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IEEE International Conference on robotics and Biomimetics

作者： Boyu Ma Baoshi Cao Yang Liu Yuteng Xie Xiaohang Yang Wei Wang Handong Hu Xiaokai Zhou Yiming Ji Zhengpu Wang Mengfei Li Haoran Sun Wandong Sun Zongwu Xie State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China School of Resources and Environment Northeast Agricultural University Harbin China

This article proposes a novel trajectory planning framework for the Experimental Module Manipulator (EMM) on-orbit servicing (OOS). For a start, the simulation system of the EMM based on Open Inventor is developed, and the trajectory planning technical route of the EMM for OOS missions is illustrated, including two main phases: large-scale transfer and small-scale approach. Before trajectory planning, the Gilbert–Johnson–Keerthi convex bounding boxes of the CSS and the EMM are modeled. Moreover, we present a Cartesian-space collision-free planning scheme. For the different task requirements in the above two phases, two corresponding collision-free trajectory planning schemes in joint space are proposed. Finally, simulations of the EMM supporting an astronaut extra-vehicular activity verify the effectiveness of our trajectory planning framework and all of the schemes presented in this article.

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Tsun: A Traversability-Based Safety-Ensured Uneven Terrain Navigation system

SSRN

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SSRN 2024年

作者： Wang, Han Wang, Gongcheng Zhu, Hongbiao Du, Chengjin Bai, Hua Ding, Pengchao Xu, Wenda Du, Zhijiang Wang, Weidong The State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China Istituto Italiano di Tecnologia The BioRobotics Institute Scuola Superiore Sant’Anna Genova16163 Italy

The application of autonomous navigation in mobile robots has seen notable success in indoor, structured 2D environments. However, applying this technology to outdoor, 3D unstructured environments — especially on uneven terrains, presents significant challenges. To address this, we propose a navigation framework for uneven terrain based on continuous traversability analysis. Our methodology unfolds in three stages: First, Bayesian generalized kernel inference is employed to estimate attributes of the unobserved point cloud, enabling the computation of continuous terrain traversability values by using dispersed point cloud data. Second, an improved A* (TRA-A*) algorithm is proposed for global path determination. In the final stage, nonlinear model predictive control (NMPC) is utilized for path following. This involves integrating the traversable exponents and uncertainty into the cost function, resulting in a safety-focused motion planning algorithm that encourages the navigation stability of the robot. The effectiveness of this method was verified through simulation and actual scene experiments. This algorithm improves the robot's navigation success rate by 8% while sacrificing a small amount of 9.83% of the path length in the actual environment, preventing the robot from tipping over or getting stuck in rough terrain, and improving the stationarity of the robot during driving. It reduces the cost of the terrain the robot travels on by 42.6%, reduces the robot's longitudinal acceleration fluctuation and lateral angular velocity fluctuation by 28.52% and 28.9% respectively, and improves the robot's driving stability. © 2024, The Authors. All rights reserved.

关键词： Cost functions

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Robotic Visual Inspection and Positioning Method for Flexible Wire Coating Operation 12

Robotic Visual Inspection and Positioning Method for Flexibl...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Rao, Chenglong Song, Yifeng Wang, Hongguang Wang, Junpeng Jing, Fengren Shenyang University of Chemical Technology Shenyang110142 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Overhead bare wire is not protected by insulating sheaths, and is prone to safety accidents. It is of great practical significance to use coating robots to insulate bare wires. During the robotic coating operation, the flexible wire is much deformed due to the robot gravity, this paper proposes a robot vision inspection and positioning method for flexible wire coating operation. The offset distance of the calibration plate is defined in the image space to characterize the change in the robot's working posture, and the target position tracking control law based on the PID method is designed. Since the target in the flexible wire environment will change with the flexible wire, the technical means of hand-eye calibration and camera calibration are used to locate and track the dynamically changing target in real-time. The experimental results show that the method can effectively inspect and locate the target, and adapt to the semi-structural environment. © 2022 IEEE.

关键词： Inspection

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DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching

arXiv

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arXiv 2023年

作者： Xie, Tao Dai, Kun Wang, Ke Li, Ruifeng Zhao, Lijun State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China SenseTime Group Inc. Beijing100080 China

Local feature matching between images remains a challenging task, especially in the presence of significant appearance variations, e.g., extreme viewpoint changes. In this work, we propose DeepMatcher, a deep Transformer-based network built upon our investigation of local feature matching in detector-free methods. The key insight is that local feature matcher with deep layers can capture more human-intuitive and simpler-to-match features. Based on this, we propose a Slimming Transformer (SlimFormer) dedicated for DeepMatcher, which leverages vector-based attention to model relevance among all keypoints and achieves long-range context aggregation in an efficient and effective manner. A relative position encoding is applied to each SlimFormer so as to explicitly disclose relative distance information, further improving the representation of keypoints. A layer-scale strategy is also employed in each SlimFormer to enable the network to assimilate message exchange from the residual block adaptively, thus allowing it to simulate the human behaviour that humans can acquire different matching cues each time they scan an image pair. To facilitate a better adaption of the SlimFormer, we introduce a Feature Transition Module (FTM) to ensure a smooth transition in feature scopes with different receptive fields. By interleaving the self- and cross-SlimFormer multiple times, DeepMatcher can easily establish pixel-wise dense matches at coarse level. Finally, we perceive the match refinement as a combination of classification and regression problems and design Fine Matches Module to predict confidence and offset concurrently, thereby generating robust and accurate matches. Experimentally, we show that DeepMatcher significantly outperforms the state-of-the-art methods on several benchmarks, demonstrating the superior matching capability of DeepMatcher. The code is available at https://***/XT1997/DeepMatcher. © 2023, CC BY.

