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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Jiqing Li Jianghua Duan Yongsheng Ou Sheng Xu Zhiyang Wang Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

For the traditional image-based visual servoing (IBVS) system, the desired configurations of grasping are predefined for the robot and the gain λ in the control law is fixed. While training the robots with new skills, the predefined grasping configurations may increase the workload; meanwhile, the fixed controller gain λ may slow down the convergence of the system. To this end, in this paper, an intelligent IBVS system using learning-based grasping configurations and adaptive controller gain is presented. With the Gaussian Mixed Model (GMM), the robot is capable of learning the desired grasping configurations through a certain number of demonstrations. To accelerate the convergence of the IBVS system, a fuzzy logic (FL) unit is applied to adjust the gain λ. The simulation results demonstrate that the proposed approach enables the IBVS system gain the learning ability on grasping configurations, besides accelerating the convergence.

关键词： Grasping Robots Fuzzy logic Robot kinematics Convergence Cameras Adaptation models

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Haptic and visual enhance-based motor imagery BCI for rehabilitation lower-limb exoskeleton

Haptic and visual enhance-based motor imagery BCI for rehabi...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Duan, Shengcai Wang, Can Li, Mengyao Long, Xingguo Wu, Xinyu Feng, Wei Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Chinese Academy of Sciences China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163215

The motor imagery (MI) based on electroencephalogram (EEG) via non-invasive Brain Computer Interface (BCI) is a promising interactive method to provide effective communication between the disabled and rehabilitation robotic device. Interactive efficiency of MI-based BCI largely relies on the performance of subject's motor imagery. In this work, we proposed a simple and effective paradigm based on visual and haptic modalities for a high-quality initialized and calibrated MI-BCI model. Five healthy male subjects participated in motor imagery experiments with two paradigms. The proposed paradigm utilized mixed cues based on the visual and haptic modalities where the haptic cues were provided by the mini vibration motors, and a traditional paradigm utilized the ordinary arrow visual cues. The analysis of the topographic maps was conducted to illustrate the differences of the EEG in both paradigms. The average classification accuracy of motor imagery in the proposed paradigm improved about 14% compared to the traditional paradigm. Furthermore, a subject completed the qualitative verification experiment of the trained model on a rehabilitation lower-limb exoskeleton which proved the feasibility of the proposed paradigm. © 2019 IEEE.

关键词： Brain computer interface

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Learning Stable Control for a Wheeled Inverted Pendulum with Fast Adaptive Neural Network

Learning Stable Control for a Wheeled Inverted Pendulum with...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yuanzhe Peng Yongsheng Ou Wei Feng Chinese Academy of Sciences (CAS) Shenzhen Institutes of Advanced Technology (SIAT) Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

Since it is difficult to work out a complete and accurate analytical model for some complex nonlinear dynamical systems, we mainly propose a method for learning stable control with fast adaptive neural network. Furthermore, we provide a rigorous analytical evaluation of our proposed method in terms of stability. In order to validate the theoretical analysis, our proposed method has been applied in the stable control of a wheeled inverted pendulum (WIP) platform. It achieves high prediction accuracy and fast learning speed. In addition, the proposed method has incremental learning capabilities. When a new instance is provided, there is no need to retrain the entire training set, thus, the controller is more effective and more generalized.

关键词： Neural networks Adaptation models Mathematical model Analytical models Real-time systems Lead Nonlinear dynamical systems

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Development of an assist upper limb exoskeleton for manual handling task

Development of an assist upper limb exoskeleton for manual h...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Yan, Zefeng Yi, Haoyuan Du, Zihao Huang, Tiantian Han, Bin Zhang, Lufeng Peng, Ansi Wu, Xinyu State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology China CAS Key Laboratory of Human-Manchine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Science Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

In this paper we describe the construction of a wearable upper arm exoskeleton to provide support for industrial workers who engaged in repetitive handling tasks and patients who full or partial loss of function in their upper limb due to accident injuries, spinal cord and strokes. We use flexible rope driven by a motor to realize the shoulder and elbow joints linkage, use ball screw to realize the self-locking in any position to providing assist for users. This novel design not only saves energy but also make the whole system portable. The total weight of the prototype is only 2 kg. The exoskeleton is connected upper limb with 3 bandages, and users take about 30 seconds to put it on and 20 seconds to put the exoskeleton off without any help. The PD adaptive control method is applied to the proposed exoskeleton to track expected trajectories. Five healthy people participated in data acquisition. Upper limb muscle integrated electromyography (IEMG) of the deltoid muscle(DM), the biceps femoris muscle (BFW), the femoral muscle (FM), the femoral wrist flexor (FWF) were compared in continuous handling for four different loads (0, 1.5, 3, 4.5kg). The average assistance efficiency were 39.1%, 41.5%, 36.5% and 40.6% for the DM, BFM, FM and FWF respectively. The experimental data show that the upper limb exoskeleton can provide assist for workers and protect their upper limbs injuries in long-term handling task. © 2019 IEEE.

关键词： Muscle

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A Trajectory Optimization Algorithm for Drone Target Localization and Tracking

A Trajectory Optimization Algorithm for Drone Target Localiz...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Xiufeng Han Can Wang Yuxiao Li Xinyu Wu Nanchang Hangkong University Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Accurate positioning is the premise for the drone to successfully capture the target, this paper proposes a solution to the positioning failure caused by the relatively severe jitter of the drone and the target. In this paper, the Improved Kernel Correlation Filter (KCF) is used for target tracking, and the improved Kalman filter is used to smooth the input signal in real time, which reduces the continuous jitter of the drone. The advantage of this approach is that it will not cause misjudgment from the deviation where the measured value is too large. Further, the hysteresis is also significantly improved. The experimental works are carried out on a M100 UAV to verify the improved KCF and the improved Kalman algorithm, respectively. The results showed the effectiveness of the proposed approach.

