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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Wenju Shao, Keyong Zhu, Chi Ma, Yue Cao, Wujing Yin, Meng Yang, Lijun Luo, Mingxiang Wu, Xinyu Northeast Petroleum University School of Electrical Engineering Information Daqing China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Guangdong Shenzhen518055 China Maebashi Institute of Technology Department of Systems Life Engineering Maebashi3710132 Japan

ISBN: (纸本)9781665481090

Motion imaging (MI) is widely used in exoskele-ton robot control direction. However, online real-time con-trol of exoskeletons based on biological signals is difficult to achieve. Because the anti-interference of biological signals in real life is poor and affected by individual differences, the control of auxiliary equipment by the realization of human-machine interface is still facing great challenges. In this paper, a human-machine interface (HMI) based on hybrid biological signals is designed and online real-time control of lower extremity exoskeletons is realized. Based on a comprehensive analysis of human walking, the HMI uses the electromyography signals (upper extremities) and EEG signals generated during real exercise as input signals. Different analysis methods are designed for different modal signals. We extracted four features of sEMG signals to distinguish between different movement processes in the human body. For EEG signals, the use of Independent Component Analysis (ICA) increases the robustness of the system. Event-related desynchronization/synchronization (ERS/ERD) and power spectral densities are reused for analysis from the perspective of the interaction of multiple information in the channel, time and frequency domains. The human-machine interface integrates EEG and EMG multimodal biological signals, and the average recognition accuracy of different subjects has reached 93.7%. In order to increase the recognition accuracy of practical applications and reduce the differences between different individuals, a window voting mechanism is designed. Healthy subjects were tested, and the recognition accuracy of the lower limb exoskeleton online real-time assistance system reached 99%, with high accuracy and reliability. © 2022 IEEE.

关键词： Real time control

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Towards Open-Source and Modular Space systems with ATMOS

arXiv

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arXiv 2025年

作者： Roque, Pedro Phodapol, Sujet Krantz, Elias Lim, Jaeyoung Verhagen, Joris Jiang, Frank J. Dörner, David Mao, Huina Tibert, Gunnar Siegwart, Roland Stenius, Ivan Tumova, Jana Fuglesang, Christer Dimarogonas, Dimos V. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden School of Engineering Sciences KTH Royal Institute of Technology Stockholm Sweden Autonomous Systems Laboratory ETH Zürich Zürich Switzerland Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

In the near future, autonomous space systems will compose many of the deployed spacecraft. Their tasks will involve autonomous rendezvous and proximity operations with large structures, such as inspections, assembly, and maintenance of orbiting space stations, as well as human-assistance tasks over shared workspaces. To promote replicable and reliable scientific results for autonomous control of spacecraft, we present the design of a space robotics laboratory based on open-source and modular software and hardware. The simulation software provides a software-in-the-loop architecture that seamlessly transfers simulated results to the hardware. Our results provide an insight into such a system, including comparisons of hardware and software results, as well as control and planning methodologies for controlling free-flying platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Space stations

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Fractional-order Non-singular Terminal Sliding Mode Control for Bilateral Teleoperation System 48

Fractional-order Non-singular Terminal Sliding Mode Control ...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Duan, Xiaolong Ma, Zhiqiang Liu, Zhengxiong Liu, Yu Bai, Lifei Northwestern Polytechnical University Research Center for Intelligent Robotics School of Astronautics Xi'an710072 China Beijing Institute of Tracking and Telecommunication Technology Beijing100094 China Systems Engineering Research Institute of China State Shipbuilding Corporation Limited Beijing100094 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This article investigates the stabilization of the bilateral teleoperation system with inherent strong nonlinearities and model parametric uncertainties, and a fractional-order non-singular terminal sliding mode control (FONTSMC) strategy for high-precision master-slave tracking is generated by combining fractional calculus and sliding mode control. The stability of the closed-loop system and finite-time convergence are analyzed using the Lyapunov stability theory. For simulation verification, the presented control scheme is applied to a pair of 3-DoF Phantom Omni haptic manipulators. The results reveal that the performance of non-singular terminal sliding mode control with fractional calculus is significantly better than that of the integer-order non-singular terminal sliding mode control in terms of trajectory tracking accuracy and response time. © 2022 IEEE.

