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作者： Shan, Jichao Li, Xiuzhi Jia, Songmin Lu, Xingyang Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781538636749

To measure the velocity for the omni-directional intelligent wheelchair for the depth research on control and improve the measured accuracy and computational efficiency, the variational optical flow based velocity estimation method is used in this paper. Firstly, an improved energy functional model which combines a brightness constancy assumption, a gradient constancy assumption, a Laplacian constancy assumption and a discontinuity-preserving spatio-temporal smoothness constraint is employed to improve the accuracy of optical flow estimation;Then, a planar optical flow model based improved voting Random Sample Consensus(RANSAC) method is presented to remove heterogeneous optical flow vectors produced by non-uniform ambient illumination, local motion blur and chaotic surrounding environment. Finally, aiming at the installation attitude of the camera, an effective method of camera attitude adjustment is introduced to simplify the spatial coordinate system transformation and solve the model. This paper has realized the velocity estimation of the wheelchair by real-time acquisition of images in the framework of the Robot Operating system(ROS). Experimental results showed that the wheelchair velocity estimation method performed robustly in the presence of chaotic surrounding environment through the above improvements. © 2017 IEEE.

关键词： Machine vision Motion estimation Optical flow ROS Voting RANSAC

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A method of cliff detection in robot navigation based on multi-sensor

A method of cliff detection in robot navigation based on mul...

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IEEE International Conference on Real-time computing and Robotics (RCAR)

作者： Zhilong Su Can Wang Yuxiao Li Xinyu Wu 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 Technolog Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper proposed a method to detect cliff edge by using an RGB-D camera, and then to fuse the cliff information with LiDAR, finally to provide navigation information for robot autonomous exploration and autonomous movement. Compared with the prior methods which set sonar or infra-red sensor in the bottom of the robot, our method could detect cliff earlier by set the RGB-D camera forward. In cliff detection, we use region growing algorithm and the normal vector of plane to segment out ground plane in point cloud, then extract the boundary points of ground, and we proposed an Edge Point Filter (EPF) to get the edge points of cliff, then use the edge points to simulate LiDAR data and combine with real LiDAR data to mark the untraversable area. We verified the practicability of this method in simulation, and verified the accuracy with real sensors.

关键词： Laser radar Robots Three-dimensional displays Robot sensing systems Indexes Cameras Image edge detection

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Adaptive GridMap Building Algorithm for Mobile Robot Based on Multiway Tree

Adaptive GridMap Building Algorithm for Mobile Robot Based o...

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2018 International Conference on Electrical Engineering, Control and Robotics, EECR 2018

作者： Zhou, Jing Ruan, Xiaogang Dong, Pengfei Zhang, Jingjing Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

In order to solve the problem that uniform grid map occupies large storage space in the process of large-scale map creation, a new adaptive grid map building algorithm based on multiway tree is proposed. The environment map is divided into nine grids. The algorithm determines whether the grid is completely occupied, partially occupied or vacant. Then the part occupied gridsare further subdivided into smaller grids. The algorithm continues to determine whether the smaller grid is completely occupied, partially occupied or *** the above segmentation process until the entire map search is completed and the accuracy requirements are met. Finally, the scale of grid map does not depend on human experience but presenting adaptive characteristics. The algorithm is compared with the uniform grid *** simulation results show that the algorithm has *** with the uniform scale raster map, itgreatly saves the storage space. © The Authors, published by EDP Sciences, 2018.

关键词： Forestry

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Position-based visual servo control of intelligent wheelchair/bed docking

Position-based visual servo control of intelligent wheelchai...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Li, Xiuzhi Liang, Xingnan Fan, Jinhui Jia, Songmin Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781538680698

For the automatic docking problem of a Mecanum-wheeled omnidirectional intelligent wheelchair/bed system, a visual servo control method which is based on position feedback is proposed in this paper. When it is docked with the auxiliary bed, the intelligent wheelchair firstly collects and identifies special landmark on the auxiliary bed through the vehicle vision system;Then the relative pose between the wheelchair and the auxiliary bed is determined according to the transformation between the camera coordinate system and the bed coordinate system based on an artificial landmark;Finally, the intelligent wheelchair is controlled to travel from the initial position to the desired position by position-based visual servo (PBVS) control method and completes docking task between the intelligent wheelchair and the auxiliary bed. The implementation of visual docking control for the intelligent wheelchair is based on the Robotic Operating system (ROS) platform. Experimental results are presented to validate the practicality and effectiveness of the proposed method in this paper. © 2018 IEEE.

