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第43届中国控制会议

作者： Biting Ma Chaoyu Cui Ruimin Li Minghui Li Jinyu Qiu Ke Li Mingzhu Sun Xin Zhao Qili Zhao National Key Laboratory of Intelligent Tracking and Forecasting fo Infectious Diseases Engineering Research Center of Trusted BehaviorIntelligence Ministry of Education Tianjin Key Laboratory of intelligentRobotic (tikLIR) Institute of Robotics and Automatic Information System(IRAlS) Nankai University Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The dual-arm patch clamp technique, using one micropipette electrode to patch the target cell to record its electric signals with the other micropipette mechanically stimulating the target cell, is a significant technique in neuroscience for studying neuron excitability under mechanical stimulation. A key step for dual-arm patch clamp operation is to coordinate two micropipettes to reach the target cells in the narrow brain tissue environment. The traditional manual operations face issues such as inaccurate workspace estimation, clumsy movements, and collision risks of the two micropipettes, resulting in low operational efficiency. Addressing the above issues, a robotic dual-arm patch clamp method was developed for neuron excitability research under mechanical stimulation in this paper. First, the reachable workspace of the dual-arm was calculated based on a kinematics model. Then, a robotic dual arm patch clamp operation procedure is proposed to approach, whole-cell patch, and mechanically stimulate the target cell. The experiment results on neurons in mouse brain slices show that the success rate of dual micropipette contacting cell surface reaches up to 90%(18/20) at an average speed of 6 mins/cell, which are both significantly improved in comparison to manual operation. An increase in neuron excitability in the primary visual cortex(V1) was found under the mechanical stimulation, proving the feasibility of the proposed system for studying the impact of mechanical stimulation on ion channel activities.

关键词： Robotic Patch Clamp Robotic Cell Measurement Workspace Calculation Whole-cell Recording Mechanical Stimulation

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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability

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IEEE Access 2025年 13卷 100731-100751页

作者： Abdennabi Morchid Abdennacer Elbasri Zahra Oughannou Hassan Qjidaa Rachid El Alami Badre Bossoufi Saleh Mobayen Paweł Skruch LIMAS Laboratory Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah (SMBA) University Fes Morocco Department of Data Science Mundiapolis University of Casablanca Campus Nouaceur Casablanca Morocco Innovation in Mathematics and Intelligent Systems (IMIS) Laboratory Ibn Zohr University Agadir Morocco Systems and Sustainable Environment Laboratory (SED) Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fes Morocco Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Energy Systems Research Center Khazar University Baku Azerbaijan Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

Global agriculture is facing major challenges such as food security, sustainable water management, and the preservation of natural resources. Water scarcity, exacerbated by climate change, requires adopting more efficient agricultural practices. Traditional irrigation systems, often imprecise, contribute to water wastage. The use of embedded systems and machine learning offers a solution for optimizing irrigation according to local conditions and actual crop needs while contributing to food security and environmental sustainability. This study proposes an innovative approach to irrigation management, integrating real-time data and predictive models to improve irrigation efficiency. This study proposes an irrigation system based on embedded systems, using sensors and algorithms to collect and analyze data in order to optimize water management. The system adjusts irrigation levels according to specific crop needs, thus contributing to more sustainable water management. Using ML algorithms like linear regression algorithms to model the relationships between environmental factors and crop water requirements, enabling accurate prediction of required irrigation levels based on data collected by sensors. The use of embedded systems such as the ESP32, combined with temperature, humidity, and water level sensors, has enabled the development of an autonomous and efficient system for collecting data in real-time and processing it for decision-making. The proposed model has an MAE of 0.8434, an RMSE of 0.8434, and a coefficient (R2 Score) of 0.4044, offering soil moisture prediction accuracy. Furthermore, the training time of our model is 0.00253 seconds, while the prediction time is 0.00117 seconds. These results show not only the performance of the proposed model in terms of accuracy but also its computational efficiency, outperforming some of the studies mentioned. The results of the study show a significant reduction in water consumption, with a marked improvement in water

关键词： Climate change Embedded systems Smart agriculture Irrigation Food security Optimization methods Machine learning Machine learning algorithms Sustainable development Water resources Water monitoring Environmental management

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UAV Path Planning Based on Simultaneous Optimization of Monitoring Frequency and Security

UAV Path Planning Based on Simultaneous Optimization of Moni...

