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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Bin Qi Songyuan Zhang Yili Fu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Leg length, joint maximum torque/speed, motor model and transmission ratio are the most concerning parameters in the leg mechanism design of the legged-robot. The first two parameters are related to the locomotion performance, and the last two determine the actuator’s performance. At the beginning of the design, it is difficult to intuitively grasp the relationship between the leg length, the maximum torque of the joint and the locomotion performance of the legged-robot. When selecting the motor and determining the transmission ratio, these factors impact the control bandwidth and the locomotion performance. Considering the above-mentioned practical problems in the design, this paper proposes a systematic and two-stage sequential leg design framework for the legged-robot, which provides the design guidance in the early stage and the selection of the actuator motor and the determination of the transmission ratio in the later stage. Furthermore, the analytic solution of nonlinear constraint’s gradient was derived. Finally, a case study shows the application of the proposed framework. This work shows that finding an optimal set of design parameters among multiple objectives is not always possible. During design process, it is necessary to weigh the relationship between multiple indexes in many aspects.

关键词： Legged locomotion Actuators Torque systematics Process control Indexes Task analysis

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GCA-Net: A Global Context Aggregation Network for Effective Optical Flow

GCA-Net: A Global Context Aggregation Network for Effective ...

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Annual Conference of Industrial Electronics Society

作者： Tao Xie Jinghan Gao Ke Wang Ruifeng Li State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Optical flow seeks to estimate the per-pixel 2D motion between two frames by identifying corresponding pixels. Current flow estimators typically involve per-pixel feature extraction, multi-scale 4D correlation volume construction, and iterative flow field updates through a Conv-GRU module. However, the locality of convolutional features in these methods renders the calculated correlations vulnerable to different noises. In addition, the Conv-GRU module of these methods is only executed with a convolution layer, which is incapable of exploiting context clues from larger window sizes even inside the image being queried itself, thus raising it more difficult for the model to process images with challenging regions, e.g., textureless areas. To this end, we introduce GCA-Net, a global context aggregation network for credible yet effective optical flow estimation. More specifically, we propose a highly efficient multi-scale transformer (MSFormer) layer which enables the per-pixel feature to aggregate long-range information from other features, hence building more accurate 4D correlation volumes. Besides, we develop an attention-enhanced Conv-GRU block (AttGRU) that can incorporate information alongside a larger context window even within itself empowering our network to estimate optical flow in even the most challenging regions. Experimentally, we demonstrate that GCA-Net outperforms previous state-of-the-art methods by large margins on Sintel (Final) and KITTI 2015 (background and foreground) benchmarks.

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Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking

Poly-MOT: A Polyhedral Framework For 3D Multi-Object Trackin...

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

作者： Xiaoyu Li Tao Xie Dedong Liu Jinghan Gao Kun Dai Zhiqiang Jiang Lijun Zhao Ke Wang State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

3D Multi-object tracking (MOT) empowers mobile robots to accomplish well-informed motion planning and navigation tasks by providing motion trajectories of surrounding objects. However, existing 3D MOT methods typically employ a single similarity metric and physical model to perform data association and state estimation for all objects. With large-scale modern datasets and real scenes, there are a variety of object categories that commonly exhibit distinctive geometric properties and motion patterns. In this way, such distinctions would enable various object categories to behave differently under the same standard, resulting in erroneous matches between trajectories and detections, and jeopardizing the reliability of downstream tasks (navigation, etc.). Towards this end, we propose Poly-MOT, an efficient 3D MOT method based on the Tracking-By-Detection framework that enables the tracker to choose the most appropriate tracking criteria for each object category. Specifically, Poly-MOT leverages different motion models for various object categories to characterize distinct types of motion accurately. We also introduce the constraint of the rigid structure of objects into a specific motion model to accurately describe the highly nonlinear motion of the object. Additionally, we introduce a two-stage data association strategy to ensure that objects can find the optimal similarity metric from three custom metrics for their categories and reduce missing matches. On the NuScenes dataset, our proposed method achieves state-of-the-art performance with 75.4% AMOTA. The code is available at https://***/lixiaoyu20001P0Iy-MOT.

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Cable Length Estimation for a Hyper-Redundant Robot Based on Cable Hole Clearance Calculation 19

Cable Length Estimation for a Hyper-Redundant Robot Based on...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Li, Ziqing Deng, Jiangqin Zheng, Yang Liu, Chao Gu, Guoying Robotics Institute School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China

ISBN: (纸本)9798350320695

For the cable-driven hyper-redundant robot, most of the current drive cable length estimation methods consider that the cable always passes through the center of the hole, and do not consider the effect of cable-hole clearance, resulting in a large motion error. In this paper, we propose a cable length estimating method by considering cable-hole clearance. Based on the simplified space model of the cable hole, it uses the recursive algorithm to obtain the approximate solution of the actual drive cable length. The algorithm can meet the requirements of real-time control while ensuring calculation accuracy. The correctness of the approximate solution is verified by using the interior point penalty function method. Taking a 24DoF hyper-redundant manipulator with joint angle closed-loop control as an example, the effectiveness of the proposed method is proved by comparing the theoretical calculation results with the actual drive cable length. © 2023 IEEE.

