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5th World Robot Conference Symposium on Advanced robotics and automation, WRC SARA 2023

作者： Shen, Cuifeng Deng, Zhaokun Wang, Jinzhi Wang, Shuangyi Chen, Chen Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong

ISBN: (纸本)9798350307320

Ultrasound scanning is an indispensable diagnostic tool in modern medicine, but obtaining an accurate standard plane relies heavily on the sonographer's skill and expertise. This paper presents a novel approach toward autonomous robotic ultrasound scanning using a reinforcement learning-based navigation method with volumetric ultrasound data. The proposed method enables an ultrasound robot to autonomously acquire high-quality 2D standard planes from patients, thereby reducing the need for human intervention. The core of the approach is a deep reinforcement learning algorithm that detects 2D standard planes within the 3D ultrasound volume and guides the ultrasound probe to extract high-quality standard planes. To assess the effectiveness of the 2D standard plane detection method, an evaluation was conducted using a realworld ultrasound dataset, and a comparison was made between its performance and that of other state-of-the-art autonomous approaches. The simulation experiment was also conducted to validate the autonomous robotic ultrasound acquisition workflow. Results demonstrate that the reinforcement learning-based volumetric data navigation method outperforms existing techniques in terms of standard plane accuracy and is very effective for volumetric data-based robotic navigation. © 2023 IEEE.

关键词： Diagnosis

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AGV State Monitoring Based on Global and First-person View Fusion in Digital Workshops 12

AGV State Monitoring Based on Global and First-person View F...

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12th International Conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Zhang, Sichao Liang, Wei Li, Gengyu Yuan, Xudong An, Haibo Zhang, Yinlong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Science Beijing100049 China School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of Cetc Jiangsu Nanjing210007 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

AGV state monitoring is becoming increasingly important for production system safety in digital workshops. This paper develops a global and first-person view fusion to monitor the AGV (Automated Guided Vehicle) running state in the complex digital workshops. Based on the affine transformation model, the AGV running states, i.e., position, velocity and orientation are estimated by the global camera with the assistance of ArUco markers, which are pasted on the front side of the AGV. Besides, the first-person view camera is used to implement pedestrian detection and distance measurement based on the YOLOv3 model. It is noticeable that the pitch angle is considered for distance measurement, and an improved triangular model for distance measurement is proposed. The effectiveness of the proposed method has been verified in our built platform. © 2022 IEEE.

关键词： Cameras

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Path-tracking of Mobile Robot Using PD-type Iterative Learning Control with Forgetting Factor

Path-tracking of Mobile Robot Using PD-type Iterative Learni...

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第35届中国控制与决策会议

作者： Tao Zhang Jingzhe Fang Yun Teng Xintong Wang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

In this paper,a discrete iterative learning control strategy is proposed for the path-tracking problem of mobile robot trajectory tracking with repeated *** algorithm adds the forgetting factor to the PD-type discrete iterative learning control and considers the correction of the initial state of the *** convergence of the algorithm is proved rigorously by using the paradigm theory,and the sufficient conditions of the paradigm form to make the algorithm converge are ***,the effectiveness of the proposed algorithm is verified by simulation.

关键词： Mobile robot discrete iterative learning control forgetting factor path-tracking

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Predicting Salinity Resistance of Rice at the Seedling Stage: An Evaluation of Transfer Learning Methods 1st

Predicting Salinity Resistance of Rice at the Seedling Stage...

