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IEEE International Conference on robotics and Automation (ICRA)

作者： Sukhan Lee H. Moradi G. Kardaras Sung-Kwon Kim Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Computer Science-EE Systems Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA Production Engineering Center Samsung Electronics Company Limited Suwan South Korea

In this paper a method for collision detection and collision avoidance for dual arm robots is proposed. The collision detection, a fast geometric approach based on link intersection, detects the collision and constructs the collision region for given trajectories. The time delay planning determines the minimum necessary time delay to avoid collisions using a potential field representation for collision regions. The planning is based on a flexible, massively parallel approach. It effectively avoids any local minima problems due to the potential field representation and can be used regardless of the complexity of the collision region (e.g., concave or complicated regions).

关键词： Delay effects Robotic assembly Manipulators Orbital robotics Intelligent robots Parallel robots Parallel processing Assembly systems robotics and automation Path planning

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CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face Anti-Spoofing

CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face A...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ying Zhang Xiangyu Zhu Ajian Liu Xun Lin Jun Wan Jing Zhang Zhen Lei State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Engineering Beihang University School of Computer Science and Engineering Macau University of Science and Technology Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. This innovation addresses the shortcomings of conventional multi-modal FAS approaches, which typically demand separate model training and deployment for each modality. However, existing flexible-modal FAS approaches activate specific network branches based on the modality of the tested sample. This not only increases the model’s parameters but also necessitates the provision of the image’s modality for testing, thereby constraining deployment flexibility. To address the issue, we present Compound Prompt Learning CLIP (CPL-CLIP), a novel method for flexible-modal FAS. This approach capitalizes on a learned textual prompt that is nearly independent of modality, thus bolstering class-based classification across arbitrary modalities. Specifically, our CPL-CLIP introduces a Dual-Branch Prompt (DBP), consisting of class and modal prompts that describe and guide classification, where each prompt is composed of learnable vectors and fixed templates. To further render the class prompt as modality-agnostic as possible, a Cosine Similarity Loss (CSL) is proposed to facilitate the maximal separation of the class prompt from the modality prompt. With only the class prompt utilized during testing, CPL-CLIP enables deployment in diverse modal testing scenarios without the necessity of the test image’s modality to be known. Extensive experiments demonstrate CPL-CLIP’s superiority over existing methods on several flexible-modal FAS benchmarks.

关键词： Training Adaptation models Technological innovation Accuracy Face recognition Benchmark testing Vectors Robustness Data models Compounds

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Design of a Control Platform for Mobile Robot with SSVEP-BCI System

Design of a Control Platform for Mobile Robot with SSVEP-BCI...

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IEEE Conference on Cybernetics and Intelligent systems

作者： Yang Liu Xinyang Li Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Hammersmith Campus Imperial College London National Heart and Lung Institute London Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728134581

ISBN: (纸本)9781728134598

This paper introduces a control platform based on brain-computer interface(BCI) for the mobile robot. The proposed platform consists of three parts: Path Planning, SSVEP-BCI and Mobile Robot Control. The path planning part aims to seek an optimal path out from the starting point to the target point, which is denoted as A* algorithm in this work. The found path is shown to the user who determines the actual movement direction using SSVEP-BCI. The signal is sent to the mobile robot to control the movement of the robot. In the BCI part, the collected EEG signals are analyzed with the CCA method to obtain the homologous control signals. The mobile robot is required to complete the movement from the initial point to the destination without collision. The platform is evaluated with an off-line experiment with 4 subjects and the off-line analysis shows the feasibility of the proposed platform.

