognize different users and select the trained fitting model in recognizing the facial expressions, we combine the methodology of facial expression recognition with Eigen-faces dynamic face recognition. In this paper, we have demonstrated our approach which can accurately recognize different user's facial expressions and emotional variation.

关键词： Active appearance model Face recognition Face Fitting Vectors Optical imaging

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Visual simultaneous localization and mapping using stereo vision with human body elimination for service robotics

Visual simultaneous localization and mapping using stereo vi...

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IEEE/ASME (AIM) international Conference on Advanced intelligent Mechatronics

作者： Ren C. Luo Kuan Yu Chen Ming Hsiao International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University Taipei Taiwan

Generally speaking, vision based simultaneous localization and mapping (V-SLAM) systems have to avoid obstacles especially for the people moving around in the environment because it will cause temporary landmarks captured by camera, and thus will increase computational loading and estimative error in the V-SLAM system. However, in some situations, we cannot always evacuate people when the robot is collecting data. In this paper, we propose a V-SLAM system which consists of human body elimination and verifying that the human body filter can decrease estimative error to the system. We employ FAST machine learning approach to perform corner detection which is faster than most of the other feature detection methods with similar quality for detecting the location of landmarks. We al so implement “landmark buffer” to find robust landmarks in a frame. We also detect where the human body appears in a frame and eliminate the landmark features extracted from human body. Through landmark buffer and human body elimination, our approach derives accuracy estimation of the robot pose. This proposed approach has been successfully demonstrated through experimental verification and the results are summarized in the conclusion.

关键词： Feature extraction Humans Cameras Simultaneous localization and mapping Robot kinematics

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Confidence fusion based Human-Robot Interaction with hand gesture and emotion recognition for service robotics

Confidence fusion based Human-Robot Interaction with hand ge...

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international Symposium on Computational Intelligence and Informatics

作者： Ren C. Luo International Center of Excellence on Intelligent Robotics and Automation National Taiwan University Taiwan

ISBN: (纸本)9781467352055

Hand gesture recognition is an essential way for Human-Robot Interaction (HRI). Sign language is the most intuitive and direct way to communication for impaired or disabled people. Furthermore, emotional interaction with human beings is desirable for robots. In this talk, hand gesture recognition and emotion recognition of an integrated system will be described which has ability to track multiple people at the same time, to recognize their facial expressions, and to identify social atmosphere. Consequently, robots can easily recognize hand gestrure and facial expression with emotion variations of different people, and can respond properly. A combining hand gesture recognition algorithm which combines two distinct recognizers has been studied. These two recognizers collectively determine the hand's gesture via a process called combinatorial approach recognizer (CAR) equation. As for the facial expression recognition scheme, we fuse feature vectors based approach (FVA) and differential-active appearance model features based approach (DAFA) to obtain not only apposite positions of feature points, but also more information about texture and appearance. Experimental results through video demonstration will be included in this talk that the proposed algorithms can recognize hand gesture and facial expressions accurately and robustly.

关键词： expression feature recognition emotion recognition Human machine interaction Gesture recognition Facial Expression Gestures Recognition (Psychology) Hand Speaking Robot Feature Points

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A Study On Iterative Learning Control With Adjustment Of Learning Interval For Monotone Convergence In The Sense Of Sup-Norm

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Asian Journal of Control 2008年第1期4卷

作者： Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.

It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of λ-norm is guaranteed. In this paper, a new ILC algorithm with adjustment of learning interval is proposed to resolve such an undesirable phenomenon, and it is shown that the output error can be monotonically converged to zero in the sense of sup-norm when the proposed ILC algorithm is applied. A numerical example is given to show the effectiveness of the proposed algorithm.

关键词： Iterative learning control monotone convergence learning interval sup-norm

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