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Disturbance rejection and performance enhancement of perturbed tri-stable energy harvesters by adaptive finite-time disturbance observer

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Acta Mechanica Sinica 2022年第8期38卷 132-148页

作者： Shitong Fang Naser Padar Mohammad Javad Mirzaei Shengxi Zhou Wei-Hsin Liao Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong Kong999077China Department of Electrical Engineering Amirkabir University of TechnologyTehran15875-4413Iran Faculty of Electrical and Computer Engineering University of TabrizTabriz516661-6471Iran Research&Development Institute in Shenzhen Northwestern Polytechnical UniversityShenzhen518057China

Tristable energy harvesters(TEHs)have been proposed to achieve broad frequency bandwidth and superior low-frequency energy harvesting ***,due to the coexistence of three potential wells and the sensitivity to system conditions and external disturbances,the desired high-amplitude inter-well oscillation in the TEHs may be replaced by the chaotic or intra-well oscillations with inferior energy ***,the chaos has an unpredictable trajectory and may cause system damages,lessen the structural durability as well as require a more complicated circuit for power ***,in this paper,we firstly propose an adaptive finite-time disturbance observer(AFTDO)for performance enhancement of TEHs by detecting the external disturbances that induce the chaos,and reject them for the recovery of the desired inter-well *** proposed AFTDO eliminates the need to know in advance the upper bounds of imposed perturbations in conventional observers by means of the proposed adaptive protocols,leading to the higher efficacy of *** mathematical model of the piezoelectric TEH system and the AFTDO is *** demonstrate the effectiveness of the AFTDO,a series of numerical simulations have been *** show that for both cases with sinusoidal and impulsive disturbances,the AFTDO can successfully track the trajectories of the disturbance signals with the adaptive gain,and reject the disturbance to enable the TEH to sustain the periodic inter-well oscillation with effective energy harvesting performance.

关键词： Tristable energy harvesters Finite-time disturbance observer Sinusoidal and impulsive disturbances Chaos Inter-well oscillation

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MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement

MMA-Net: Multiple Morphology-Aware Network for Automated Cob...

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IEEE International Conference on robotics and automation (ICRA)

作者： Zhengxuan Qiu Jie Yang Jiankun Wang Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Scoliosis diagnosis and assessment depend largely on the measurement of the Cobb angle in spine X-ray images. With the emergence of deep learning techniques that employ landmark detection, tilt prediction, and spine segmentation, automated Cobb angle measurement has become increasingly popular. However, these methods encounter difficulties such as high noise sensitivity, intricate computational procedures, and exclusive reliance on a single type of morphological information. In this paper, we introduce the Multiple Morphology-Aware Network (MMA-Net), a novel framework that improves Cobb angle measurement accuracy by integrating multiple spine morphology as attention information. In the MMA-Net, we first feed spine X-ray images into the segmentation network to produce multiple morphological information (spine region, centerline, and boundary) and then concatenate the original X-ray image with the resulting segmentation maps as input for the regression module to perform precise Cobb angle measurement. Furthermore, we devise joint loss functions for our segmentation and regression network training, respectively. We evaluate our method on the AASCE challenge dataset and achieve superior performance with the SMAPE of 7.28% and the MAE of 3.18°, indicating a strong competitiveness compared to other outstanding methods. Consequently, we can offer clinicians automated, efficient, and reliable Cobb angle measurement.

关键词： Training Image segmentation Sensitivity Scoliosis Noise Morphology Reliability

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A Landmark-Aware Network for Automated Cobb Angle Estimation from X-Ray Images

SSRN

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SSRN 2024年

作者： Yang, Jie Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

Automated Cobb angle estimation based on X-ray images plays an important role in scoliosis diagnosis, treatment, and progression monitoring. The inadequate feature extraction and the noise in X-ray images are the main difficulties of automated Cobb angle estimation, and it is challenging to ensure that the calculated Cobb angle meets clinical requirements. To address these problems, we propose a Landmark-aware Network named LaNet with three components, Feature Robustness Enhancement Module (FREM), Landmark-aware Objective Function (LOF), and Cobb Angle Calculation Method (CACM), for automated Cobb angle estimation in this paper. To enhance feature extraction, FREM is designed to explore geometric and semantic constraints among landmarks, thus geometric and semantic correlations between landmarks are globally modeled, and robust landmark-based features are extracted. Furthermore, to mitigate the effect of background noise on landmark localization, LOF is proposed to focus more on the foreground near the vertebrae and ignore irrelevant background pixels by exploiting category prior information of landmarks. In addition, we also advance CACM to locate the bending segments first and then calculate the Cobb angle within the bending segment, which facilitates the calculation of the clinical standardized Cobb angle. The experiment results on the AASCE dataset demonstrate that our proposed LaNet can significantly improve the Cobb angle estimation performance and outperform other state-of-the-art methods. © 2024, The Authors. All rights reserved.

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research on Obstacle Recognition Methods of Unmanned Vehicles in Off-road Environment 6

Research on Obstacle Recognition Methods of Unmanned Vehicle...

