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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jingyu Sun Lelai Zhou Yi Zhang Guowei Li Shenglin Fan Mingjun Sui Xiaomeng Dai Yibin Li Center for Robotics School of Control Science and Engineering Shandong University Jinan China Ministry of Education Engineering Research Center of Intelligent Unmanned System Jinan China National Innovation Center of High-Speed Rail Technology (Qingdao) Qingdao China China Railway Construction Corporation Bridge Engineering Bureau Group Construction Assemble Technology Corporation Tianjin China China Railway Construction Bridge Engineering Bureau Group Tianjin China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Aiming at the variation of different task focuses of the quadruped robot in response to different contact events, this paper proposes a virtual stiffness adaptive compensation strategy that introduces the perception of proprioceptive disturbance dynamics. The method first relies on floating base dynamics and generalized momentum to establish the estimation method of the disturbance force of the quadruped robot. Then the method of recognizing different contact events in the base and legs of the quadruped robot is established by building band pass filter and isolation filter for estimating the force and by classifying the contact events into hard and soft contacts. Then by combining the method with the virtual model control method of the quadruped robot and swing leg, the recognized contact events can help the robot to change the virtual coefficients, and realized more accurate leg trajectory tracking.

关键词： Legged locomotion Band-pass filters Adaptation models Accuracy Trajectory tracking Force Dynamics

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Egcnet: Edge-Guided Context Real-Time Semantic Segmentation Network

SSRN

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SSRN 2023年

作者： Yu, Kaili Chen, Zhenxue Ye, Mengting Guo, Yixin Wu, Q.M. Jonathan School of Control Science and Engineering Shandong University Jinan250061 China School of Control Science and Engineering Shandong University Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Semantic segmentation is widely needed in many application areas. Driven by the development of deep learning, semantic segmentation methods have made significant progress in accuracy, robustness, and speed. Based on the short-term dense concatenate network, this paper proposes an edge-guided context real-time semantic segmentation network (EGCNet), which further improves the detail branch, extracts edge information from feature maps, and enhances the network’s ability to extract edge information through edge-assisted training. Because the top-level feature map contains minimal detailed information, the network only extracts the boundary of the low-level feature map such that the useful information is utilized as much as possible while reducing the computational load. In addition, we improve the attention-refining module by embedding position information, allowing it to capture the target structure more accurately. EGCNet achieves 71 FPS and 78.6\% mIoU on the Cityscapes dataset at a low computational cost, making it an efficient real-time semantic segmentation network. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Crushing Behavior of Contact-Aided Alsi10mg Sandwich Structure Based on Chiral Mechanical Metamaterials

SSRN

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SSRN 2023年

作者： Xu, Weiyun Zhang, Lei Zhang, Boqin Zhang, Hanyu Liu, Zhao Zhu, Ping The State Key Laboratory of Mechanical System and Vibration National Engineering Research Center of Automotive Power and Intelligent Control School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China School of Design Shanghai Jiao Tong University Shanghai China

Mechanical metamaterials with unique properties inaccessible for their constituent materials have aroused great interest in various fields. As metamaterials with additional screw degrees of freedom, chiral mechanical metamaterials reveal a prosect in impact resistance and energy absorption recently by not only facilitating the bending-dominated characteristics in deformation but also enabling possible energy dissipation through contact among unit cells. However, the corresponding energy absorbing capabilities are still understudied considering more complex deformation modes. In this paper, we systematically investigated the impact of the compression-to-twist effect of chiral metamaterials on the crushing behavior. By introducing intercellular contact during twisting deformation to the design method, AlSi10Mg sandwich structures composed of chiral metamaterials were designed, manufactured, and tested. We demonstrated and discussed the unique deformation mechanism and failure mode which showed a multi-segment mechanical responses to a flat compression, followed by the structure-property relationships with respect to specific energy absorption (SEA) and compressive modulus. The proposed metamaterials with a contact-aided twist indicated a practical way of combining bending-dominated lattices and stretching-dominated ones, paving the road for designing metamaterials with graded energy absorption performance, rigid-flexible coupling properties, multi-functional responses, etc. © 2023, The Authors. All rights reserved.

