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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Zhang, Shaojie Ni, Fenglei Li, Xueai Li, Xu Ji, Yi Shao, Xiangyu 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: (纸本)9798350384185

Robustness walking and recovery ability in uneven terrains and unexpected collisions are crucial for the practical application of humanoid ***, existing methods struggle to effectively balance stability, motion safety and walking *** 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 *** NMPC generates long-term and macroscopic movements, with capture point theory being utilized for foot placements determination under external *** this foundation, the WBC regulates the robot refinedly in real-time based on task priorities and *** verify the effectiveness of the proposed method, involving uneven terrain walking and external impacts rejection. © 2024 IEEE.

关键词： Anthropomorphic robots

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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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2024 China Automation Congress, CAC 2024

作者： Tan, Ruiyu Li, Xueai Ni, Fenglei Zhou, Dong Ji, Yi Shao, Xiangyu 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: (纸本)9798350368604

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. © 2024 IEEE.

关键词： Anthropomorphic robots

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Analysis and Optimization of Unbalanced Magnetic Pull for Eccentric Doubly-Fed Induction Motor

Analysis and Optimization of Unbalanced Magnetic Pull for Ec...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Kang, Haoyu Wang, Jin Liu, Liyang Sun, Lu Li, Zequan Zhou, Libing Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China Robotics and Autonomous Systems Thrust Guangzhou China

ISBN: (纸本)9798350370607

Due to the frequent switching of working conditions, the large doubly-fed induction motors (DFIMs) utilized in variable speed pumping units are subjected to intense hydraulic shock, thereby significantly increasing the risk of rotor eccentricity fault and posing a substantial threat to the safe operation of the motor. This paper proposes a method for suppressing the unbalanced magnetic force in DFIMs caused by eccentricity, through the injection of excitation compensation current in groups. Firstly, the air gap magnetic field of DFIMs with eccentricity is modeled using the subdomain method. Then, the optimal compensation current is efficiently calculated using the eccentric subdomain model in conjunction with the particle swarm optimization algorithm. Finally, the proposed method is implemented on a 10MW DFIM. By injecting the optimal compensation current, the unbalanced magnetic pulling force caused by eccentricity is effectively suppressed. The comparison with the finite element simulation results showed that the unbalanced magnetic pulling force is reduced by 81.6%. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Unified Framework for Vertical States Estimation: Data-Driven Approach Incorporating Uncertainty 24

A Unified Framework for Vertical States Estimation: Data-Dri...

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4th International Conference on robotics and Control Engineering, RobCE 2024

作者： Shangguan, Zhegong Mrhasli, Younesse El Atheupe, Gaël P. Mouton, Xavier Monsuez, Bruno Tapus, Adriana Autonomous Systems and Robotics Laboratory/U2IS ENSTA Paris Institut Polytechnique de Paris Palaiseau France Chassis Systems Department Groupe RENAULT Guyancourt France

ISBN: (纸本)9798400716782

Accurate and real-time knowledge of vertical states plays a critical role in vehicular safety and comfort. It contributes to enhanced cornering performance, detailed modeling of vehicle dynamics, and effective implementation of control strategies for numerous active safety systems, especially semi-active and active suspensions. For this purpose, this study aims to estimate three key dynamical states that are impractical to acquire via direct measurements: tire vertical force, road profile, and unsprung mass velocity. A unified framework tailored to the context of vertical dynamics is proposed, leveraging a state-of-the-art multivariate prediction model known as Neural Basis Expansion Analysis for Time Series (N-BEATS). This model uses existing in-vehicle signals and is trained using data from a simulation platform with a high-fidelity vehicle model. Additionally, the model provides an uncertainty interval of its estimation rather than a single value. Test results reveal that N-BEATS exhibits good tracking performance, outperforming Long Short-Term Memory (LSTM) in terms of estimation errors and inference speed. Finally, we highlight extra functionalities of the developed virtual sensor such as road quality classification and the consistency of the temporal-frequency content between predicted signals and the ground truth labels. Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Active safety systems

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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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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 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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A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

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Science China(Technological Sciences) 2023年第10期66卷 2882-2896页

作者： ZHANG Xin ZHOU Hao LIU JinGuo JU ZhaoJie LENG YuQuan YANG ChenGuang 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 School of Computing University of PortsmouthPortsmouth PO13HEUK Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518055China Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

关键词： compliant interaction control force tracking PID variable stiffness impact compensator unknown environments

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Machine Learning-based Optimal Trajectory Planning for Spacecraft Passively Flying Around a Satellite for Proximity Operations

Machine Learning-based Optimal Trajectory Planning for Space...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Barth, Andrew Roach, Dylan Ma, Ou Hernandez, Michael Ashbaugh, Ryan Fleming, Alex Department of Aerospace Engineering Intelligent Robotics and Autonomous Systems Laboratory University of Cincinnati CincinnatiOH45221 United States iMETALX Inc SausalitoCA94965 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In-space or On-orbit Servicing, Assembly, and Manufacturing (ISAM or OSAM) activities require spacecraft to fly around a client satellite or another orbiting object in a proximity range from 100’s of meters to 0 meters in distance. When performing such proximity operations, it becomes extremely important for the servicing craft to not only avoid collision but at the same time fly close enough for better situational awareness and minimal fuel consumption. This is especially important because such orbiting spacecraft will often be low-cost CubeSats performing OSAM tasks, therefore, planning a Passive Relative Orbit (PRO) becomes essential for low-risk and energy-efficient operations. Next, the problem of how to deploy a CubeSat from a station-keeping Chief spacecraft along a transfer trajectory, followed by a PRO-based two-impulse burn maneuver with minimal fuel consumption and variable time constraints, needs to be solved. Once the task is completed, another two-burn transfer trajectory will be calculated for the servicing deputy to return to the Chief. In this paper, we use a machine learning approach to determine the ΔV burns required to achieve the relative transfer and task trajectories. Machine learning allows us to improve our mission planning capabilities and gives the system more freedom to make choices where the operator does not have clear or full insight of the complex physics under many constraints. The system was trained using the proximal policy optimization algorithm for a range of task trajectory parameters randomly selected for each training episode. The results of the trained system were validated by comparison to those produced by traditional optimization methods from our previous work. The robustness of the trained algorithm was verified by 1,000 random scenarios falling within the range of trained conditions. The study demonstrates the ability to use the trained model for a variety of situations without the need to retrain specially for each type

关键词： Orbits

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

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IFAC-PapersOnLine 2023年第2期56卷 1003-1008页

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

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.

关键词： Manipulation Mechatronic systems robotics Joint angle estimation

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