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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Zhang, Zhiyuan Ahmed, Daniel Acoustic Robotics Systems Laboratory Institute of Robotics and Intelligent Systems Department of Mechanical and Process Engineering ETH Zurich Zurich Switzerland

ISBN: (纸本)9781733419048

Microparticle manipulation actuating by external fields, magnetic fields and acoustic fields, are attractive for lab-on-chip systems and microscale medical applications since they allow for the manipulation of microparticles without contact, and unaffected by object opacity. The combination of both fields could overcome the limitations encountered by single actuation methods and provide more freedoms of operations. Here we demonstrate the intentional control over the local orientation and pattern of microparticles in a bulk liquid medium, impelled by a combination of magnetic and acoustic fields. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： Acoustic fields

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Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

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Tsinghua Science and Technology 2024年第1期29卷 244-256页

作者： Wenjie Geng Zhiqiang Cao Peiyu Guan Fengshui Jing Min Tan Junzhi Yu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Grasp detection plays a critical role for robot *** pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and ***,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are *** is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains ***,the channel information in high-level feature is also not well ***,the performance of grasp detection is *** solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle *** low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature *** a hierarchical fusion guarantees the quality of grasp ***,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective *** such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the ***,an information enhancement module is added before the encoder to reinforce input *** proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.

关键词： grasp detection hierarchical multi-scale feature fusion skip connections with attention inverted shuffle residual

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A Multi-Gas Detection System Based on Convolutional Neural Network 43

A Multi-Gas Detection System Based on Convolutional Neural N...

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43rd Chinese Control Conference, CCC 2024

作者： Chen, Si-Wen Li, Hong-Yue Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared (NDIR) multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), and propane (C3 H8). We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network (1D-CNN) and depthwise separable convolutional neural network (DS-CNN). The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Air quality

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization 43

A Depression Level Classification Model Based on EEG and GCN...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Zhongyi Xu, Chenyang Jin, Licheng Hou, Huirang Meng, Qinghao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network (GCN) to identify levels of depression. Additionally, to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art (SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Convolution

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids 43

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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43rd Chinese Control Conference, CCC 2024

作者： Song, Tian-Shu Chen, Zhen-Peng Hou, Hui-Rang Li, Hong-Yue Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Detecting ignitable liquids (ILs) at the scene of a fire is crucial for fire investigation. The electronic nose (e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network (FMG-Net). This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network (SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs (diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset. © 2024 Technical Committ

关键词： Convolutional neural networks

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A Machine Learning-Driven Model for Predicting the Productivity of Internet of Mechanical Things 2nd

A Machine Learning-Driven Model for Predicting the Productiv...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Sedik, Ahmed Nasralla, Moustafa M. Esmail, Maged Abdullah Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

ISBN: (纸本)9789819783472

This study delves into the utilization of artificial intelligence (AI) for predicting the performance of Hemispherical Solar Stills (HSS), aiming to transcend reliance on empirical methods. Employing real-world experimental data, five distinct prediction models were developed. The investigation revealed that the DT model emerged as the most efficacious in estimating both hourly productivity and instantaneous efficiency, showcasing remarkable accuracy and efficiency in its predictions. Statistical metrics demonstrated that the DT model's estimates for HSS productivity and efficiency were closely aligned with unity. This research accentuates the transformative potential of AI models in streamlining and enhancing predictive techniques for estimating HSS performance. The findings advocate for the adoption of AI-driven approaches as cost-effective and precise solutions for researchers and practitioners within the realm of mechanical engineering. By substantiating the efficacy of the DT model, this study contributes valuable insights that not only advance our understanding of AI applications in solar technology but also offer practical tools for optimizing the design and performance assessment of Hemispherical Solar Stills. The integration of AI in this context promises to usher in a new era of efficiency and accuracy, addressing challenges in the field and fostering advancements in sustainable energy technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Distributed accelerated primal-dual neurodynamic approaches for resource allocation problem

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Science China(Technological Sciences) 2023年第12期66卷 3639-3650页

作者： ZHAO You HE Xing YU JunZhi HUANG TingWen College of Electronic and Information Engineering Southwest UniversityChongqing 400715China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China Science Program Texas A&M University at QatarDoha 2387Qatar

This paper investigates two distributed accelerated primal-dual neurodynamic approaches over undirected connected graphs for resource allocation problems(RAP)where the objective functions are generally *** the help of projection operators,a primal-dual framework,and Nesterov's accelerated method,we first design a distributed accelerated primal-dual projection neurodynamic approach(DAPDP),and its convergence rate of the primal-dual gap is O(1/(t^(2)))by selecting appropriate parameters and initial ***,when the local closed convex sets are convex inequalities which have no closed-form solutions of their projection operators,we further propose a distributed accelerated penalty primal-dual neurodynamic approach(DAPPD)on the strength of the penalty method,primal-dual framework,and Nesterov's accelerated *** on the above analysis,we prove that DAPPD also has a convergence rate O(1/(t^(2)))of the primal-dual *** with the distributed dynamical approaches based on the classical primal-dual framework,our proposed distributed accelerated neurodynamic approaches have faster convergence *** simulations demonstrate that our proposed neurodynamic approaches are feasible and effective.

