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A New Workspace and Structural Parameter Optimization Method of Parallel Robot 18

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A New Workspace and Structural Parameter Optimization Method...

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Haiyun Hu, Weijun Zhang, Renge Wang, Jin Lu, Guodong School of Robotics Ningbo University of Technology Ningbo315211 China Robotics Institute of Zhejiang University Hangzhou310027 China Zhejiang Qiantang Robot and Intelligent Equipment Reserch Co. Ltd Hangzhou310018 China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China

ISBN: (纸本)9798350312201

The parallel robot workspace refers to the set of points and boundary that can be reached by the parallel mechanism. With the continuous development and industrial application of parallel robots, the workspace analysis and optimization becomes a critical link in this process. In this paper, a workspace solution approach based on seed space segmentation and principal component analysis is proposed. In order to improve the working performance of parallel robots, a flexible workspace evaluation method based on quadratic fourth moment is proposed, and a relationship model between flexible workspace and structural parameters is established. Furthermore, based on the multi-objective genetic algorithm, the structure parameters of the parallel robot are optimized by the workspace and flexible workspace indexes. Finally, the advantage and effectiveness of proposed method are verified by experiment and comparison with traditional methods. © 2023 IEEE.

关键词： Principal component analysis

Adaptive Integral Sliding Mode Control for Attitude Tracking of Underwater Robots With Large Range Pitch Variations in Confined Space

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arXiv 2024年

作者： Wang, Xiaorui Sha, Zeyu Zhang, Feitian The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China

Underwater robots play a crucial role in exploring aquatic environments. The ability to flexibly adjust their attitudes is essential for underwater robots to effectively accomplish tasks in confined space. However, the highly coupled six degrees of freedom dynamics resulting from attitude changes and the complex turbulence within limited spatial areas present significant challenges. To address the problem of attitude control of underwater robots, this letter investigates large-range pitch angle tracking during station holding as well as simultaneous roll and yaw angle control to enable versatile attitude adjustments. Based on dynamic modeling, this letter proposes an adaptive integral sliding mode controller (AISMC) that integrates an integral module into traditional sliding mode control (SMC) and adaptively adjusts the switching gain for improved tracking accuracy, reduced chattering, and enhanced robustness. The stability of the closed-loop control system is established through Lyapunov analysis. Extensive experiments and comparison studies are conducted using a commercial remotely operated vehicle (ROV), the results of which demonstrate that AISMC achieves satisfactory performance in attitude tracking control in confined space with unknown disturbances, significantly outperforming both PID and SMC. Copyright © 2024, The Authors. All rights reserved.

关键词： Sliding mode control

TDANet: Target-Directed Attention Network For Object-Goal Visual Navigation With Zero-Shot Ability

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arXiv 2024年

作者： Lian, Shiwei Zhang, Feitian The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China

The generalization of the end-to-end deep reinforcement learning (DRL) for object-goal visual navigation is a longstanding challenge since object classes and placements vary in new test environments. Learning domain-independent visual representation is critical for enabling the trained DRL agent with the ability to generalize to unseen scenes and objects. In this letter, a target-directed attention network (TDANet) is proposed to learn the end-to-end object-goal visual navigation policy with zero-shot ability. TDANet features a novel target attention (TA) module that learns both the spatial and semantic relationships among objects to help TDANet focus on the most relevant observed objects to the target. With the Siamese architecture (SA) design, TDANet distinguishes the difference between the current and target states and generates the domain-independent visual representation. To evaluate the navigation performance of TDANet, extensive experiments are conducted in the AI2-THOR embodied AI environment. The simulation results demonstrate a strong generalization ability of TDANet to unseen scenes and target objects, with higher navigation success rate (SR) and success weighted by length (SPL) than other state-of-the-art models. TDANet is finally deployed on a wheeled robot in real scenes, demonstrating satisfactory generalization of TDANet to the real world. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

A Multi-Modal Approach Based on Large Vision Model for Close-Range Underwater Target Localization

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arXiv 2024年

作者： Yang, Mingyang Sha, Zeyu Zhang, Feitian The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China

Underwater target localization uses real-time sensory measurements to estimate the position of underwater objects of interest, providing critical feedback information for underwater robots in tasks such as obstacle avoidance, scientific exploration, and environmental monitoring. While acoustic sensing is the most acknowledged and commonly used method in underwater robots and possibly the only effective approach for long-range underwater target localization, such a sensing modality generally suffers from low resolution, high cost and high energy consumption, thus leading to a mediocre performance when applied to close-range underwater target localization. On the other hand, optical sensing has attracted increasing attention in the underwater robotics community for its advantages of high resolution and low cost, holding a great potential particularly in close-range underwater target localization. However, most existing studies in underwater optical sensing are restricted to specific types of targets, thus lacking generalization capabilities. In addition, these studies typically focus on the design of estimation algorithms and ignore the influence of illumination conditions on the sensing performance, thus hindering wider applications in the real world. To address the aforementioned issues, this paper proposes a novel target localization method that assimilates both optical and acoustic sensory measurements to estimate the 3D positions of close-range underwater targets. The proposed sensing method integrates a large vision model with unique acoustic-based model prompt design to process multi-modal sensor measurements, ensuring the generalizability and robustness of underwater target localization. A test platform with controllable illumination conditions is developed. Extensive experiments are conducted, the results of which validate the effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Strain measurement

Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method

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arXiv 2025年

作者： Zhang, Zhong-Yi Xu, Chen-Yang Zhao, Li-Xuan Hou, Hui-Rang Meng, Qing-Hao Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China

Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring, widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depression-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced two novel modules: the sample confidence module and the minority sample penalty module. The former leverages the L2-norm of prediction errors to progressively filter EEG samples with weak label alignment during training, thereby reducing the impact of subjectivity;the latter automatically upweights misclassified minority-class samples to address imbalance issues. After testing on two public EEG datasets, DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class severity recognition, outperforming baseline models. Ablation studies confirmed both modules’ contributions. We further discussed the strengths and limitations of regression-based models for depression-level recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

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Journal of Bionic engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

Methodology of Designing and Modeling Projects for robotics and Mechatronics Course of Intelligent robotics Master Program

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Methodology of Designing and Modeling Projects for Robotics ...

Educational Innovation (ECEI), IEEE Eurasian Conference on

作者： Shifa Sulaiman Alexander Eryomin Tatyana Tsoy Evgeni Magid Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia School of Electronic Engineering HSE University Moscow Russia

Intelligent robots are employed in a variety of sectors due to their capability to carry out tasks autonomously. As a response to the growing importance of intelligent robots, a new master’s program in intelligent robotics was introduced by Kazan Federal University, Russia in 2017. It was created by analyzing the curricula of leading robotics universities worldwide. The main objective of this master’s program is to provide in-depth knowledge to students regarding intelligent robots’ development and applications. Classes of the program were conducted by combining theoretical lectures with applications of robotics software and hardware tools. Computer-aided design (CAD) software and Robotic Operating System (ROS) libraries were practiced by students in courses. We present an approach for designing and modeling projects within the robotics and Mechatronics course for first-year students using SOLIDWORKS and ROS. Educational methodologies adopted by a student are presented in this article as a case study of an elevator-operating robot.

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State-of-Charge (SOC) Estimation of Lithium-Ion Battery Based on Unscented Kalman Filtering 6

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State-of-Charge (SOC) Estimation of Lithium-Ion Battery Base...

6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Miao, Mengfan Li, Ligang Wang, Zhongfeng Jiang, Jing Shenyang Ligong University College of Automation and Electrical Engineering Liaoning China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (纸本)9798350316520

Real-time estimation of State of Charge (SOC) for lithium-ion batteries is one of the core technologies in Battery Management systems (BMS). Accurate SOC estimation is fundamental for managing lithium-ion batteries. In this study, a second-order RC equivalent circuit model is established, and parameter identification methods are employed to determine the model parameters. The identified parameters are then incorporated back into the second-order RC model. The accuracy of these parameters and the model is validated through Hybrid Pulse Power Characteristic (HPPC) tests. Finally, the Unscented Kalman Filtering algorithm (UKF) is utilized to estimate battery SOC. Experimental results demonstrate that SOC estimation based on UKF exhibits high precision and stability. © 2024 IEEE.

关键词： Lithium-ion batteries

Learn to Coordinate: A Whole-Body Learning from Demonstration Framework for Differential Drive Mobile Manipulators

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Learn to Coordinate: A Whole-Body Learning from Demonstratio...

2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Yang, Yuqiang Huang, Darong Chen, Chen Zeng, Chao He, Yanong Yang, Chenguang South China University of Technology Automation Science and Engineering Guangzhou China Application Innovate Laboratory Huawei Technologies Co. Ltd Shenzhen China Universitat Hamburg Technical Aspects of Multimodal Systems Hamburg Germany Bristol Robotics Laboratory University of the West of England Bristol United Kingdom

ISBN: (纸本)9798350337020

This paper proposes a whole-body learning from demonstration (LfD) framework that enables differential drive mobile manipulators to learn coordination working and disturbance rejection. First, an efficient kinesthetic teaching method is devised based on the weighted least-norm (WLN) inverse kinematics solution and an admittance controller, which facilitates human users to guide the mobile manipulator to perform tasks. Second, we propose a whole-body LfD framework through Gaussian Process, which endows the mobile manipulator's skill learning process with features of large-scale convergence, coordination working and disturbance rejection, after just a few human demonstrations. The proposed learning framework also allows for human-in-the-loop correction when the whole-body is conducting a task. Finally, the effectiveness of the proposed framework is verified via two simulations and a pick-and-place experiment. Supplementary video for this paper is available in github https://***/yuqiang-yang/SMC2023-Video. © 2023 IEEE.

关键词： Gaussian Process Learning from Demonstration Wholebody Control