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

作者： Dai, Yanping Chen, Wenyuan Xi, Ning Wang, Wenxue Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Emerging Technologies Institute University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong

ISBN: (纸本)9798350307320

Detecting human movement intentions and transmitting human operational capabilities to robots is of great significance for teleoperations. However, the difference in kinematic structure between human hands and robotic manipulators brings difficulties to teleoperation mapping. In this paper, we present an approach to hand grasp recognition and mapping based on feature extraction to drive heterogeneous remote manipulator. The hand grasp data set HUST recorded with a data glove was used for this study. The data were preprocessed and their features were extracted by PCA. A neural network-based classifier was obtained through offline training of human grasping activities. We compared different network models and selected the network with the best performance based on the training time and the task requirements of teleoperation. The experimental results showed that the recognition accuracy of dynamic grasping motion with feature extraction is higher than that without feature extraction and can be up to 97.47%. Finally, we validated the feasibility of this method to tele-operate an Barrett hand through online experiments. Our proposed method provides an effective approach of mapping natural human grasping operations to heterogeneous robotic teleoperation systems. © 2023 IEEE.

关键词： Feature extraction

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

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

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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A Biofuel Risk Management Model for Biomass-Based Heat Sources Considering Supply-Demand-Side Uncertainties 12

A Biofuel Risk Management Model for Biomass-Based Heat Sourc...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

作者： Fu, Dianzheng Zhou, Tingting Yang, Tianji Zhang, Tao Dong, Jingya Key Laboratory of Networked Control Systems Institutes for Robotics and Intelligent Manufacturing Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Technical Management Division Guodian Technology & Environment Group Co Ltd Beijing China Digital Factory Department Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839538360

Biofuel-based heat sources are gradually penetrated into heating systems in Northern China in the background of carbon neutrality. It is essential to conduct biofuel risk management study, not only for enhancing the ability of continuous heat supply for heating systems, but also improving the ability of effective cost control. A biofuel risk management model is thus proposed innovatively for biomass-based heat sources considering supply-demand-side uncertainties. The developed model integrates Chance-constrained programming, Two-stage stochastic programming and the traditional linear programming into a general framework. After that, the developed model is applied in to a real heat-source case of biofuel risk management under uncertainties and the corresponding solutions on biofuel risk management are generated. The beneficial results of biofuel allocation pattern can be obtained for decision-makers to provide the fuel management support under different risk levels. Besides, a trade-off analysis between the constraint-violation risk and the system cost can also be provided to conduct in-depth analysis. © 2022 IEEE.

关键词： Risk management

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Less Is More: Robust Robot Learning via Partially Observable Multi-Agent Reinforcement Learning

arXiv

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arXiv 2023年

作者： Zhao, Wenshuai Rantala, Eetu-Aleksi Salimpour, Sahar Pajarinen, Joni Peña-Queralta, Jorge Department of Electrical Engineering and Automation Aalto University Finland Lab University of Turku Finland Institute of Robotics and Intelligent Systems ETH Zurich Switzerland

In many multi-agent and high-dimensional robotic tasks, controllers can be optimized centrally or decentrally, using either single-agent reinforcement learning (SARL) or multiagent reinforcement learning (MARL). However, the relationship between these two paradigms is not well-studied. This work aims to systematically investigate the robustness and performance of SARL and MARL in the same task. We first analytically show that independent Gaussian policies optimized by policy-gradient based SARL and MARL are equivalent under full-state observations. Following, we empirically show that in certain inherently single-agent tasks, perhaps surprisingly, we can use multiple agents to control a robot such that each agent only has access to partial observations. Since in these cases an agent does not depend on full state information multi-agent policies can provide additional robustness to perturbations and failures. Experiments on an illustrative decentralized control task and a mobile manipulation task with a real robot show that multiple agents with access to partial observations outperform a single agent when parts of the system fail. © 2023, CC BY.

关键词： Adversarial machine learning

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Hazard Analysis of Collaborative automation systems: A Two-layer Approach based on Supervisory Control and Simulation

Hazard Analysis of Collaborative Automation Systems: A Two-l...

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IEEE International Conference on robotics and automation (ICRA)

作者： Tom P. Huck Yuvaraj Selvaraj Constantin Cronrath Christoph Ledermann Martin Fabian Bengt Lennartson Torsten Kröger Intelligent Process Automation and Robotics Lab Institute of Anthropomatics and Robotics (IAR-IPR) Karlsruhe Institute of Technology India Department of Electrical Engineering Division of Systems and Control Chalmers University of Technology Gothenburg Sweden

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory Control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

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Atomic force microscopy for revealing micro/nanoscale mechanics in tumor metastasis:from single cells to microenvironmental cues

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中国药理学报（英文版） 2021年第3期42卷 323-339页

作者： Mi Li Ning Xi Yue-chao Wang Lian-qing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Department of Industrial and Manufacturing Systems Engineering The University of Hong KongHong KongChina