关键词： Mobile robots Visualization Electroencephalography Path planning Heuristic algorithms Frequency modulation

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Automated Electromagnetic Manipulation of Micron-Scale Rotors on Unstructured Surfaces in Low Reynolds Numbers

Automated Electromagnetic Manipulation of Micron-Scale Rotor...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Shihao Zhong Kailun Bai Yaozhen Hou Hen-Wei Huang Qing Shi Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Science and Technology on Space Physics Laboratory Beijing China Laboratory for Translational Engineering Harvard Medical School Cambridge MA USA Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

The utilization of magnetic microrotors in low Reynolds number liquid environments holds great promise for a wide range of biomedical applications, owing to their ability to maneuver with flexibility. However, during the movement of the microrotors on solid unstructured surfaces, the interaction with neighboring walls has the potential to generate unpredictable disruptions in its motion. This study proposes an automated electromagnetic manipulation method for guiding micron-scale surface rotors along planar paths at low Reynolds numbers. To this end, we designed a magnetic field drive system using multistage electromagnets, and a double-helix rotor with micrometer-scale dimensions. Additionally, we developed a kinematic model for the microrotor that closely resembles that of wheeled mobile robots and employed a sliding mode motion controller to ensure high-precision path following. Results obtained from simulation and experimentation confirm the efficacy of our proposed method, demonstrating precise following of planar paths.

关键词： Visualization Mechatronics Rotors Kinematics Solids Electromagnets Magnetic fields Mobile robots Micromagnetics Electromagnetics

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End-to-end throughput maximization for underlay multi-hop cognitive radio networks with rf energy harvesting

arXiv

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arXiv 2017年

作者： Xu, Chi Zheng, Meng Liang, Wei Yu, Haibin Liang, Ying-Chang State Key Laboratory of Robotics Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Robotics Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China School of Electrical and Information Engineering University of Sydney NSW2006 Australia University of Electronic Science and Technology of China Chengdu611731 China

This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multiple access, by which SUs first harvest energy from the RF signals of PUs and then transmit data in the allocated time concurrently with PUs, all in the licensed spectrum. In this way, the available transmit energy of each SU mainly depends on the harvested energy before the turn to transmit, namely energy causality. Meanwhile, the transmit powers of SUs must be strictly controlled to protect PUs from harmful interference. Thus, subject to the energy causality constraint and the interference power constraint, we study the end-to-end throughput maximization problem for optimal time and power allocation. To solve this nonconvex problem, we first equivalently transform it into a convex optimization problem and then propose the joint optimal time and power allocation (JOTPA) algorithm that iteratively solves a series of feasibility problems until convergence. Extensive simulations evaluate the performance of EH-CRNs with JOTPA in three typical deployment scenarios and validate the superiority of JOTPA by making comparisons with two other resource allocation algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Cognitive radio

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A Novel Multi-Stream Informer Used for Lower Extremity Joint Angle Estimation 12

A Novel Multi-Stream Informer Used for Lower Extremity Joint...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Zhou, Xin Zhang, Liming Liu, Jiaqing Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In lower extremity exoskeleton rehabilitation systems, synergy and proportionality between the human lower extremity and the exoskeleton robot have been a critical goal to pursue. In recent years, changeable deep learning models based on multi-source signals have been used for lower limb continuous motion estimation. During the process, multimode signals are fed into the time processing model as time series. However, there are some deficiencies in this framework, on the one hand, what kind of multi-source combination can be more efficient;on the other hand, most of the existing models need several iterations or network stacking to complete the extraction of temporal information. This study set out to address the above problems, we put forward Multi-Stream Informer (MS-Informer), using Multi-Stream combination and sparse attention mechanisms for lower limb continuous motion estimation. In the experiment, we compare MS-Informer and Temporal Convolutional Network (TCN) and Long Short-term Memory (LSTM) under distinctive multi-source combinations. In summary, MS-informer performs well in multiple evaluations compared with other models. Experimental results illustrate that the MS-Informer is more efficacious in lower limb joint angle estimation and is a promising method for achieving coordinated control of lower limb exoskeletons. © 2022 IEEE.

关键词： Motion estimation

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Trajectory Planning Towards Water-Air Trans-Media Motion for Robotic Dolphin

Trajectory Planning Towards Water-Air Trans-Media Motion for...