关键词： Visual servoing

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A Micro SLAM system Based on ORB for RGB-D Cameras

A Micro SLAM System Based on ORB for RGB-D Cameras

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2018 International Conference on Electrical Engineering, Control and Robotics, EECR 2018

作者： Wang, Fei Ruan, Xiaogang Dong, Pengfei Sie, Ouattara Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

In this paper, a micro SLAM system based on ORB features for RGB-D cameras has been proposed. With only a RGB-D sensor, this method can be applied in small environment for localization and mapping. Furthermore, the task of 3D reconstruction can also be accomplished by using the approach. The pose graph based on Bundle Adjustment is adopted for reducing the estimation error. In order to further speed up computing to meet the requirement of real-time, we have proposed the piecewise optimization strategy. The approach is evaluated on public benchmark datasets. Compared with several state-of-the-art scheme, this method has proven to work well in these environments. © The Authors, published by EDP Sciences, 2018.

关键词： Cameras

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A cognitive map learning model based on hippocampal place cells 2018

A cognitive map learning model based on hippocampal place ce...

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4th International Conference on Mechatronics and Robotics Engineering, ICMRE 2018

作者： Chai, Jie Ruan, Xiaogang Huang, Jing Zhu, Xiaoqing Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781450363655

Aiming at the environment cognition and navigation problem of autonomous mobile robots in unknown environment, a cognitive map learning model is proposed based on hippocampal place cells, which can memorize and map surroundings. The model uses the self-organizing feature map as the basic structure. Each hippocampal place cell is represented by a neural node. The robot builds up the hippocampal place cells layer through environment exploration. The simulation results show that the model has self-learning ability, which enables robots to acquire environment knowledge and establish a complete cognitive map gradually like human beings and animals, making the robot's environment cognition and navigation process become more bionic and intelligent. © 2018 Association for computing Machinery.

关键词： Cells

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Simultaneous Localization and Mapping of Mobile Robot Based on Improved RBPF

Simultaneous Localization and Mapping of Mobile Robot Based ...

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2018 International Conference on Electrical Engineering, Control and Robotics, EECR 2018

作者： Zhang, Jinging Ruan, Xiaogang Dong, Pengfei Zhou, Jing Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

The traditional SLAM based on RBPF has the problem of constructing high-precision map which requires large amounts of particles to make the calculation complexity and the phenomenon of particle depletion caused by particle degradation. Aiming at these problems, an improved RBPF particle filter based on adaptive bacterial foraging optimization algorithm and adaptive resampling is proposed for mobile robot SLAM problem. Firstly, the introduction of adaptive bacterial foraging algorithm to RBPF making the distribution of particles before resampling closer to the real situation. Then use the adaptive resampling method makes the newly generated particles closer to the real movement, thereby increasing the robot position estimation accuracy and map creation accuracy. The experimental results show that this method can improve the practicability of the system, reduce the computational complexity, improve the operation speed and get more effective particles while guaranteeing the accuracy of the grid map. © The Authors, published by EDP Sciences, 2018.