关键词： Energy absorption

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Anomaly Detection of Logistics Packages Based on CNN and Image Recognition Technology

Anomaly Detection of Logistics Packages Based on CNN and Ima...

引用

Data Science and Computer Application (ICDSCA), IEEE International Conference on

作者： Kun Liang Hang Zhang School of Management Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

To address abnormal situations such as packaging damage, liquid leakage, and item displacement during logistics package transportation, this paper introduces Convolutional Neural Network (CNN) and image recognition technology to achieve real-time anomaly detection and alarm, improving the response speed and accuracy of the logistics system. First, by designing multiple layers of convolution and pooling layers, adding Batch Normalization layers and Dropout layers to prevent overfitting, a CNN deep learning model suitable for logistics package anomaly detection is constructed. Key features are extracted through image preprocessing methods such as normalization and data enhancement to ensure that the model has high recognition adaptability to various abnormal situations such as damage and leakage. Secondly, through data enhancement methods such as rotation, scaling, brightness adjustment, and transfer learning using pre-trained models, the model's adaptability in different lighting, angles, and occlusion conditions is improved. Finally, the model is applied to actual monitoring, and real-time alarms are issued for abnormalities such as damage and leakage, which are then fed back to the system closed-loop management. This method is significantly better than traditional methods in terms of detection accuracy and response speed. Under natural light environment, the model detection accuracy is as high as 98.4%, reducing false positives and false negatives. Combining CNN's anomaly detection technology effectively improves the accuracy and efficiency of logistics monitoring and provides technical support for intelligent logistics management.

关键词： Industries Adaptation models Accuracy Image recognition Data models Real-time systems Convolutional neural networks Anomaly detection Monitoring Logistics

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A Model-Based control Framework for a Novel Combined Octopod Robot

A Model-Based Control Framework for a Novel Combined Octopod...

引用

WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Yi Zhang Lelai Zhou Jingyu Sun Shenglin Fan Guowei Li Mingjun Sui Xiaomeng Dai Yibin Li Center for Robotics School of Control Science and Engineering Shandong University Jinan China Ministry of Education Engineering Research Center of Intelligent Unmanned System Jinan China National Innovation Center of High-Speed Rail Technology (Qingdao) Qingdao China China Railway Construction Corporation Bridge Engineering Bureau Group Construction Assemble Technology Corporation Tianjin China China Railway Construction Bridge Engineering Bureau Group Tianjin China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

The legged robot with a waist structure has a more flexible body structure than that with a rigid body. To address the complexity of the waist structure, this paper proposes a lightweight combined octopod robot consisting of two quadruped robots connected back and forth by a novel detachable docking mechanism. To address the issue of performance loss caused by model predictive control (MPC) in high-dimensional nonlinear systems of octopod robots, a centralized control framework for nonlinear model predictive control (NMPC) and whole body control (WBC) is proposed to optimize the contact forces and joint torques. Apart from that, an attitude adaptation control strategy is designed in order to improve the traversing capability in the steep slope terrain. The combined octopod robot platform and motion control framework have been evaluated through a series of experiments, and the results show that the robot has excellent omnidirectional motion capability, as well as strong terrain adaptability and traversing ability in sloping terrain.

关键词： Legged locomotion Turning Complexity theory Quadrupedal robots Motion control Nonlinear systems Robots

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Newly Developed Universal Analytical Approach to Incomplete Spherical Conformal Contact Problems: Application to Spherical Plain Bearings with Self-Lubricating Fabric Liner

SSRN

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SSRN 2024年

作者： Xue, Yahong Kang, Dongzhuang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China College of Mechanical Engineering Tianjin University of Technology Tianjin300384 China

To address the conformal contact problem in self-lubricating spherical plain bearings bonded with a self-lubricating layer, a universal analytical model for normal contact between friction-less spheres is established. The accuracy and generality of this new model are verified through numerous finite element analyses. Our model overcomes the limitations of elastic half-space and small deformation assumptions. It can accurately calculate the contact stress distribution of self-lubricating spherical bearings bonded with a self-lubricating layer. Based on the new model, the complex non-linear relationship between the variables is being investigated. The model can be extended and applied to the conformal contact problem of spherical contact surfaces in biomechanics and other fields. © 2024, The Authors. All rights reserved.