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Chinese Automation Congress (CAC)

作者： Yan Xiong Pengfei Zhang Di Chen Junzhi Yu Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China Shandong Nuclear Power Company LTD. Yantai China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Water-air trans-media navigators have multi-media motion modes, breaking through the limitation that traditional robots can only operate in a single medium. Autonomous navigation is an indispensable component of the process of transmedia motion. Most of the current work has only completed the development and testing of prototypes, and the problem of task-oriented motion planning and control needs to be solved. In this paper, a safe and efficient hierarchical approach is proposed. First, a novel rapidly exploring random tree-based algorithm is proposed to search for a suitable path. Then, by fixing bound waypoints, the time-optimization trajectory is planned. Finally, the trajectory tracker is designed based on nonlinear model predictive control (NMPC). Simulation experiments numerically validate the proposed method, offering some new insights for further trans-media motions.

关键词： Tracking Trajectory planning Navigation Prototypes Prediction algorithms Trajectory Planning Testing Predictive control Dolphins

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Ultrafast dynamics in perovskite-based optoelectronic devices

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Cell Reports Physical science 2023年第9期4卷

作者： Gong, Shaokuan Huang, Yuling Yu, Xuemeng Hu, Qiushi Liu, Jingjing Meng, Jiazhi Wen, Yifan Chen, Xihan Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen 518055 China

Halide-perovskite-based materials are rising stars for optoelectronic applications. The key for optoelectronic device development is the photogenerated carrier dynamics, which directly describes the motion of photogenerated electrons and holes. A fundamental understanding of the carrier dynamics can help researchers gain deeper insight into the light-matter interaction. The generation and movement of charge carriers can be on the order of femto- and pico-second (ultrafast) timescales, which pose challenges for detection methods. Recent development in transient spectroscopic techniques enable the understanding of charge-carrier dynamics. In this perspective, we introduce several ultrafast spectroscopic methods that investigate total carrier dynamics, hot-carrier cooling dynamics, interfacial carrier transport dynamics, and carrier spin relaxation dynamics in perovskite optoelectronics. Through these investigations, we provide our perspective on the future development of perovskite-based optoelectronics. © 2023 The Author(s)

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Wheel-Terrain Contact Geometry Estimation using the Aside-Wheel RGB-D Sensor over Soft Terrain

Wheel-Terrain Contact Geometry Estimation using the Aside-Wh...

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robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Pengxuan Zhana Chen Yao Longteng Hu Feng Xue Peichen Wang Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China SUSTech Guang-dong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin of Institute of Technology (HIT) Harbin China College of Engineering University of Michigan Ann Arbor Michigan USA School of Electronics Harbin Institute of Technology Harbin China

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Wheeled mobile robots (WMRs) have exhibited notable competitiveness in various applications, including geo-graphic and planetary exploration. Nonetheless, when traversing on soft terrains, their wheels might encounter sinkage and slippage, leading to mission failure. Consequently, real-time estimation of contact parameters such as sinkage and slip ratio holds significant importance for rover operations. In this paper, we introduce a novel algorithm that employs only one aside-wheel RGB-D camera for the simultaneous detection of wheel sinkage and slip ratio on deformable terrains in real-time. We estimate sinkage through the depth module by processing point cloud data, and we estimate slip ratio through the color module with a sequence of image processing steps. Utilizing only one single camera reduces errors arising from sensor signal alignment and enhances detection accuracy. With a single-wheel testbed, we simulate wheel motion under different slip ratios and carry out numerous experiments to validate the effectiveness of the proposed algorithm. Our algorithm attains a detection frequency of up to 20Hz and exhibits outstanding precision. The relative sinkage error remains below 4%, while the relative slippage error is confined within 0.8%.

关键词： Accuracy Image color analysis Robot vision systems Wheels Estimation Cameras Real-time systems

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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