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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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Affine Formation Maneuver Control for Multiagent systems Based on Unknown Input Reconstruction

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IEEE Access 2025年 13卷 102311-102322页

作者： Liduo Hu Wei Zhang Chenhang Yan Yuhang Cai Hao Chen Zhi Hu Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing China

Disturbances can significantly impact the dynamic performance and stability of a system, making them a key focus in stability analysis. However, direct measurement of disturbances is often challenging due to technological and cost limitations. These disturbances introduce uncertainties that complicate multi-agent coordination, particularly in formation control tasks. Consequently, designing effective control strategies for multi-agent systems becomes both challenging and crucial. This paper addresses the affine transformation formation control problem of multi-agent systems in the presence of unknown disturbances. We begin by using a state-space model to describe agent dynamics and employing an unknown input observer to estimate both the state and the unknown disturbances. Based on these estimates, we redesign the control law to ensure that tracking errors converge to an adjustable residual set. This distributed control approach allows agents to operate without global information, thereby reducing communication requirements. Finally, a simulation example demonstrates the feasibility and effectiveness of the proposed control law.

关键词： Formation control Observers Laplace equations Directed graphs Vectors Multi-agent systems Protocols Matrix decomposition Aerospace electronics Vehicle dynamics

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A Domain Generalization Method for E-nose Cross-platform Identification of Ignitable Liquids

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IEEE Sensors Journal 2025年

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

Electronic nose (e-nose) can carry out odor fingerprinting and identification, exhibiting several advantages such as speed, low cost and portability. The e-nose technology allows objective, accurate and real-time detection and recognition of ignitable liquids (ILs), which provide technical support for fire cause determination and fire scene reproduction. Metal oxide semiconductor (MOS) gas sensors have certain technical limitations, and multiple e-nose platforms assembled using the same sensor array will produce different output responses when sampling the same gas. Manual data labeling and repetitive training of models involve a huge workload. This study proposes a novel domain generalization method to mitigate this problem. During the training process, we assign pseudo-domain labels iteratively for the e-nose response signals obtained from multiple source domains by clustering. This approach avoids the situation where multiple potential domains can exist for the same e-nose. The highlights of our approach are as follows: 1) The training paradigm of traditional domain generalization methods that require domain labels is not used;2) Pseudo-domain labels are generated for the samples according to each cycle iteration during training;3) The feature extractor is trained to learn domain-invariant features by adversarial learning based on pseudo-domain labels;4) The statistics of each hidden layer of the neural network are used to cluster the e-nose signals and generate pseudo-domain labels from them. Five e-noses, each with 16 identical sensor channels, are used to recognize four types of ILs. Our method achieves an average cross-platform recognition accuracy of 85.2%. © 2001-2012 IEEE.

关键词： Deep neural networks

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A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image

arXiv

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arXiv 2023年

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

autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot’s kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results. © 2023, CC BY-NC-ND.

关键词： Manipulators

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Versatile Jumping of Humanoid Robots via Curriculum-Assisted Reinforcement Learning

Versatile Jumping of Humanoid Robots via Curriculum-Assisted...

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Chinese Automation Congress (CAC)

作者： Ruiyu Tan Xueai Li Fenglei Ni Dong Zhou Yi Ji Xiangyu Shao Key Laboratory of Autonomous Intelligent Unmanned Systems Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Versatile jumping is crucial for agile and adaptive behavior of humanoid robots in complex environments. However, humanoid robots have to manage impact forces while maintaining balance and stability throughout the take-off, flight, and landing phases, which imposes significant challenges for motion planning and controller design. This paper proposes a curriculum-assisted reinforcement learning framework for the humanoid robot to achieve various jumping tasks. The curriculum learning divides the jump training process into multi-stages and design different episodes in each stage to guarantee the training feasibility and generalize learned skills. Simulations on a 28 DOFs humanoid robot verify the excellent diverse jumping capability of the proposed policy. This exploration of different tasks also proves the policy's robustness, allowing the robot quickly recover from perturbations using a variety of learned maneuvers.