关键词： Sliding mode control

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Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search

arXiv

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arXiv 2024年

作者： Huang, Tianyu Li, Haoang Peng, Liangzu Liu, Yinlong Liu, Yun-Hui The Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong The Thrust of Robotics and Autonomous Systems and the Thrust of Intelligent Transportation The Hong Kong University of Science and Technology Guangzhou China University of Macau 999078 China

Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios (> 95 % is common), underscoring the significance of robust registration methods. Many researchers turn to parameter search-based strategies (e.g., Branch-and-Bround) for robust registration. Although related methods show high robustness, their efficiency is limited to the high-dimensional search space. This paper proposes a heuristics-guided parameter search strategy to accelerate the search while maintaining high robustness. We first sample some correspondences (i.e., heuristics) and then just need to sequentially search the feasible regions that make each sample an inlier. Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness. Since directly parameterizing the 6-dimensional nonlinear feasible region for efficient search is intractable, we construct a three-stage decomposition pipeline to reparameterize the feasible region, resulting in three lower-dimensional sub-problems that are easily solvable via our strategy. Besides reducing the searching dimension, our decomposition enables the leverage of 1-dimensional interval stabbing at all three stages for searching acceleration. Moreover, we propose a valid sampling strategy to guarantee our sampling effectiveness, and a compatibility verification setup to further accelerate our search. Extensive experiments on both simulated and real-world datasets demonstrate that our approach exhibits comparable robustness with state-of-the-art methods while achieving a significant efficiency boost. © 2024, CC BY.

关键词： Efficiency

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A Sensing Platform to Monitor Sleep Efficiency 11th

A Sensing Platform to Monitor Sleep Efficiency

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11th Italian Forum on Ambient Assisted Living, ForItAAL 2020

作者： Crivello, Antonino La Rosa, Davide Wilhelm, Elisabeth Palumbo, Filippo 1 Via G. Moruzzi Pisa56124 Italy Department of Health Sciences and Technology Istitute of Robotics and Intelligent Systems Sensory Motor Systems Lab ETH Zurich Zurich8092 Switzerland Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands

ISBN: (纸本)9783031088377

Sleep plays a fundamental role in the human life. Sleep research is mainly focused on the understanding of the sleep patterns, stages and duration. An accurate sleep monitoring can detect early signs of sleep deprivation and insomnia consequentially implementing mechanisms for preventing and overcoming these problems. Recently, sleep monitoring has been achieved using wearable technologies, able to analyse also the body movements, but old people can encounter some difficulties in using and maintaining these devices. In this paper, we propose an unobtrusive sensing platform able to analyze body movements, infer sleep duration and awakenings occurred along the night, and evaluating the sleep efficiency index. To prove the feasibility of the suggested method we did a pilot trial in which several healthy users have been involved. The sensors were installed within the bed and, on each day, each user was administered with the Groningen Sleep Quality Scale questionnaire to evaluate the user’s perceived sleep quality. Finally, we show potential correlation between a perceived evaluation with an objective index as the sleep efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sleep research

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BLIP-Adapter: Bridging Vision-Language Models with Adapters for Generalizable Face Anti-spoofing 18

BLIP-Adapter: Bridging Vision-Language Models with Adapters ...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Bin Zhu, Xiangyu Gao, Li Zhang, Xiaoyu Lei, Zhen Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China China Mobile Financial Technology Co. Ltd. China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation China University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Hong Kong

ISBN: (纸本)9798350364132

Face anti-spoofing is essential for ensuring the security of facial recognition systems against spoofing attacks. Recent methods have transferred Vision-Language models to face anti-spoofing (e.g., FLIP and CLIPC8), demonstrating that learning perception from supervision in natural language can enhance the model's detection performance. However, such methods exhibit limited depth in the interaction between images and texts, resulting in poor performance on fine-grained understanding tasks such as face anti-spoofing. Besides, the lack of diversity in image-text pairs for face anti-spoofing further hinders such methods from playing their best. To address these issues, we propose a novel fine-tuning strategy for Vision-Language models in face anti-spoofing. This strategy introduces the Bootstrapping Language-Image Pre-training model (BLIP), known for its novel interaction mechanisms and superior image-text comprehension, to construct a more generalized feature representation for face anti-spoofing. Furthermore, we propose an Adapter module for the text branch to reduce the negative impact of insufficient data diversity and catastrophic forgetting. Extensive experiments conducted on various cross-domain testing benchmarks demonstrate the significant superiority of our method over the state-of-the-art, highlighting its effectiveness and robustness. © 2024 IEEE.

关键词： Visual languages

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Identification of PMSM Moment of Inertia based on Model Reference Adaptive Algorithm 4

Identification of PMSM Moment of Inertia based on Model Refe...