关键词： Real time control

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Health Monitoring of Pressure Transmitter Sensing Line Based on Convolutional Neural Network 12

Health Monitoring of Pressure Transmitter Sensing Line Based...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

作者： He, Tianfang Xu, Aidong Wang, Kai Zeng, Peng Laboratory of Industrial Control Network and System State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Industrial Control Network and System State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781839538360

Blockages of pressure transmitter sensing line are a recurring problem in nuclear plants. The traditional solution is to purge them with nitrogen periodically, which is time consuming and radiation intensive. It is thus desirable to monitor the status of sensing lines on-site. The noise analysis method is a promising method for sensing line health monitoring. However, the existing noise analysis methods, which evaluate the sensing line status by analyzing time domain or frequency domain features of process noise signal, are susceptible to working conditions of pressure transmitters. Further, blockage degree of the sensing line can not be determined by existing methods. Therefore, this paper proposes a machine learning based noise analysis method for health monitoring of pressure transmitter sensing line, in which 1-D Convolutional Neural Network (CNN) is used to extract features from process noise signals. Experimental results show that the proposed method achieved satisfactory results in diagnosis accuracy, false alarm rate and robustness of the model under different working conditions. Further, blockage degree of sensing line can also be evaluated. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Frequency domain analysis

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An Aerial Non-contact Detection system For Quayside Crane Using A Hexacopter

An Aerial Non-contact Detection System For Quayside Crane Us...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Zhang, Yehui Wang, Zhaoying Dong, Wei Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China Shanghai Jiao Tong University Robotics Institute School of Mechanical Engineering Shanghai200240 China

ISBN: (纸本)9798350327182

In recent years, aerial robots have been widely used and developed in high-altitude defect detection in academia and business. To solve the detection problem of beams at quayside cranes, reduce the cost of staffing in the traditional manual detection process and improve detection safety, a non-contact detection system using a hexacopter is designed. This article analyzes the task requirements of flaw detection for quayside cranes and the workflow. The Unmanned Aerial Vehicle (UAV) system is equipped with a non-contact detective transmitter, a non-contact detective imaging plate and other task detection equipment to detect the crack of quayside crane beams. The system is based on multi-sensor modules. Besides, the system is designed as light as possible to increase the flight endurance of the UAV. On this basis, the system takes the UAV as the carrier and carries the detection equipment to the location which needs detection. At the same time, people on the ground can control the non-contact detective transmitter and imaging plate to move to the defect area through remote radio control, then can obtain the internal crack. The reliability and effectiveness of the whole system are verified by performing an actual detection experiment with a crane model. © 2023 IEEE.

关键词： Antennas

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A Survey of Underwater Multi-Robot systems

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 1-18页

作者： Ziye Zhou Jincun Liu Junzhi Yu State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and RoboticsBeijing Innovation Center for Engineering Science and Advanced TechnologyCollege of EngineeringPeking UniversityBeijing 100871China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China

As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent *** this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new *** specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their ***,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of *** conclude,we have discussed related promising directions for future *** survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.

关键词： Cooperation formation control multi-robot systems(MRS) taxonomy underwater robots underwater tasks

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HabitAction: A Video Dataset for Human Habitual Behavior Recognition

arXiv

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arXiv 2024年

作者： Li, Hongwu Zhang, Zhenliang Wang, Wei The State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China Beijing100080 China

Human Action Recognition (HAR) is a very crucial task in computer vision. It helps to carry out a series of downstream tasks, like understanding human behaviors. Due to the complexity of human behaviors, many highly valuable behaviors are not yet encompassed within the available datasets for HAR, e.g., human habitual behaviors (HHBs). HHBs hold significant importance for analyzing a person’s personality, habits, and psychological changes. To solve these problems, in this work, we build a novel video dataset to demonstrate various HHBs. These behaviors in the proposed dataset are able to reflect internal mental states and specific emotions of the characters, e.g., crossing arms suggests to shield oneself from perceived threats. The dataset contains 30 categories of habitual behaviors including more than 300,000 frames and 6,899 action instances. Since these behaviors usually appear at small local parts of human action videos, it is difficult for existing action recognition methods to handle these local features. Therefore, we also propose a two-stream model using both human skeletons and RGB appearances. Experimental results demonstrate that our proposed method has much better performance in action recognition than the existing methods on the proposed dataset. All the dataset and codes are available at ***/view/hhb30. Copyright © 2024, The Authors. All rights reserved.

关键词： Video analysis

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Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 722-736页

作者： Da Yu Mingyi Zhang Mantian Li Fusheng Zha Junge Zhang Lining Sun Kaiqi Huang the State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin 150080China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190the School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100049 the CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China IEEE

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter *** this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new ***,prior knowledge is unavailable during training and *** works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory *** better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous ***,besides the previous raw samples,we store the corresponding logits and features in the ***,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the *** the same number of samples for replay,our method can use more past knowledge to prevent *** conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic *** further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

关键词： Catastrophic forgetting class-incremental learning continual learning(CL) experience replay

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A Contactless Magnetic Frequency-Boosting Mechanism for Wind Energy Harvesting

A Contactless Magnetic Frequency-Boosting Mechanism for Wind...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Yunfei Li Manjuan Huang Tianyi Tang Heng Zhao Lining Sun Huicong Liu State Key Laboratory of Robotics and System Harbin Institute of Technology (HIT) CHINA School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University CHINA

ISBN: (数字)9798350380200

ISBN: (纸本)9798350380217

This paper presents a non-contact magnetic frequency-boosting wind energy harvester suitable for low wind speed environments, designed to power various IoT sensors. The device comprises a driving component, a driven component, and a stator component. These components work together to convert low-speed rotational motion caused by wind into high-speed rotational motion, thereby achieving high-power electromagnetic energy output. Performance tests have shown that the output voltage increases with wind speed, reaching a peak voltage of 27V and a peak power of 1.6W at a wind speed of 10 m/s. The device has successfully powered LED bulbs and Bluetooth sensors, demonstrating its practicality for real-time environmental monitoring applications.

关键词： Temperature sensors Wind energy Wind speed Magnetic sensors Voltage Stators Sensor systems Environmental monitoring Electromagnetics Power generation

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