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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Li, Hao Wang, Zhiyang Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9781728163215

Obstacle avoidance is a key problem of robot motion planning. This paper introduces an improved method to realize obstacle avoidance of manipulators based on artificial potential field method which offers a relatively fast and effective way to solve the problem. We establish the attractive potential field function of the goal and the repulsive potential filed functions of obstacles in the workspace instead of the configuration space. Then a series of control points on the manipulator are chosen to be influenced by the total potential. A policy is proposed that only the end point is attracted to the goal while all selected points are repulsed by obstacles. Afterwards, we transfer the potential force of each point in workspace into the moment in configuration space to make the manipulator move by iteration of joint angles. The effectiveness of the method is confirmed by simulation experiments. © 2019 IEEE.

关键词： Motion planning

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Planning and execution of dynamic whole-body locomotion for a hydraulic quadruped on challenging terrain

arXiv

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arXiv 2019年

作者： Winkler, Alexander W. Mastalli, Carlos Havoutis, Ioannis Focchi, Michele Caldwell, Darwin G. Semini, Claudio Department of Advanced Robotics Istituto Italiano di Tecnologia via Morego 30 Genova16163 Italy Agile & Dexterous Robotics Lab Institute of Robotics and Intelligent Systems ETH Zurich Switzerland

— We present a framework for dynamic quadrupedal locomotion over challenging terrain, where the choice of appropriate footholds is crucial for the success of the behaviour. We build a model of the environment on-line and on-board using an efficient occupancy grid representation. We use Any-time-Repairing A* (ARA*) to search over a tree of possible actions, choose a rough body path and select the locally-best footholds accordingly. We run a n-step lookahead optimization of the body trajectory using a dynamic stability metric, the Zero Moment Point (ZMP), that generates natural dynamic whole-body motions. A combination of floating-base inverse dynamics and virtual model control accurately executes the desired motions on an actively compliant system. Experimental trials show that this framework allows us to traverse terrains at nearly 6 times the speed of our previous work, evaluated over the same set of trials. Copyright © 2019, The Authors. All rights reserved.

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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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Modular Design Solutions of BIT Wheelchair for Motion Assistance

Modular Design Solutions of BIT Wheelchair for Motion Assist...

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IEEE Workshop on advanced robotics and its Social Impacts, ARSO

作者： Gao Huang Marco Ceccarelli Weimin Zhang Qiang Huang Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology 5 Nandajie Zhongguancun Haidian Beijing China Beijing Advanced Innovation Center for Intelligent Robotics and Systems Beijing Institute of Technology Beijing China

The BIT wheelchair design is presented with its modular design features for its user-oriented application at home, in exercising centers, and in hospital frames. It can be used in different solutions for different levels of motion assistance combining locomotion, exercising, and rehabilitation. The three design features are presented as function of the operation of the equipment with mechatronic solutions in modular design structures.

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A Quantifiable Muscle Fatigue Method Based on sEMG during Dynamic Contractions for Lower Limb Exoskeleton

A Quantifiable Muscle Fatigue Method Based on sEMG during Dy...

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

作者： Shengcai Duan Can Wang Yuxiao Li Lufeng Zhang Ye Yuan 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-Synergy Systems Shenzhen Institutes of Advanced Technology University of Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

advanced interactive methods between users and lower-limb exoskeletons are gradually drawing attention. Muscle fatigue is regarded as a significant contributory factor in muscle injuries and low work efficiency which is harmful to human-robot interactions. The detection and quantification of muscle fatigue during dynamic contractions remain as a challenging research. In this research, we introduce a novel method called Wavelet Packet Energy Entropy (WPEE) to quantify the muscle fatigue based on sEMG during dynamic contractions. Fatigue experiments and exoskeleton experiments are executed and three healthy subjects complete these experiments. sEMG signals from six muscles of arms are selected and analyzed. The results derived from fatigue experiments validate that the introduced method is better at quantifying the muscle fatigue compared with traditional method mean power frequency (MNF). WPEE is applied in exoskeleton experiments to investigate the arms' muscle fatigue under three conditions. The quantitative results detailly express the user's muscle fatigue condition and fatigue trends when the user is using an exoskeleton with crutches. Considering the muscle fatigue will contribute to ergonomic design and high-quality interactive control of exoskeleton.