关键词： Analytical models

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LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement

arXiv

引用

arXiv 2024年

作者： Yang, Haodong Xu, Jisheng Lin, Zhiliang He, Jianping The Department of Computer Science Shanghai Jiao Tong University China The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai200240 China

Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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A depthwise separable convolution based 6D pose estimation network by efficient 2D-3D feature fusion

A depthwise separable convolution based 6D pose estimation n...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Feng, Qi Gu, Chaochen Qin, Jiani Xu, Rui Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665436786

Precise 6D pose estimation of the target object is an essential prerequisite for robots to understand the real world. Previous 6D pose estimation methods based on 3D data usually have problems such as long model training time, imperfect feature extraction, redundant network model parameters, and complicated follow-up processing steps. This paper proposes a 2D-3D feature fusion module that could enhance feature extraction for the 6D pose estimation network. Furthermore, we compress the size of model parameters by adopting depthwise separable convolution to accelerate training speed and to reduce memory consumption. The experiment results on LineMOD dataset show the effectiveness of our method. Our method achieves on par or better performance than the state-of-art methods for 6D pose estimation and reduces model training time and the number of model parameters simultaneously. © 2021 IEEE.

关键词： Convolution

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LSDNet: Boosting change detection of high-resolution remote sensing images by combining convolution-involution and ensemble coordinate attention 11

LSDNet: Boosting change detection of high-resolution remote ...

引用

2021 11th International Workshop on Computer Science and engineering, WCSE 2021

作者： Liu, Yifan Liu, Jingdong Raza, Asif Li, Zeng Huo, Hong Fang, Tao Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9789811817915

Change detection has always been a crucial task in remote sensing fields, and there have already been great efforts made on it for decades. However, as high resolution (HR) remote sensing images generally contain abundant ground details, it still faces a huge challenge for their change detection, especially from the aspects of change detection accuracy and speed. Concerning this issue, a novel lightweight Siamese deep network (LSDNet) is proposed, and it combines Convolution-Involution Module (CIM) and Ensemble Coordinate Attention Module (ECAM) for boosting the change detection of HR remote sensing images. CIM summarizes the context of ground objects and reweights the importance of different positions, while ECAM aggregates multiple levels of semantic features and pays different attention to different spatial information. The experiments on CNZ data set have shown that the proposed LSDNet performs better than state-of-the-art (SOTA) change detection methods, especially it improves the accuracy by 1.92% and reduces the amount of model parameters by 32.89% compared to SNUNet-CD which has the best performance currently. © 2021 11th International Workshop on Computer Science and engineering, WCSE 2021. All Rights Reserved.

关键词： Change detection

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High-Resolution representation object pose estimation from monocular images

High-Resolution representation object pose estimation from m...

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2021 China Automation Congress, CAC 2021

作者： Guan, Qi Li, Wenfa Xue, Shibei Li, Dewei Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665426473

This paper addresses the 6D challenge of monocular 6D pose estimation on small targets and weak texture objects. We propose a monocular object pose estimation algorithm, High Resolution Object Pose Estimation (HRPE) based on the high-resolution feature representation, which detects the 2D keypoints and uses the Perspective-n-Point (PnP) algorithm to compute the 6D pose. HRPE can maintain high-resolution feature representation through parallel convolution of multi-resolution feature branches and repeated cross-scale fusion, generating more precise pose estimation. Experimental results show that HRPE surpasses the existing state-of-the-arts, and achieves a significant accuracy improvement for small objects, which demonstrates the effectiveness of high-resolution feature representation on pose estimation. © 2021 IEEE

关键词： Textures

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