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1st International Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Shiragudikar, Sharada K. Bharamagoudar, Geeta K., Manohara K. Y., Malathi S. G.Totad, Shashikumar School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India CSE Department KLEIT Karnataka Hubballi India Central Coastal Agricultural Research Institute Old Goa India Department of Automation and Robotics KLE Technological University Hubballi India

ISBN: (纸本)9783031756047

Rice has become one of the largest and most widely cultivated food crops in the world. Nevertheless, there is a significant probability that high amounts of salt, especially at the stage of seedling development, would have a detrimental effect on rice output. As a result, it is critical to quickly discover and develop salinity-tolerant rice crop types, particularly at the stage of seedling development, to prevent a decrease in rice output. Expertise from humans is required for the categorization of visual signals and the conventional way of a standard assessment system for identifying rice crop salinity stress. It's time demanding and prone to mistakes, both of which may lead to inaccurate classification. Recent developments in deep learning and transfer training have opened the road for the automated categorization of a wide range of agricultural jobs, from the detection of plant diseases and pests to the identification of weeds and invasive species. The lack of plant-domain-specific pre-trained models and the limited number of available datasets remain significant obstacles to agricultural automation. We use the AgriNet dataset, a database of 850 image graphs of farms and agricultural settings. The work shows the need for a deep learning developed model over the conventional way of measuring rice crop sensitivity to salt stress throughout the seedling stage to detect and categorize stress caused by salinity in the seedling stage of rice using field images. Thus, we use one of the most advanced deep learning approaches, VGG 16, VGG 19, Resnet 50, Resnet 100, exception, Resnet 50V2, Inception V3, MobileNetV2, DenseNet121, and ResNet101V2 which was built in Python's Jupyter Notebook, to construct the classification model. To further demonstrate the need for a computer-based classification system for salinity estimation that could be used as a tool for automating the classification process for rice development to aid scientists and farmers in the rice crop management

关键词： Deep learning

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Active Generalized Category Discovery

Active Generalized Category Discovery

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shijie Ma Fei Zhu Zhun Zhong Xu-Yao Zhang Cheng-Lin Liu MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes, and that novel categories are fully unlabeled, GCD inherently faces intractable problems, including imbalanced classification performance and inconsistent confidence between old and new classes, especially in the low-labeling regime. Hence, some annotations of new classes are deemed necessary. However, labeling new classes is extremely costly. To address this issue, we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem, we devise an adaptive sampling strategy, which jointly considers novelty, informativeness and diversity to adaptively select novel samples with proper uncertainty. However, owing to the varied orderings of label indices caused by the clustering of novel classes, the queried labels are not directly applicable to subsequent training. To overcome this issue, we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier, thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://***/mashijie1028/ActiveGCD

关键词： Training Computer vision Uncertainty Computational modeling Clustering algorithms Transforms Pattern recognition

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Internet of Things Enabled Automatic Meter Reading

Internet of Things Enabled Automatic Meter Reading

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Smart Devices (ICSD), International Conference on

作者： Sarthak Malik Shikha Agarwal Srinivas Aluvala Jyoti S. Bali Rachit Garg Mohemmed Hussien School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Ajay Kumar Garg Engineering College Ghaziabad UP India School of Computer Science & Artificial Intelligence SR University Warangal Dept of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune The Islamic University Najaf Iraq

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

The world is currently going through a huge transition towards digitalization and sustainability. Sustainability focuses on responsible resource management and environmental preservation, whereas digitalization entails the conversion of many processes and systems into connected, digital forms. Energy management, which aims to offer clean and affordable energy solutions while minimising waste and environmental damage, is one of the important sectors garnering significant attention in this context. Energy management involves the methodical optimisation of energy use in a variety of contexts, including commercial and industrial facilities and residential settings. The energy metre, a tool used to measure electricity, gas, or other energy kinds, is essential to this project. Traditional energy metres are evolving into "smart" energy metres, which may send consumption data periodically or in real-time in addition to measuring energy usage. The usability of energy metres is considerably increased by this feature for both consumers and energy suppliers. Industry 4.0 refers to the fourth industrial revolution, which is characterised by the incorporation of digital technologies into manufacturing and industrial processes, in the broader context of business and technology. Internet of Things (IoT), artificial intelligence (AI), and automation are key technologies advancing this transformation. These technologies are essential for improving productivity, sustainability, and efficiency across sectors. A key component of increasing energy management practises is the Internet of Things (IoT). The internet of things (IoT) is a network of connected gadgets and sensors that can gather and send data. IoT-enabled devices are crucial to the real-time monitoring and management of energy use in the field of energy management. These gadgets are capable of collecting information from many sources, such as energy metres, and transmitting it for analysis and well-informed decision-making. An

