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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Yu, Yingying Cao, Zhiqiang Liang, Shuang Liu, Zhicheng Yu, Junzhi Chen, Xuechao Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Intelligent Robotics Institute BIT Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (纸本)9781728163215

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. © 2019 IEEE.

关键词： Image segmentation

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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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Human-like Walking of a Biped Robot Actuated by Pneumatic Artificial Muscles and Springs*

Human-like Walking of a Biped Robot Actuated by Pneumatic Ar...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Yixiang Liu Qing Bi Xizhe Zang Yibin Li School of Control Science and Engineering Shandong University Jinan China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan China Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Human walking gait is much more natural looking and energy efficient compared with biped robots. This paper presents a bio-inspired approach to realizing more human-like biped robotic walking. For this purpose, a biped robot actuated coordinately by pneumatic artificial muscles and springs is developed, capable of exploiting passive compliance of the mechanical system in locomotion. And a control scheme for human-like walking is designed based on finite state machine. Experiments show that the biped robot can walk stably on the treadmill even with some small obstacles. The realized gait has the important features of human walking, including heel strike and toe off, stretched knees, and variation in the height of the body's center of mass, which demonstrates the effectiveness of the proposed approach.

关键词： Conferences Automation Computer aided software engineering

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

arXiv

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation 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 Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Design and Kinematic Analysis of a 2-DoF Spatial Parallel Mechanism with Flexible Orientation Capability

Design and Kinematic Analysis of a 2-DoF Spatial Parallel Me...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Jiakai Chen Fugui Xie Xin-Jun Liu The State Key Laboratory of Tribology & Tsinghua University (DME)-Siemens Joint Research Center for Advanced Robotics Tsinghua University Beijing China State Key Laboratory of Tribology & Tsinghua University (DME)-Siemens Joint Research Center for Advanced Robotics and Beijing Key Lab of Precision /Ultra-precision Manufacturing Equipments and Control Tsinghua University Beijing China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Mechanisms with flexible orientation capability and compact structure have great application prospects in manufacturing equipment, flight simulator, airborne radar, and similar fields. In this paper, a two-degree-of-freedom (2-DoF) spatial parallel kinematic mechanism with three identical kinematic chains is presented. Due to the strong coupling property, its kinematic mechanism issues in terms of forward and inverse kinematics are fundamental to its practical application and thus have been investigated. Based on the kinematic analysis results, the orientation capability of the presented mechanism can reach 40° for an arbitrary azimuth angle. Benefitting from the parallel kinematic structure and coupling kinematics, the presented mechanism also has potential advantages of high stiffness and quick response.

关键词： Gears Kinematics Mathematical model Solid modeling Airborne radar Couplings Satellite broadcasting

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation 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 Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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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

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

引用

arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Walking of Biped Robot with Variable Stiffness at the Ankle Joint*

Walking of Biped Robot with Variable Stiffness at the Ankle ...

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IEEE International Conference on robotics and Biomimetics

作者： Qing Bi Yixiang Liu Xizhe Zang Rui Song Haibin Wang Weimin Li Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China School of Control Science and Engineering Shandong University Jinan China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Achieving versatile and efficient walking on biped robots is an important research objective. The impact between the foot and the ground has significant effects on the walking stability of biped robots. Research on human walking indicates that the joints in human lower limbs show different stiffness values in different phases of walking. Inspired by humans, this paper presents the realization of biped robot walking with variable stiffness at the ankle joint. A walking control scheme which combines PD control and variable impedance control is designed for the biped robot. Results of dynamic walking simulation verified that variable stiffness at the ankle joint could contribute to the improvement of walking stability and energy efficiency.

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