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IEEE International Conference on automation Science and Engineering (CASE)

作者： Ren C. Luo Ming Hsiao Che-Wei Liu Center for Intelligent Robotics and Automation Research National Taiwan University Taipei Taiwan

In this paper, we develop an online real-time system using Kinect and inertial measurement unit (IMU) to recognize and measure stairs for stair climbing tasks of erect mobile robots. This system combines the ideas of statistical characteristics of 3D point cloud data and adjustable geometric models for stair recognition tasks. Through this system, we not only verify the advantages of statistics methods in 3D environment perception process, but also realize the idea of using adjustable geometric models to fit 3D perception data correctly and efficiently. Because our stair recognition system keeps good quality results (average errors within ±0.4 cm) and less time consumption (less than 0.1 second per frame) even under dynamic situations, it is expected that it can help mobile robots to achieve online dynamic stair climbing tasks.

关键词： Three-dimensional displays Vectors Solid modeling Data models Sensors Robots Predictive models

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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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Development of robust behaviour recognition for an at-home biomonitoring robot with assistance of subject localization and enhanced visual tracking

Scientific World Journal

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Scientific World Journal 2014年第1期2014卷 280207页

作者： Imamoglu, Nevrez Dorronzoro, Enrique Wei, Zhixuan Shi, Huangjun Sekine, Masashi González, José Gu, Dongyun Chen, Weidong Yu, Wenwei Graduate School of Engineering Chiba University Chiba 263-8522 Japan Research Center for Frontier Medical Engineering Chiba University Chiba 263-8522 Japan Department of Electronic Technology University of Seville Seville 41012 Spain Institute of Robotics and Intelligent Information Processing Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Department of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 China Spanish National Research Council Bioengineering Group Madrid 28500 Spain

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy. © 2014 Nevrez Imamoglu et al.

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Erect Wheel-Legged Stair Climbing Robot for Indoor Service Applications

Erect Wheel-Legged Stair Climbing Robot for Indoor Service A...

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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Ren C. Luo Ming Hsiao Tsung-Wei Lin Center for Intelligent Robotics and Automation Research National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei Taiwan 106

ISBN: (纸本)9781467363563

In this paper, an automatic erect stair climbing mobile robot is developed for indoor service applications. The design of high center of mass, tilt axis near ground, and the triangular wheel-legged structure enable the robot to climb stairs in a dynamic and self-balancing way. The wheel-legged mechanism also keeps the advantages of differential-wheeled mobile platforms when moving on flat ground, such as easy to control, saving power, and zero turning radius. Moreover, the overall mechanical design fits the requirements for a service robot to have proper height and small footprint, which can make human-robot interaction more natural and comfortable in indoor environment. Since the perceptual and control system of the robot are both well integrated, we successfully demonstrate the stair-climbing function and prove that the design and implementation of our work are feasible and efficient. As a result, the robot is expected to be a prototype of universal platform for indoor mobile service robots in the future.

关键词： Erect Wheel-Legged Stair Climbing Robot Indoor Service Applications small footprint Robot control systems INDOORS Service robots Climbing indoor environment Mobile robots ground

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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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Mobile sensor node deployment and asynchronous power management for wireless sensor networks

Mobile sensor node deployment and asynchronous power managem...

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IEEE Transactions on Industrial Electronics

作者： Luo, Ren C. Chen, Ogst Center for Intelligent Robotics and Automation Research National Taiwan University Taipei 10617 Taiwan Department of Electrical Engineering National Chung Cheng University Taipei 10617 Taiwan

Mobile sensor node deployment and power management are important issues in the wireless sensor network system. This study designs a mobile sensor node platform to achieve a highly accurate localization mechanism by using ultrasonic, dead reckoning, and radio frequency information which is processed through a particle filter algorithm. Mobile sensor node with accurate localization ability is of great interest to basic research works and applications, such as sensor deployment, coverage management, dynamic power management, etc. In this paper, we propose an efficient mobile sensor node deployment method, grid deployment, where the map is divided into multiple individual grids and the weight of each grid is determined by environmental factors such as predeployed nodes, boundaries, and obstacles. The grid with minimum values is the goal of the mobile node. We also design an asynchronous power management strategy in our sensor node to reduce power consumption of the sensor network. Several factors such as probability of event generation, battery status, coverage issues, and communication situations have also been taken into consideration. In network communication, we propose an asynchronous awakening scheme so that each node is free to switch on or off its components according to observed event statistics and make a tradeoff between communication and power consumption. The deepest sleep state period is determined by the residual power. By combining these methods, the power consumption of the sensor node can be reduced. © 2006 IEEE.

关键词： Sensor nodes Communication Distributed computer systems Electric network synthesis Energy management Sensor networks Asynchronous power management Basic research Battery status Communication situations Coverage Coverage management Dead reckoning dynamic power management Environmental factors Grid deployment Minimum value Mobile nodes Mobile sensor node Network communications Particle filter algorithms Power managements Radio frequencies Residual power Sensor deployment Sleep state Study design Switch on Wireless sensor

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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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