关键词： Ergonomics Exoskeletons Muscles Fatigue Market research Wavelet packets Entropy

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Mobile robot localization based on optical sensor

Mobile robot localization based on optical sensor

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

作者： Liu, Yang Ou, YongSheng Han, WeiChao Wuhan University of Technology Wuhan430070 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781728137261

This paper presents a method of using optical sensor for mobile robot localization, which improves the accuracy of position estimation by using extended Kalman filter to fuse three kinds of sensor data. By installing the optical mouse sensor on the edge of the robot's central axis, the linear and angular speed of the robot can be calculated. These help to solve the problem of wheel-slip and the accumulation error of yaw angle that obtained by low-cost MEMS IMU (Inertial Measurement Unit). An extended Kalman filter method that fuse the data from wheel encoder, IMU and optical mouse sensor is implemented. Eventually, experiments of position estimation were carried out on our own two-wheel drive robot. By comparing the accuracy of fusion algorithm in many ways, it indicated that the location result that of three kinds of sensors is better than that of one or two sensors in traditional way, and the error of position estimation is generally within 0.6%. © 2018 IEEE

关键词： Extended Kalman filters

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A Grasping CNN with Image Segmentation for Mobile Manipulating Robot

A Grasping CNN with Image Segmentation for Mobile Manipulati...

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IEEE International Conference on robotics and Biomimetics

作者： Yingying Yu Zhiqiang Cao Shuang Liang Zhicheng Liu Junzhi Yu Xuechao Chen State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Intelligent Robotics Institute BIT Beijing China

This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FPN network combined with point cloud clustering is used to obtain the mask of the target object. Then, the grayscale map and the depth map corresponding to the target object are combined and sent to the DrGNet network for providing multi-scale images. On this basis, depthwise separable convolution is used for encoding. The results of encoders are refined according to the light-weight RefineNet as well as sSE, which can achieve a better grasp detection. The proposed method is verified by the experiments on mobile manipulating robot.

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Semantic Segmentation Model for Road Scene Based on Encoder-Decoder Structure

Semantic Segmentation Model for Road Scene Based on Encoder-...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Peng, Yuanzhe Han, Weichao Ou, Yongsheng 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-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Semantic segmentation as a pixel-wise segmentation task provides rich object information, which is an important research topic in robotic perception. It has been widely applied in many fields, such as autonomous driving and robot navigation. In the application of understanding road scene, the semantic segmentation model should accurately describe the appearance and shape of different categories of objects. In addition, the semantic segmentation model need to understand the spatial relationships between different categories. In order to improve the performance of semantic segmentation model for road scene, we present a model based on encoder-decoder structure with dilated convolution. We apply this model on the Cityscapes dataset and compare it with other classical models. To assess performance, we rely on the standard Jaccard Index IoU (Intersection over Union) and mIoU (mean Intersection over Union). The experimental results verify that this model can effectively improve the performance of semantic segmentation and meet the requirements for road scene. © 2019 IEEE.

关键词： Semantic Segmentation

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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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End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences

End-to-End 3D Point Cloud Learning for Registration Task Usi...

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2020 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Huanshu Wei Zhijian Qiao Zhe Liu Chuanzhe Suo Peng Yin Yueling Shen Haoang Li Hesheng Wang The Chinese University of Hong Kong T Stone Robotics Institute Hong Kong Shanghai Jiao Tong University China University of Cambridge United Kingdom Carnegie Mellon University Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and it's even harder in the absence of any initial estimation information. In this paper, we present an end-to-end deep-learning based approach to resolve the point cloud registration problem. Firstly, the revised LPD-Net is introduced to extract features and aggregate them with the graph network. Secondly, the self-attention mechanism is utilized to enhance the structure information in the point cloud and the cross-attention mechanism is designed to enhance the corresponding information between the two input point clouds. Based on which, the virtual corresponding points can be generated by a soft pointer based method, and finally, the point cloud registration problem can be solved by implementing the SVD method. Comparison results in ModelNet40 dataset validate that the proposed approach reaches the state-of-the-art in point cloud registration tasks and experiment resutls in KITTI dataset validate the effectiveness of the proposed approach in real applications.

关键词： Three-dimensional displays Aggregates Pose estimation Feature extraction Task analysis intelligent robots

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