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第34届中国控制与决策会议

作者： Yifei Shu Yang Chen Mian Hu Huaiyu Wu Xingang Zhao Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

UAVs have great potential when applied to persistent monitoring,but there are still problems such as difficulty in ensuring the monitoring frequency and easy leakage of monitoring path ***,it is necessary to increase the UAV monitoring frequency of targets and the randomness of monitoring paths as much as possible on the premise of covering all monitoring *** response to the above problems,this paper studies the UAV path planning problem of simultaneous optimization of monitoring frequency and path *** the evaluation of monitoring frequency and path security based on monitoring overdue time and path entropy,a mathematical model for UAV path planning is *** improved ant colony algorithm based on the monitoring overdue time(Overdue-aware Ant Colony Optimization,OACO) is designed,and finally the UAV flight trajectory with high monitoring frequency and the high monitoring path security is *** simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node to be accessed,improve the security of the UAV monitoring path,which is of great significance for enhancing monitoring security and preventing intrusion.

关键词： UAV persistent monitoring path planning monitoring frequency overdue time security

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Collision-free Navigation of Magnetic Mobile Microrobot in Multiple Scenarios

Collision-free Navigation of Magnetic Mobile Microrobot in M...

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IEEE International Conference on robotics and Biomimetics

作者： Yueying Tang Yi Xing You Li Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781665481106

Microrobot has been developed for the manipulation of micro- and nanoscale objects for many biological and manufacturing tasks. However, there are still challenges in the collision-free operation of microrobots in multiple scenarios. Essential functions, such as path planning and autonomous and adaptive navigation in dynamic environments, are urgently needed to implement microrobots in practical scenarios. Here, we implement path planning and the autonomous navigation of microrobots in 2D static obstacle scenarios and micro-mazes. A probabilistic-based RRT algorithm is employed to speed up the path search in static obstacle scenarios. The Dijkstra algorithm is employed in the micro-maze navigation of the microrobot to avoid the narrow and long pipeline effect. To further ease the active manipulation of magnetic mobile microrobots, a handle-based interactive operation is developed to realize the operational tasks in the three-dimensional space based on artificial vision planning and navigation, which improves the interactivity and flexibility of the system, and supplements the current limitation of autonomous navigation on the three-dimensional space.

关键词： Visualization Navigation Magnetic resonance imaging Heuristic algorithms Probabilistic logic Path planning Real-time systems

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DG-NBV: A Cognitive Framework for Direct Generation of Next Best View in Continuous View Space

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IEEE Transactions on Cognitive and Developmental systems 2024年

作者： Liu, Zhicheng Cao, Zhiqiang Li, Jianjie Guan, Peiyu Yu, Junzhi Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Peking University Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

The next best view (NBV) plays an important role in the three-dimensional object reconstruction. Existing NBV methods mainly adopt the generate-and-test strategy to select the best view in the discrete view space. This affects the adaptation to diverse objects. To solve this problem, a new NBV paradigm to directly generate the next best view in the continuous view space according to the input point cloud is proposed. Specifically, a point cloud feature extraction module with learnable view and position tokens is presented. These tokens are added upon the neighborhood and the position features of the point cloud to fully mine global contextual information, enhancing the feature representation. The predicted view from the proposed network is linked to a pre-trained view evaluation network. By duplicating this prediction view and then concatenating the duplication result with the extracted point cloud feature, the evaluation network is endowed with the ability to evaluate arbitrary views. Take the evaluation score of the evaluation network corresponding to the predicted view as the supervision signal, the network is trained. In this way, an effective solution of the next best view selection in the continuous view space is obtained. Accordingly, the adaptability to different objects is reinforced. Experimental results on the ShapeNet and MIT CSAIL datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Feature extraction

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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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Rail-Guided Multi-Robot System for the Cooperative Manipulation of Cross-Scale Targets

Rail-Guided Multi-Robot System for the Cooperative Manipulat...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Han Tao Huaping Wang Yaozhen Hou Siyu Guo Kaijun Lin Maolin Wang Qiang Huang Toshio Fukuda Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology China Ministry of Education Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

In the field of biomedicine, contact micromanipulations are mostly based on multi-robot coordinated operations to achieve mechanical contact interactions with a single type of target in a specific task scenario, which has advantages such as high repeatability, high precision and high stability. With the rapid development of biomedical technology, the increasingly complex biological micromanipulation process has been transformed from a single task to a task sequence of several subtasks, which puts forward higher requirements for the coordination, flexibility, operation accuracy and so on. However, due to the immutable configuration of the end-effector, most microrobotic manipulation systems have a fixed operation dimension, which makes it difficult to include the whole processing of complex tasks and meet the differentiated needs of different operational tasks and objectives. Therefore, this paper proposes a formation control based multi-robot dynamical reconfiguration collaborative micromanipulation to meet the dynamic requirements of multi-process complex tasks. By combining macro multi-robot formation control with collaborative micromanipulation, the dynamical reconfiguration of the end-effector under different manipulation tasks is realized. It can execute efficient and precise manipulation on fragile micro-objects ranging from 50 $\mu \mathrm{m}$ and 1000 $\mu \mathrm{m}$, and provide a new idea for accomplishing the whole processing of complex tasks, such as biological microassembly.