Mechanics are intrinsic properties which appears throughout the formation,development,and aging processes of biological *** have been shown to play important roles in regulating the development and metastasis of tumors,and understanding tumor mechanics has emerged as a promising way to reveal the underlying mechanisms guiding tumor *** particular,tumors are highly complex diseases associated with multifaceted factors,including alterations in cancerous cells,tissues,and organs as well as microenvironmental cues,indicating that investigating tumor mechanics on multiple levels is significantly helpful for comprehensively understanding the effects of mechanics on tumor ***,diverse techniques have been developed for probing the mechanics of tumors,among which atomic force microscopy(AFM)has appeared as an excellent platform enabling simultaneously characterizing the structures and mechanical properties of living biological systems ranging from individual molecules and cells to tissue samples with unprecedented spatiotemporal resolution,offering novel possibilities for understanding tumor physics and contributing much to the studies of *** this review,we survey the recent progress that has been achieved with the use of AFM for revealing micro/nanoscale mechanics in tumor development and *** and future progress are also discussed.

关键词： atomic force microscopy tumor mechanics cancerous cell tumor microenvironment exosome extracellular matrix

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Joint Moment Estimation for Hip Exoskeleton Control: A Generalized Moment Feature Generation Method

arXiv

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arXiv 2024年

作者： Zhang, Yuanwen Xiong, Jingfeng Xian, Haolan Chen, Chuheng Chen, Xinxing Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China The Key Laboratory of Image Processing and Intelligent Control The Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 ± 0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction (p Copyright © 2024, The Authors. All rights reserved.

关键词： Exoskeleton (robotics)

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On Networked Dynamical systems with Heterogeneous Constraints:Equilibrium Points and Stability 41

On Networked Dynamical Systems with Heterogeneous Constraint...

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第41届中国控制会议

作者： Qichao Ma Jiahu Qin Yu Kang Huijun Gao Department of Automation University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Institute of Advanced Technology University of Science and Technology of China Research Institute of Intelligent Control and Systems and the State Key Laboratory of Robotics and System Harbin Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we investigate multi-agent systems with heterogeneous constraints,which can represent the heuristic beliefs of the agents towards an issue or the physical constraints of the *** typical example is a group of interacting unmanned aerial vehicles in a complex environment with multiple physical *** a result of the heterogeneity of the constraints,an equilibrium point may not exist and is presumably to be a dissensus point when it *** investigate the existence of equilibrium points from the perspective of Kakutani's fixed point theory for a set-valued *** also prove the local/global stability of certain equilibrium ***,a special case that the constraints are homogeneous is taken into *** constrains are assumed to take interval forms with nonempty *** is proved that consensus can be achieved globally and asymptotically for this *** examples are designed to illustrate our theoretical findings.

关键词： Multi-agent systems heterogeneous constraints equilibrium point and its stability constrained consensus

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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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Design and Develop a Vertical Box Smart Farm based on Programmable Logic Controller

Design and Develop a Vertical Box Smart Farm based on Progra...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Thananop Tummawai Peeraput Prakhummin Kittisak Ledkaew Anocha Phongsatja Witayakorn Lunsungyang Somboon Sukpancharoen Department of Mechanical Engineering Khon Kaen University Khon Kaen Thailand Department of Automation Robotics and Intelligent Systems Khon Kaen University Khon Kaen Thailand Department of Agricultural Engineering Khon Kaen University Khon Kaen Thailand

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Agriculture plays an indispensable role in daily life, and presently, soilless cultivation techniques such as hydroponics enjoy widespread popularity. In efforts to advance this methodology, we have implemented a sophisticated Programmable Logic Controllers (PLCs) system tailored for Nutrient Film Technique (NFT) hydroponic cultivation within a greenhouse setting. This system incorporates a diverse array of sensors designed to measure crucial parameters including water temperature, Electrical Conductivity (EC), and Potential of Hydrogen ion ($\mathbf{p H}$) levels. The PLCs is meticulously programmed to oversee and regulate the entire cultivation process seamlessly. Additionally, integration of a Raspberry Pi facilitates Internet of Things (IoT) functionalities, thereby enhancing convenience and enabling remote monitoring capabilities. We aim to conduct a comparative analysis between two distinct greenhouse systems: one under the control of the PLC, and the other devoid of such automated control, in order to discern and evaluate the disparities in plant growth and development between them. The study focuses on key variables such as canopy width and leaf count of Green Oak Lettuce over a 5 -week period, with the primary environmental factors influencing these variables being the levels of EC and pH in the nutrient solution. The findings of this study will provide valuable insights into the benefits of environmental control in greenhouse-based hydroponic cultivation of green oak lettuce, contributing to the advancement of sustainable and efficient agricultural practices.

关键词： Temperature sensors Temperature measurement Green products Programmable logic devices Hydroponics Nutrients Stability analysis Internet of Things Remote monitoring Sensor arrays

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