关键词： Target tracking Cameras Drones Kalman filters Trajectory Real-time systems

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Visual SLAM based on geometric cluster matching

Visual SLAM based on geometric cluster matching

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Tian, Chaoran Ou, Yongsheng Qu, Yangyang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University OfChinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes OfAdvanced Technology Chinese Academy OfSciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728137261

The approach of feature-based visual SLAM is very popular in Micro Aerial Vehicle (MAV) navigation and wheeled mobile robot (WMR) navigation. Most modern feature-based visual SLAM frameworks extract a fixed number of feature points and then match the feature points by brute force or adopt Fast Library for Approximate Nearest Neighbors (FLANN). The features' extracting and matching processes will determine the localization accuracy and mapping quality significantly. In this paper, we propose a feature matching algorithm for the robot equipped with a binocular camera, which provides accurate real-time localization performance and robustness. In our feature matching method, both the geometric relationship of image feature points and the similarity of descriptor are considered. This paper presents a visual SLAM framework based on geometric cluster matching and the effectiveness of the proposed method is verified by practical experiments. © 2019 IEEE.

关键词： Mobile robots

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Binocular depth estimation using convolutional neural network with siamese branches

Binocular depth estimation using convolutional neural networ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Liu, Guodong Jiang, Guolai Xiong, Rong Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two sub-networks. The first sub-network is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. The second is a fully convolutional deep network, which reconstructions the depth map to original resolution. The two sub-networks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods. © 2019 IEEE.

关键词： Convolution

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An Iterative unsupervised Person Search Algorithm on Natural Scene Images

An Iterative unsupervised Person Search Algorithm on Natural...

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Chinese Automation Congress (CAC)

作者： Sisi Cao Yuehu Liu School of Software Engineering Xi’an Jiaotong University Xi’an China Key Lab of Digital Technology and Intelligent System of Shaanxi Province Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China

Person search is a challenging task due to the different requirements of annotations between person detection and Re-identification. In general, person search methods use the supervised person Re-identification methods, where abundant identity labels of the bounding boxes are essential. However, most person images are unlabeled in the real-world scenario and it is unpractical to annotate the abundant fine-grained labels for unlabeled images. Obviously, the existing supervised methods are not appropriate with the real-world scenario. Therefore, we propose an unsupervised learning method for person search in this paper, which contacts two parts: one is unsupervised person detection and the other is unsupervised person Re-identification. The experimental results on two well-known datasets, CUHK-SYSU and PRW, indicate that proposed method achieves competitive performance than the state-of-art unsupervised methods. Note that proposed method has greater practical significance even though it does not get the results as good as the general supervised methods.

关键词： Training Clustering algorithms Noise measurement Task analysis Search methods Feature extraction Unsupervised learning

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FIST-Toy: A benchmark dataset for toy component classification

FIST-Toy: A benchmark dataset for toy component classificati...

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Qi, Chongchong Gong, Libo Cheng, Jun Tao, Dapeng FIST LAB School of Information Science and Engineering Yunnan University Kunming Yunnan650091 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Yunnan Rural Science and Technology Service Center Kunming Yunnan650000 China Chinese University of Hong Kong Hong Kong999077 Hong Kong

ISBN: (纸本)9781728137261

As a comprehensive technology of machinery, electronics and computer vision, industrial automation technology has been attracting extensive attention and developed rapidly recently years. The refinement of target classification plays an important role in the industrial automation, therefore, many computer vision based target classification methods have been proposed. However, there is few real industrial dataset can be used to evaluating the target classification algorithms. In this paper, we construct a novel toys dataset, named FIST-Toy, including 7411 images of 31 categories. Base on the proposed dataset, we conduct a comprehensive study of the state-of-the-art pretraining target classification models qualitatively and quantitatively. In addition, we propose a weight voting based classification neural network. By comparing with AlexNET, VggNet, Inception and ResNet, the proposed model outperforms in fine-grading. We evaluate the FIST-Toy dataset using various deep learning models. And the experimental results show that the FIST-Toy dataset provides a benchmark to evaluate the performance of different methods for target classification. © 2019 IEEE.

关键词： Computer vision

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Correction to: Integrating Social Assistive Robots, IoT, Virtual Communities and Smart Objects to Assist at-Home Independently Living Elders: the MoveCare Project

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International Journal of Social robotics 2022年第9期14卷 2045-2045页

作者： Luperto, Matteo Monroy, Javier Renoux, Jennifer Lunardini, Francesca Basilico, Nicola Bulgheroni, Maria Cangelosi, Angelo Cesari, Matteo Cid, Manuel Ianes, Aladar Gonzalez-Jimenez, Javier Kounoudes, Anastasis Mari, David Prisacariu, Victor Savanovic, Arso Ferrante, Simona Borghese, N. Alberto Applied Intelligent System Lab Department of Computer Science University of Milan Milano Italy Machine Perception and Intelligent Robotics Group (MAPIR) Department of System Engineering and Automation University of Malaga and Instituto de Investigación Biomédica de Málaga–IBIMA Malaga Spain Machine Perception and Interaction Lab Örebro University Örebro Sweden NearLab Department of Electronics Information and Bioengineering Politecnico di Milano Milano Italy AB.Acus SRL Milan Italy University of Manchester Manchester UK IRCCS Istituti Clinici Scientifici Maugeri University of Milan Milan Italy Junta de Extremadura Merida Spain Korian Milan Italy SignalGeneriX Limassol Cyprus EURECAT Barcelona Spain University of Oxford Oxford UK Smart Com Ljubjana Slovenia

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