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6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022

作者： Dong, Qingyu Wang, Jingyao Guo, Jinghua Jilin University State Key Laboratory of Automotive Simulation and Control Changchun China Xiamen University Department of Mechanical and Electrical Engineering Xiamen China Xiamen University Department of Automation Xiamen China Shenzhen Research Institute of Xiamen University Shenzhen China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

The off-road environment is complex, and the accurate obstacle detection of unmanned vehicles in the off-road environment is difficult. In order to identify the obstacles in the off-road environment with low cost, real-time image recognition and high efficiency, the Faster R-CNN optimized by 4 methods is designed in this paper. Comparing with the original model, SSD and YOLO, our optimized model shows obvious advantages in obstacle identification in the off-road environment. © 2022 IEEE.

关键词： Roads and streets

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Advances in artificial joint bio-friction and wear simulation assessment technology

Advances in artificial joint bio-friction and wear simulatio...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Yanyan Liang, Yiming Zhu, Shiqiang Ding, Menglong Bai, Yunhe Du, Haoyuan Gu, Jason Li, Tiefeng Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H4R2 Canada Center for X-mechanics Zhejiang University Hangzhou310027 China Zhejiang University Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Department of Engineering Mechanics Hangzhou310027 China

ISBN: (纸本)9781665481090

Natural joints have excellent low-friction properties that positively inspire the frictional wear properties of artificial joints. Replacing damaged biological joints with artificial ones can restore bio-function and reduce the pain of arthritic patients. Improvements in the frictional wear properties of artificial joints will effectively increase the life of the prosthesis, especially for young arthritic patients. This paper reviews the lubrication mechanism of natural joints before systematically introducing the development of lubrication and wear simulation techniques for artificial joints, offering references and insights for further studies of artificial joints and the optimization of tribological properties of commercial artificial joint prostheses. © 2022 IEEE.

关键词： Friction

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Design and Development of Low-Complex Methodology for sEMG-Based Hand Activity Identification and Actuation of Soft Robots for Rehabilitating and Amputated Patients

Design and Development of Low-Complex Methodology for sEMG-B...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Diptasri Ghosh Debasish Chatterjee Debeshi Dutta Somsukla Maiti Amit Acharyya Soumen Sen Department of Electrical Engineering Indian Institute of Technology Hyderabad India Human Centred Robotics and Cybernetics Group CSIR-Central Mechanical Engineering Research Institute Durgapur West Bengal India Centre for Micro-Nano Manufacturing and Metrology Central Manufacturing Technology Institute Bengaluru India

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

Advancements in automated rehabilitation and pow-ered prosthetics necessitate a holistic approach involving wearable sensor-driven activity identification followed by generation of appropriate control signals to drive user-specific assistive tools. Here, we demonstrate an end-to-end system facilitating low-complex energy-efficient surfaceElectroMyography (sEMG)-driven human hand activity classification using simple and accurate k-means clustering and subsequent control of in-house fabricated soft robotic actuator for rehabilitative and prosthetic assistance. A Delsys Trigno EMG device was used to capture EMG data from the left flexor carpi radialis muscle of 20 healthy individuals corresponding to seven different hand activities. Thereafter, a COordinate Rotation DIgital Computer (CORDIC) based k-means algorithm was implemented to classify the hand activities after the execution of CORDIC-based Ensemble Em-pirical Mode Decomposition (EEMD) and Fast Independent Component Analysis (FICA) on the acquired data, followed by, necessary segmentation and feature extraction. Simultaneously, a lightweight pneumatically actuated soft finger system fabricated out of silicone was developed with the aim of emulating the classified hand activities. The novelty of this soft actuator lies in its strategic design that enables large bending, even at pressure as low as 160 kPa yet restricts the movement in one plane with almost no bulging added with improved force exertion during contact. Accurate control signals representing each of the classified activities were realized on a Raspberry Pi 4 board with python environment. For a thorough analysis, the classification accuracy linked with the proposed algorithm was evaluated for different feature sets and an accuracy as high as 96% was obtained. Thus, an end-to-end system was established for real-time control of the soft actuator with a latency of 3 seconds, making it suitable for rehabilitation and prosthetic control applications where

关键词： Hands Accuracy Soft robotics Pneumatic systems Robot sensing systems Real-time systems Energy efficiency Electromyography System-on-chip Prosthetics

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Analysis of Influencing Factors of Carbon Emissions from Transportation Sector in Beijing Considering Energy Structure 7

Analysis of Influencing Factors of Carbon Emissions from Tra...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Zhang, Peng Chen, Junshi Zhan, Ziyi Wang, Liyong Zhang, Lu School of Electrical Automation and Information Engineering Tianjin University Tianjin China State Grid Zhejiang Electric Power Co. Ltd. Lishui Power Supply Company Marketing Department Lishui China Electric Power Research Institute State Grid Beijing Electric Power Company Beijing China