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4th IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2022

作者： Ding, Fan Zhang, Tao Jia, Danping Zhang, Hualiang Pei, Quanshen Huang, Haoran School of Information Science and Engineering Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

A method that can identify the rotational inertia in a timely manner is proposed because the performance of a permanent magnet synchronous motor servo system is easily affected by the rotational inertia. Firstly, principles of model-referenced adaptive recognition algorithm is introduced, and the influence of the adaptive gain on the identification results is analyzed. Then, design a suitable range of adaptive gain value, use the set error value to compare the identification error with the set value, and then select the appropriate gain for identification. Finally, through simulation verification, this method has fast identification convergence speed, high precision and strong anti-disturbance. © 2022 IEEE.

关键词： Adaptive algorithms

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Dynamic Obstacle Avoidance for Magnetic Helical Microrobots Based on Deep Reinforcement Learning

Dynamic Obstacle Avoidance for Magnetic Helical Microrobots ...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yukang Qiu Yaozhen Hou Haotian Yang Yigao Gao Hen-Wei Huang Qing Shi Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology 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)9798350372601

ISBN: (纸本)9798350372618

Magnetic helical microrobots hold immense promise in biomedical domains owing to their compact size and efficient propulsion capabilities. However, navigating these microrobots through dynamic and unstructured environments, particularly when encountering numerous dynamic obstacles, remains a formidable challenge. In the study, a control framework based on deep reinforcement learning (DRL) with the objective of guiding a microrobot through dynamic obstacles towards specified target goals is introduced. Initially, we design and fabricate a microdrill capable of propulsion via external magnetic rotating fields produced by our magnetic actuation system. Subsequently, we construct a custom training environment, adhering to the OpenAI gym interface, to serve as the simulator for training purposes. Utilizing the proximal policy optimization algorithm, we conduct training of the navigation policy within this simulator. Simulations and experimental validations conducted in dynamic environments affirms the efficacy of the proposed method.

关键词： Training Navigation Magnetic cores Heuristic algorithms Magnetic domains Propulsion Deep reinforcement learning

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Fuzzy Based Steering Control of a Multi-Joint AUV

Fuzzy Based Steering Control of a Multi-Joint AUV

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Control Conference (ANZCC), Australian and New Zealand

作者： Zakarya Omar Ke-Xian Liu Chao Ren Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Un-manned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Faculty of Engineering Sana'a University Sana'a Yemen

The multi-joint structure of our Autonomous Underwater Vehicle (AUV) enhances its maneuverability, allowing it to navigate in the underwater environment with greater flexibility. However, this added maneuverability poses challenges to the steering process. When the multi-joint AUV (MJ-AUV) performs steering maneuvers, its joints undergo rotation, leading to a change in the orientation of the cabins with respect to the overall forward heading of the vehicle. As a result, this change in orientation affects the values of the hydrodynamic reactions, including Coriolis and damping, experienced by the cabins. The dynamic interaction between the joints' rotation and the resulting change in hydrodynamic forces significantly impacts the steering performance and stability of the MJ-AUV. Understanding and addressing these effects are crucial for the development of effective control strategies that ensure precise and reliable steering in various underwater environments. Overcoming the difficulties posed by joint rotation during steering can lead to advancements in MJ-AUV navigation and expand their potential applications in complex underwater missions. This research proposes a fuzzy-based control with sliding mode control (SMC) approach for the steering of an MJAUV. The developed fuzzy-based SMC control algorithm is validated through extensive MATLAB simulations. The results demonstrate improved tracking performance and robustness in comparison to the SMC control method. Moreover, the proposed approach shows superior trajectory tracking accuracy while mitigating undesired chattering effects associated with standard SMC techniques.

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Impact of Employing Weather Forecast Data as Input to the Estimation of Evapotranspiration by Deep Neural Network Models**

arXiv

引用

arXiv 2024年

作者： Vaz, Pedro J. Schütz, Gabriela Guerrero, Carlos Cardoso, Pedro J.S. Universidade do Algarve Portugal CEOT – Centre for Electronics Optoelectronics and Telecommunications Portugal MED – Mediterranean Institute for Agriculture Environment and Development Portugal LARSyS Laboratory of Robotics and Engineering Systems Portugal

Reference Evapotranspiration (ET!) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for ET! computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To computeET ! using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily ET! estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several ET! estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors’ previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct ET! estimation by an Artificial Neural Network (ANN) model, and (ii) estimate SR by (another) ANN model, and then use that estimation for ET! computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (R") ranging between 0.893 and 0.667, when considering forecasts up to 15 days. © 2024, CC BY.

关键词： Irrigation

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