关键词： Viscosity Vibrations Rails Microscopy Collaboration End effectors Formation control

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All-weather,natural silent speech recognition via machine-learning-assisted tattoo-like electronics

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npj Flexible Electronics 2021年第1期5卷 183-191页

作者： Youhua Wang Tianyi Tang Yin Xu Yunzhao Bai Lang Yin Guang Li Hongmiao Zhang Huicong Liu YongAn Huang State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhanChina Flexible Electronics Research Center Huazhong University of Science and TechnologyWuhanChina School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced RoboticsSoochow UniversitySuzhouChina State Key Laboratory of Industrial Control Technologyg Institute of Cyber Systems and ControlZhejiang UniversityHangzhouChina

The internal availability of silent speech serves as a translator for people with aphasia and keeps human–machine/human interactions working under various *** paper develops a silent speech strategy to achieve all-weather,natural *** strategy requires few usage specialized skills like sign language but accurately transfers high-capacity information in complicated and changeable daily *** the strategy,the tattoo-like electronics imperceptibly attached on facial skin record high-quality bio-data of various silent speech,and the machine-learning algorithm deployed on the cloud recognizes accurately the silent speech and reduces the weight of the wireless acquisition module.A series of experiments show that the silent speech recognition system(SSRS)can enduringly comply with large deformation(~45%)of faces by virtue of the electricitypreferred tattoo-like electrodes and recognize up to 110 words covering daily vocabularies with a high average accuracy of 92.64%simply by use of small-sample machine *** successfully apply the SSRS to 1-day routine life,including daily greeting,running,dining,manipulating industrial robots in deafening noise,and expressing in darkness,which shows great promotion in real-world applications.

关键词： translator weather silent

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Development of Autonomous Underwater Robot for Navigation through Narrow Passages

Development of Autonomous Underwater Robot for Navigation th...

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IEEE International Conference on robotics and Biomimetics

作者： Yang Luo Xiyu Zhu Yuchuan Zhou You Li Zhihui Xu Yan Wei Wende Ke Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781665481106

Autonomous Underwater Vehicle (AUV), also called autonomous underwater robot, refers to an underwater robot that can independently complete tasks such as autonomous navigation, autonomous obstacle avoidance, and autonomous operation without human intervention. In some complex and narrow detection scenarios, autonomous underwater robots need to have a small volume, a high level of autonomy, and good maneuverability. Here, we design a miniature autonomous underwater robot driven by propellers, which integrates a high-performance processor, camera module, high-precision inertial measurement unit (IMU), etc. The attitude and position information of the robot is obtained in real-time through the IMU, and the processor controls the motion state of the six propellers, achieving the motion in six degrees of freedom in space. The highly integrated design ensures the maximum length of the whole machine within 15 cm, the 3-way PID controller design ensures an efficient and stable attitude control, and the high-performance processor provides more possibilities for target tracking and recognition. The developed AUV in this study shows superior navigation performance through narrow passages.

关键词： Autonomous underwater vehicles Propellers Attitude control Dynamics Process control Propulsion Aerospace electronics

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Adapting NIST Aerial Drone Tests for Thermal Identification, Inspection, and Suppression of Wildfires

Adapting NIST Aerial Drone Tests for Thermal Identification,...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Alex Fraley Ethan Nowery McCarthy Devine Adam Jacoff Paul Y. Oh Robotics Research Engineer for the Emergency Response Robots (Ground Aerial Aquatic) Project in the Intelligent Systems Division of the Engineering Laboratory National Institute of Standards and Technology (NIST) Gaithersburg MD USA Department of Mechanical Engineering University of Nevada Las Vegas (UNLV) Las Vegas NV USA Department of Aerospace Engineering University of Colorado Boulder (CU Boulder) Boulder CO USA Project Leader for Emergency Response Robots (Ground Aerial Aquatic) in the Intelligent Systems Division of the Engineering Laboratory National Institute of Standards and Technology (NIST) Gaithersburg MD USA Faculty of Mechanical Engineering University of Nevada Las Vegas

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

The National Institute of Standards and Technology (NIST) has developed standard test methods to evaluate the capabilities of aerial drones, ground robots, and aquatic vehicles, as well as the proficiency of their operators. The aerial drone tests measure key capabilities such as maneuverability, visual acuity, and thermal acuity. With a rising demand for aerial drones among first responders, these tests are essential for assessing system performance, mitigating risk, and improving operational effectiveness to ensure the safety of responders and the public. Given the increasing prominence of wildfire threats and the widespread destruction they cause, aerial drones are being adapted to help detect, inspect, and contain them. This paper outlines how NIST-developed aerial drone tests can be adapted to simulate wildfire threats and provide a framework of reproducible tests that measure the system capabilities of aerial drones and the proficiency of operators in mitigating these threats.

关键词： Heating systems Visualization Wildfires System performance NIST Inspection Writing Object recognition Underwater vehicles Drones

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