关键词： Training Automation Perturbation methods Humanoid robots Reinforcement learning Robustness Stability analysis Planning

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Robustness and Push Recovery of Humanoid Robots Using Predictive and Whole-Body Control

Robustness and Push Recovery of Humanoid Robots Using Predic...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Shaojie Zhang Fenglei Ni Xueai Li Xu Li Yi Ji Xiangyu Shao State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Key Laboratory of Autonomous Intelligent Unmanned Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Robustness walking and recovery ability in uneven terrains and unexpected collisions are crucial for the practical application of humanoid robots. However, existing methods struggle to effectively balance stability, motion safety and walking agility. This paper proposes a hybrid control strategy combing Nonlinear Model Predictive Control (NMPC) and Whole-Body Control (WBC) to improve the robot's walking stability and reaction safety in collisions situations. The NMPC generates long-term and macroscopic movements, with capture point theory being utilized for foot placements determination under external impacts. On this foundation, the WBC regulates the robot refinedly in real-time based on task priorities and weights. Simulations verify the effectiveness of the proposed method, involving uneven terrain walking and external impacts rejection.

关键词： Legged locomotion Resistance Humanoid robots Stability analysis Robustness Real-time systems Planning Collision avoidance Motion analysis Predictive control

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet 42

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, De-Xiu Meng, Qing-Hao Han, Rui-Xue Li, Hong-Yue 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 Tianjin Navigation Instruments Research Institute Tianjin300131 China

ISBN: (纸本)9789887581543

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Electronic nose

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Modeling and Planar Trajectory Tracking Control of an MJ-AUV 42

Modeling and Planar Trajectory Tracking Control of an MJ-AUV

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42nd Chinese Control Conference, CCC 2023

作者： Omar, Zakarya Liu, Ke-Xian Ren, Chao Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin300072 China Sana'a University Faculty of Engineering Sana'a Yemen

ISBN: (纸本)9789887581543

A Multi-Joint autonomous Underwater Vehicle (MJ-AUV) is designed and equipped with multiple movable segments or joints that allow for greater maneuverability and flexibility in the water. These joints allow the MJ-AUV to change its shape, orientation, and direction of movement, so it can access tight spaces or navigate in complex underwater environments, making it useful for a variety of tasks in the deep sea, such as surveillance, exploration, and maintenance. However, the multiple joint structure of the MJ-AUV complicates the dynamics of the system. Furthermore, the joints' rotation causes misalignment of the cabins, causing deviations in the desired forces and torques. To address these issues, this study focuses on the 2-dimensional (2D) modeling as well as the trajectory tracking controller of the MJ-AUV. In this setting, the proposed controller utilizes a hierarchically structured robust nonlinear back-stepping and sliding-mode approach. Hence, this controller is simulated under various uncertainties and disturbances and the results show that it presents a global finite-time convergence to a bounded neighborhood of the origin for both position and velocity tracking errors, demonstrating its effectiveness and robustness. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： autonomous underwater vehicles

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A Multi-Gas Detection System Based on Convolutional Neural Network

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

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第43届中国控制会议

作者： Si-Wen Chen Hong-Yue Li Hui-Rang Hou 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

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（CO）,carbon monoxide（CO）, nitric oxide（NO）, sulfur dioxide（SO）, and propane（CH）. 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.

关键词： Non-Dispersive Infrared Multi-gas Detection System Pattern Recognition Convolutional Neural Network

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Bidirectional Human Interactive AI Framework for Social Robot Navigation

arXiv

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arXiv 2024年

作者： Girgin, Tuba Girgin, Emre Yildirim, Yigit Ugur, Emre Haklidir, Mehmet Robotics and Autonomous Systems Laboratory TUBITAK BILGEM Kocaeli41470 Turkey Department of Computer Engineering Bogazici University Istanbul Turkey

Trustworthiness is a crucial concept in the context of human-robot interaction. Cooperative robots must be transparent regarding their decision-making process, especially when operating in a human-oriented environment. This paper presents a comprehensive end-to-end framework aimed at fostering trustworthy bidirectional human-robot interaction in collaborative environments for the social navigation of mobile robots. In this framework, the robot communicates verbally while the human guides with gestures. Our method enables a mobile robot to predict the trajectory of people and adjust its route in a socially-aware manner. In case of conflict between human and robot decisions, detected through visual examination, the route is dynamically modified based on human preference while verbal communication is maintained. We present our pipeline, framework design, and preliminary experiments that form the foundation of our proposition. © 2024, CC BY.

关键词： Mobile robots

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