关键词： Mobile robots

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Biologically Inspired Blind Quality Assessment of Tone-Mapped Images

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2525-2536页

作者： Yue, Guanghui Hou, Chunping Gu, Ke Mao, Shasha Zhang, Wenjun Tianjin University Tianjin China Tianjin University Tianjin China Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing China Nanyang Technological University Singapore Shanghai Jiao Tong University Shanghai China

Currently, many tone mapping operators (TMOs) have been provided to compress high dynamic range images to low dynamic range (LDR) images for visualizing them on the common displays. Since quality degradation is inevitably induced by compression, how to evaluate the obtained LDR images is indeed a headache problem. Until now, only a few full reference (FR) image quality assessment metrics have been proposed. However, they highly depend on reference image and neglect human visual system characteristics, hindering the practical applications. In this paper, we propose an effective blind quality assessment method of tone-mapped image without access to reference image. Inspired by that the performance of existing TMOs largely depend on the brightness and chromatic and structural properties of a scene, we evaluate the perceptual quality from the perspective of color information processing in the brain. Specifically, motivated by the physiological and psychological evidence, we simulate the responses of single-opponent (SO) and double-opponent (DO) cells, which play an important role in the processing of the color information. To represent the textural information, we extract three features from gray-level co-occurrence matrix (GLCM) calculated from SO responses. Meanwhile, both GLCM and local binary pattern descriptor are employed to extract texture and structure in the responses of DO cells. All these extracted features and associated subjective ratings are learned to reveal the connection between feature space and human opinion score. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art blind quality assessment methods and is comparable with the popular FR methods on two recently published tone-mapped image databases.

关键词： High dynamic range (HDR) image quality assessment (IQA) no/blind reference (NR) tone mapping operator (TMO)

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An Enhanced Fault Diagnosis Method with Uncertainty Quantification Using Bayesian Convolutional Neural Network

An Enhanced Fault Diagnosis Method with Uncertainty Quantifi...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Qihang Fang Gang Xiong Xiuqin Shang Sheng Liu Bin Hu Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Fault diagnosis is a vital technique to pinpoint the machine malfunctions in manufacturing systems. In recent years, the deep learning techniques greatly improve the fault detection accuracy, but there still remain some problems. If one fault is absent in the training data or the fault signal is disturbed by severe noise interference, the fault classifier may misjudge the health state. This problem limits the reliability of the fault diagnosis in real applications. In this paper, we enhance the fault diagnosis method by using Bayesian Convolutional Neural Network (BCNN). A Shannon entropy-based method is presented to quantify the prediction uncertainty. The BCNN turns the deterministic predictions to probabilistic distributions and enhances the robustness of the fault diagnosis. The uncertainty quantification method helps to indicate the wrong predictions, detect unknown faults, and discover the strong disturbances. Then, a fine-tuning strategy is applied to enhance the model performance further. The potential usability of the proposed method in monitoring the motors of 3D printers is studied. And the experiment is conducted on a motor bearing dataset provided by Case Western Reserve University. The proposed BCNN achieves 99.82% fault classification accuracy over nine health conditions. Its robustness is verified by comparing the testing accuracy with three other methods on the noisy datasets. And the uncertainty quantification method successfully detects the outlier inputs.

关键词： Fault diagnosis Uncertainty Bayes methods Circuit faults Robustness Predictive models Noise measurement

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A Hierarchical Fusion Strategy Based on EEG and sEMG for Human-Exoskeleton system

A Hierarchical Fusion Strategy Based on EEG and sEMG for Hum...

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IEEE International Conference on Real-time computing and Robotics (RCAR)

作者： Mengyao Li Shengcai Duan Yao Dong Can Wang Wei Feng Xinyu Wu Guangdong Provincial Key Laboratory 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 Chinese Academy Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

Most of the human spinal cord injuries result in lower-limb disability. To integrate the brain's holistic motion intention with sEMG's control reliability for lower-limb motion neurorehabilitation with the exoskeleton, an ingenious hierarchical fusion strategy based on EEG and sEMG for ambulation with obstacle crossing task was proposed. Specifically, EEG delivers senior task-specific commands to execute refined locomotion while the specific implementation is determined by sEMG. The practical feasibility of ridge regression-based EEG decoding was proved firstly via offline and online methods involving 3 subjects, with subsequent online verification of the proposed strategy yielding higher than 85% of average accuracy. Furthermore, one subject was invited to wear the exoskeleton for the practical application of the strategy.

关键词： Electroencephalography Task analysis Exoskeletons Switches Legged locomotion Feature extraction Eigenvalues and eigenfunctions

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