ISBN: (纸本)9798350345094

In this paper, the LMDI decomposition method is used to systematically analyze the factors of carbon emissions in the transportation sector in Beijing considering the energy structure, and nine influencing factors such as development level, energy intensity, energy structure and transportation structure are separated. The results show that: There are differences in the energy structure between the public and private sectors. The indirect carbon emissions of electric energy account for about 13.6% of the road carbon emissions in the public sector, and only 0.9% of the road carbon emissions in the private sector. The results of this paper provide a reference for the decision-making of transportation energy structure adjustment;The contribution value of development level and energy intensity in the public sphere is 25.20% and -38.71%, which are the two most critical factors affecting carbon emissions from public roads;The number of private cars in the private sector contributes 60.17% to the carbon emissions of Beijing, which is the most critical factor affecting the carbon emissions of private roads. © 2023 IEEE.

关键词： Carbon

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Smart Dairy Farm - Digitalization and Innovation 8

Smart Dairy Farm - Digitalization and Innovation

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8th International Conference on Energy Efficiency and Agricultural engineering, EE and AE 2022

作者： Markov, Nikolay Stoycheva, Svetoslava Hristov, Miroslav Mondeshka, Lora Atanasova, Tatiana Blagoev, Ivan Petrov, Plamen Valova, Irena Valov, Nikolay Mladenova, Tsvetelina Agricultural Academy Research Institute of Mountain Stockbreeding and Agriculture Troyan Bulgaria Institute of Information and Communication Technologies Modeling and Optimization Department Bulgarian Academy of Science Sofia Bulgaria University of Ruse Angel Kanchev Faculty of Electrical Engineering Electronics and Automation Ruse Bulgaria

ISBN: (纸本)9781665407090

Digitalization is an important part of modern agriculture. Digital dairy farming is developing based on modern digital technologies. These technologies allow the formation of a sufficient cloud of data in accordance with the physiological and technological requirements of different categories of dairy cattle. The article assesses the development of various digital technologies together with biometric sensors and blockchain technologies in dairy cattle breeding, respectively in a modern dairy farm. The tendencies and perspectives for digital innovation and digitalization of dairy farms and complexes are studied. It is concluded that the main factors influencing the development of digital technologies in dairy farming are their price, validity and response time. The aim of the current study is to make a retrospective review, characterization and analysis of modern scientific achievements of various digital technologies and systems applied in the technological cycle of dairy cattle breeding. © 2022 IEEE.

关键词： Farms

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Constrained Generative Sampling of 6-DoF Grasps

arXiv

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arXiv 2023年

作者： Lundell, Jens Verdoja, Francesco Le, Tran Nguyen Mousavian, Arsalan Fox, Dieter Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland KTH Royal Institute of Technology Sweden NVIDIA Corporation United States Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States

Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object’s body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6-Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10–15% higher grasp success rate than the baseline while being 2–3 times as sample efficient. Supplementary material is available on our project website. Copyright © 2023, The Authors. All rights reserved.

关键词： Bottles

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FogROS2-FT: Fault Tolerant Cloud robotics

FogROS2-FT: Fault Tolerant Cloud Robotics

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

作者： Kaiyuan Chen Kush Hari Trinity Chung Michael Wang Nan Tian Christian Juette Jeffrey Ichnowski Liu Ren John Kubiatowicz Ion Stoica Ken Goldberg Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Robert Bosch Research and Technology Center North America Sunnyvale CA USA Robotics Institute Carnegie Mellon University Department of Industrial Engineering and Operations Research University of California Berkeley CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Cloud robotics enables robots to offload complex computational tasks to cloud servers for performance and ease of management. However, cloud compute can be costly, cloud services can suffer occasional downtime, and connectivity between the robot and cloud can be prone to variations in network Quality-of-Service (QoS). We present FogROS2-FT (Fault Tolerant) to mitigate these issues by introducing a multi-cloud extension that automatically replicates independent stateless robotic services, routes requests to these replicas, and directs the first response back. With replication, robots can still benefit from cloud computations even when a cloud service provider is down or there is low QoS. Additionally, many cloud computing providers offer low-cost "spot" computing instances that may shutdown unpredictably. Normally, these low-cost instances would be inappropriate for cloud robotics, but the fault tolerance nature of FogROS2-FT allows them to be used reliably. We demonstrate FogROS2-FT fault tolerance capabilities in 3 cloud-robotics scenarios in simulation (visual object detection, semantic segmentation, motion planning) and 1 physical robot experiment (scan-pick-and-place). Running on the same hardware specification, FogROS2-FT achieves motion planning with up to 2.2x cost reduction and up to a 5.53x reduction on 99 Percentile (P99) long-tail latency. FogROS2-FT reduces the P99 long-tail latency of object detection and semantic segmentation by 2.0x and 2.1x, respectively, under network slowdown and resource contention. Videos and code are available at https://***/view/fogros2-ft.

关键词： Fault tolerance Heavily-tailed distribution Semantic segmentation Fault tolerant systems Quality of service Object detection Planning Servers Robots Cloud robotics

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