关键词： accelerated primal-dual neurodynamic approaches RAP projection operators penalty method convergence rate O(1/(t^(2)))

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Multi-Aspect Optimal Sliding Mode Controller for a Mecanum Wheeled Robot 11

Multi-Aspect Optimal Sliding Mode Controller for a Mecanum W...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Allahyari, Sohrab Rahmanei, Hamid Moosavian, S. Ali A. K. N. Toosi University of Technology Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems Laboratory Dept. of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798350308105

Four-wheel drive Mecanum robots have gained attention due to their ability to move in all directions, which allows them to work in tight and complex environments. For this reason, ensuring precise control of these robots along predefined trajectories is crucial. This work presents a comparative study on the effects of parametric uncertainties, external disturbances, measurement noises, and initial deviations on the trajectory-Tracking performance of a 4-wheel drive Mecanum robot. First, the kinematic and kinetic models of the Mecanum robot are introduced. Then, the integral sliding mode control is designed, and proof of stability is provided. In addition, we explore other control strategies, including PID, feedback linearization, sliding mode, and adaptive sliding mode control, that are designed for this robot. Furthermore, the particle swarm optimization algorithm is hired for the controllers with a cost function as the total sum of average error, maximum error, and final error. Optimization has been done on all controllers to compare the results fairly. To appraise the effectiveness of the proposed control optimization methods, simulations are carried out in MATLAB/Simulink. The results reveal that the proposed ISMC method performs best in both defined scenarios compared to other controllers. However, it must be noted that the ISMC controller exhibits limitations when facing significant uncertainties in the dynamic model. This study underscores the effectiveness of our proposed control method and provides valuable insights into the practical implementation controller for the Mecanum robot in real-world applications. © 2023 IEEE.

关键词： Three term control systems

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Leveraging large language models for comprehensive locomotion control in humanoid robots design

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Biomimetic Intelligence & robotics 2024年第4期4卷 48-63页

作者： Shilong Sun Chiyao Li Zida Zhao Haodong Huang Wenfu Xu School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Inteligent Morphing Mechanisms and Adaptive Robotics Shenzhen 518055China Key University Laboratory of Mechanism&Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen 518055China

This paper investigates the utilization of large language models(LLMs)for the comprehensive control of humanoid robot *** reinforcement learning(RL)approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward *** address these challenges,we propose a method that employs LLMs as the primary designer to handle key aspects of locomotion control,such as trajectory planning,inverse kinematics solving,and reward function *** using user-provided prompts,LLMs generate and optimize code,reducing the need for manual *** approach was validated through simulations in Unity,demonstrating that LLMs can achieve human-level performance in humanoid robot *** results indicate that LLMs can simplify and enhance the development of advanced locomotion control systems for humanoid robots.

关键词： Humanoid robots Large language models Locomotion control Reinforcement learning

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An Affordable Telerobotic System Architecture for Grasp Training and Object Grasping for Human-Machine Interaction

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Journal of engineering and Science in Medical Diagnostics and Therapy 2024年第1期71卷 011201页

作者： Hazra, Sudip Abdul Rahaman, Abdul Hafiz Shiakolas, Panos S. Mars Lab Mechanical & Aerospace Engineering The University of Texas at Arlington ArlingtonTX76019 United States Micro Medical Manufacturing Automation Robotics Systems Laboratory Mechanical & Aerospace Engineering The University of Texas at Arlington ArlingtonTX76019 United States

Due to mobility impairment, a person might rely on wheelchairs, canes, and crutches for assistance but could face challenges when performing tasks such as grasping and manipulating objects due to limitations in reach and capability. To overcome these challenges, a multidegree-of-freedom robotic arm with an anthropomorphic robotic hand (ARH) could be used. In this research, we propose an architecture and then implement it toward the development of an assistive system to assist a person with object grasping. The architecture interlinks three functional modules to provide three operation modes to calibrate the system, train a user on how to execute a grasp, synthesize grasps, and execute a grasp. The developed system consists of a user input and feedback glove capable of capturing user inputs and providing grasp-related vibrotactile feedback, a COPPELIASIM virtual environment emulating the motions of the ARH, and an underactuated ARH capable of executing grasps while sensing grasp contact locations. The operation of the developed system is evaluated to determine the ability of a person to operate it and perform a grasp using two control methods;using a synthesized grasp or under real-time continuous control. The successful evaluation validates the architecture and the developed system to provide the ability to perform a grasp. The results of the evaluation provide confidence in expanding the system capabilities and using it to develop a database of grasp trajectories of objects with different geometries. Copyright © 2023 by ASME.

关键词： Human robot interaction

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