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第43届中国控制会议

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

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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.

关键词： Electronic Nose Fire Investigation Ignitable Liquids Convolutional Neural Network Cross Platform

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

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

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第43届中国控制会议

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

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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.

关键词： Depression Level EEG Graph Convolution Network Domain Generalization

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GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation

arXiv

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arXiv 2024年

作者： Bilić, Ivan Marić, Filip Bonsignorio, Fabio Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems Laboratory Canada

In autonomous robotics, measurement of the robot’s internal state and perception of its environment, including interaction with other agents such as collaborative robots, are essential. Estimating the pose of the robot arm from a single view has the potential to replace classical eye-to-hand calibration approaches and is particularly attractive for online estimation and dynamic environments. In addition to its pose, recovering the robot configuration provides a complete spatial understanding of the observed robot that can be used to anticipate the actions of other agents in advanced robotics use cases. Furthermore, this additional redundancy enables the planning and execution of recovery protocols in case of sensor failures or external disturbances. We introduce GISR - a deep configuration and robot-to-camera pose estimation method that prioritizes execution in real-time. GISR consists of two modules: (i) a geometric initialization module that efficiently computes an approximate robot pose and configuration, and (ii) a deep iterative silhouette-based refinement module that arrives at a final solution in just a few iterations. We evaluate GISR on publicly available data and show that it outperforms existing methods of the same class in terms of both speed and accuracy, and can compete with approaches that rely on ground-truth proprioception and recover only the pose. Our code is available at https://***/iwhitey/GISR-robot. © 2024, CC BY.

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network 42

AMG: A Depression Detection Model with Autoencoder and Multi...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Han-Guang Meng, Qing-Hao Jin, Li-Cheng Wang, Jian-Bang Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China China Southern Power Grid Company Limited Guangzhou510623 China

ISBN: (纸本)9789887581543

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Decision trees

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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High-compatibility Manipulation Method for Parallel In-situ Isolation of Microalgal Cells via Optoelectronic Tweezers

High-compatibility Manipulation Method for Parallel In-situ ...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Qin, Shilong Liu, Jiaxin Chen, Yuxin Yang, Haotian Zhao, Ziyi Shi, Qing Huang, Qiang Wang, Huaping Beijing Institute of Technology Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing100081 China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

ISBN: (纸本)9798350372601

Isolating microalgae in in-situ liquid environment based on optoelectronic tweezers (OETs) enables providing specific species of microalgae in high-purity, thus has significant potential in environmental detection, aquaculture, bioenergy, and biomedicine development. However, the conventional OET method can only separate and extract one type of microalgae in a single-step operation and is challenging to utilize the opposite dielectrophoretic force when the cross frequency of microalgae is too high /low or even absent. In this study, a novel automated microalgae isolation method based on optoelectronic tweezers is proposed, which integrates two strategies: cluster manipulation and discretized synchronous manipulation. Firstly, an optoelectronic tweezer robot platform was built, the dielectrophoretic forces on microalgae and the factors affecting the dielectrophoretic forces were analyzed. Secondly, a cluster manipulation strategy based on light patterns dynamic locomotion structure scheme was designed to simultaneously separate microalgae subject to dielectrophoretic forces of different attributes and the same attribute. Finally, a discretized synchronous manipulation strategy based on visual feedback and PID controller was designed to realize the precise isolation of microalgae subjected to the same dielectrophoretic force and different dielectrophoretic forces. Experimental results demonstrated that the aforementioned method provides a feasible microalgae isolating method based on optoelectronic tweezers within 10 minutes, which has potential applications in the in-situ water contamination detection and treatment. © 2024 IEEE.

关键词： Microorganisms

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Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 146-159页

作者： Yinlong Zhang Wei Liang Mingze Yuan Hongsheng He Jindong Tan Zhibo Pang Key Laboratory of Networked Control Systems Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Computing Wichita State UniversityWichita 67260 USA Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeTN 37996 USA Department of Automation Technology ABB Corporate Research SwedenVasteras 72178Sweden Department of Intelligent Systems Royal Institute of Technology(KTH)Stockholm 11428Sweden

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment ***,traditional calibrations suffer inaccuracy and *** address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying *** our method,the spatial relationship is geometrically correlated between the sensing units and robotic *** decoupled estimations on rotation and translation could reduce the coupled errors during the ***,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and *** calibration has been evaluated on our developed *** the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,*** comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

关键词： Calibration inertial measurement unit(IMU) monocular camera robotic arm spiral moving trajectory

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Kinematic Control of a Mecanum Mobile Robot using Time-Varying Model Predictive Control

Kinematic Control of a Mecanum Mobile Robot using Time-Varyi...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Seyed Ali Shahzadeh Fazeli Sohrab Allahyari Arman Gholibeikian S. Ali A. Moosavian Dept. of Mechanical Engineering Advanced Robotics & Automated Systems (ARAS) Laboratory Center of Excellence in Robotics & Control K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Mecanum wheeled mobile robots have increasingly caught researchers' attention due to their wide range of applications and higher maneuverability. However, their complex structure makes their trajectory tracking and control problem more challenging. This paper presents a cascaded controller for trajectory tracking of a four-wheel Mecanum mobile robot. The cascaded controller consists of a linear time-varying model predictive controller (LTV-MPC) and an inner PID controller. The proposed controller does not require the system's dynamics model. To this end, the linear time-varying error model used to design the MPC is obtained from the robot's kinematics model. The angular velocities obtained from the predictive controller are then mapped into the required commands for the DC motors using a PID controller. The PID controller is fine-tuned considering the motor's dead zone using the Genetic Algorithm. Simulations and experimental results demonstrate the superiority of the cascaded controller over the regular LTV-MPC. The proposed algorithm can also be used for other real-time implementations of robotic tasks.

关键词： Trajectory tracking System dynamics Robot kinematics Predictive models DC motors Angular velocity Real-time systems Mobile robots Voltage control Genetic algorithms

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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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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Tang, Yueying Xing, Yi Li, You Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Rehabilitation Robotics in Universities Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation Shenzhen518055 China

ISBN: (纸本)9781665481090

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. © 2022 IEEE.

关键词： Navigation

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Towards Camera Parameters Invariant Monocular Depth Estimation in Autonomous Driving

Towards Camera Parameters Invariant Monocular Depth Estimati...

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European Conference on Mobile Robots (ECMR)

作者： Karlo Koledić Ivan Marković Ivan Petrović Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Zagreb Croatia

Monocular depth estimation is an effective approach to environment perception due to simplicity of the sensor setup and absence of multisensor calibration. Deep learning has enabled accurate depth estimation from a single image by exploiting semantic cues such as the sizes of known objects and positions on the ground plane thereof. However, learning-based methods frequently fail to generalize on images collected with different vehicle-camera setups due to the induced perspective geometry bias. In this work, we propose an approach for camera parameters invariant depth estimation in autonomous driving scenarios. We propose a novel joint parametrization of camera intrinsic and extrinsic parameters specifically designed for autonomous driving. In order to supplement the neural network with information about the camera parameters, we fuse the proposed parametrization and image features via the novel module based on a self-attention mechanism. After thorough experimentation on the effects of camera parameter variation, we show that our approach effectively provides the neural network with useful information, thus increasing accuracy and generalization performance.

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