关键词： Cloud computing Accuracy Electricity Sensor phenomena and characterization Real-time systems Internet of Things Servers Energy management Sustainable development Monitoring

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Design of a Flexible Robot Arm for Safe Aerial Physical Interaction

arXiv

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arXiv 2024年

作者： Mellet, Julien Berra, Andrea Seisa, Achilleas Santi Sankaranarayanan, Viswa Gamage, Udayanga G.W.K.N. Soto, Miguel Ángel Trujillo Heredia, Guillermo Nikolakopoulos, George Lippiello, Vincenzo Ruggiero, Fabio PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Robotics and AI Team Department of Computer Electrical and Space Engineering LuleåUniversity of Technology Luleå Sweden Automation and Control Group Department of Electrical Engineering and Photonics Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors’ integrity. Notably, the arm’s damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction. © 2024, CC BY.

关键词： End effectors

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Teleoperation of Multi-functional Neurosurgical Tool*

Teleoperation of Multi-functional Neurosurgical Tool*

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Xudong Ma Lechen Yao Anqi Chen Junchen Wang Changsheng Li Wenyong Liu Yida David Hu Bai-Quan Su Medical Robotics Laboratory School of Automation Beijing University of Posts and Tele-communications Beijing China Affiliated High School of Peking University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA

Fluid removal and electrotome coagulation are routine procedures in neurosurgery operations, and the doctor’s remote control can reduce physical fatigue. In this paper, an integrated multi-functional surgical tool which can realize electrocoagulation and liquid aspiration is designed. At the same time, the multi-functional integrated surgical device is combined with the remote operation method to realize the remote operation of the instrument. This multifunctional instrument includes CCD-imaging, suction and electrical coagulation. The system can complete the remote operation function of the special design tool, and can isolate the doctor from the operation. The teleoperation of this instrument is integrated with the teleoperation of other functional instruments to realize the teleoperation of the complete surgical process.

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Distributed Optimization by Network Flows with Spatio-Temporal Compression

arXiv

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arXiv 2024年

作者： Ren, Zihao Wang, Lei Yi, Xinlei Wang, Xi Yuan, Deming Yang, Tao Wu, Zhengguang Shi, Guodong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China College of Electronics and Information Engineering Tongji University China School of Electrical Engineering Telecommunications University of New South Wales Sydney Australia School of Automation Nanjing University of Science and Technology China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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Evaluation of motor vehicle driver fatigue based on eye movement signals

Evaluation of motor vehicle driver fatigue based on eye move...

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2021 International Conference on Computer engineering and Artificial Intelligence, ICCEAI 2021

作者： Liu, Xing Cai, Lecai Wu, Zhiming Duan, Shaosong Tang, Keyuan Zhang, Chaoyang School of Automation and Information Engineering Sichuan University of Science Engineering Zigong China Sanjiang Research Institute of Artificial Intelligence and Robotics Yibin University Yibin China Yibin University Faclty of Intelligence Manufacturing Yibin China

ISBN: (纸本)9781665439602

Fatigue driving is one of the main causes of traffic accidents, and it has a great impact on road safety. One of the most effective fatigue driving detection methods is based on the machine vision using the characteristics of driver's eye. However, this accuracy of the method is influenced by the light environment during driving. In order to address the problem, a method is proposed to detect the fatigue driving. In this method, the homomorphic filtering is first used to preprocess the image to eliminate the effect of various lighting environment;then the face of driver is detected based on the Local Binary Pattern(LBP) based method considering its rapid image processing speed and the key points of the face and eyes were extracted using direct shape regression network (DSRN);finally, the features such as blink frequency, blink duration and PERCLOS are calculated based on the key points and the support vector machine is used to build classifier to identify the fatigue state of drivers. The results show that the proposed method can identify the fatigue state with relatively high accuracy. © 2021 IEEE.

关键词： Face recognition

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