关键词： Behavioral research

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Diverse Gaussian Sampling for Human Motion Prediction 16th

Diverse Gaussian Sampling for Human Motion Prediction

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16th International Conference on Social robotics, ICSR + InnoBiz 2024

作者： Luo, Jiefu Wang, Jiansheng Liu, Zhenfei Cheng, Jun Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong Department of Pediatric Orthopedics Shenzhen Children’s Hospital Guangdong Shenzhen518000 China

ISBN: (纸本)9789819611508

In many industrial applications, it is necessary to obtain accurate human motion prediction. The prediction of human movement based on a 3D skeleton, which forecasts future postures using the human skeletal structure, significantly aids in enabling machines to understand human behavior and react more intelligently. Additionally, by predicting human actions, potential dangers can be perceived, especially in safety-related issues, necessitating a broader range of predictions. In this paper, considering the greater diversity generated by Variational Autoencoders (VAEs), we employ VAEs for prediction. In contrast to other studies, our primary focus is on obtaining more diverse samples. Extensive experiments demonstrate that our proposed model performs well on the Human 3.6M [35] and HumanEva-I [36] datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Model heat source using actual distribution of laser power density for simulation of laser processing

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Journal of Central South University 2022年第10期29卷 3277-3293页

作者： WANG Gen-wang DING Ye GUAN Yan-chao WANG Yang YANG Li-jun State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China

The model of heat source(MHS) which reflects the thermal interaction between materials and laser during processing determines the accuracy of simulation results. To acquire desirable simulations results, although various modifications of heat sources in the aspect of absorption process of laser by materials have been purposed, the distribution of laser power density(DLPD) in MHS is still modeled theoretically. However, in the actual situations of laser processing, the DLPD is definitely different from the ideal models. So, it is indispensable to build MHS using actual DLPD to improve the accuracy of simulation results. Besides, an automatic modeling method will be benefit to simplify the tedious pre-processing of simulations. This paper presents a modeling method and corresponding algorithm to model heat source using measured DLPD. This algorithm automatically processes original data to get modeling parameters and provides a step MHS combining with absorption models. Simulations and experiments of heat transfer in steel plates irradiated by laser prove the mothed and the step MHS. Moreover, the investigations of laser induced thermal-crack propagation in glass highlight the signification of modeling heat source based on actual DLPD and demonstrate the enormous application of this method in the simulation of laser processing.

关键词： heat source laser processing distribution of power density digital images processing heat transfer

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Riemannian Geometry Based Frequency Band Optimization in Motor Imagery Classification

Riemannian Geometry Based Frequency Band Optimization in Mot...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Fanbo Zhuo Fengzhen Tang The State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China The University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

This paper presents an approach for optimizing frequency bands in motor imagery (MI) classification using Particle Swarm Optimization (PSO) combined with the Generalized learning Riemannian space quantization (GLRSQ) classifier. The proposed method leverages the class distance metric to assess the distribution of EEG signals in the Symmetric Positive Definite (SPD) manifold. Experimental results on the BCI IIa dataset demonstrate improved classification accuracy and Kappa values compared to baseline method. The study highlights the effectiveness of opti-mizing frequency bands on the Riemannian manifold for enhancing MI classification performance.

关键词： Manifolds Measurement Training Time-frequency analysis Automatic frequency control Accuracy Quantization (signal) Optimization methods Motors Particle swarm optimization

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Biomimetic multi-segment worm robot based on shape memory alloy wire

Biomimetic multi-segment worm robot based on shape memory al...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Huang, Wenqiang Shang, Wanfeng Li, Yulu Liu, Xiaofei Wu, Xinyu Southern University of Science and Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China

ISBN: (纸本)9798350327182

Shape memory alloy (SMA) has been used in robotic actuators due to its advantages, such as shape memory effect, high damping capacity, and temperature sensitivity. However, its practical application has been limited by the low frequency of heating-cooling cycles and small deformation caused by heat dissipation. To address this issue, this paper presents an insect-scale multi-segment worm robot actuated by a biomimetic soft compound muscle composed of Shape Memory Alloy Wire (SMAW), Polydimethylsiloxane (PDMS), and Acrylonitrile Butadiene Styrene (ABS). The robot's design includes multiple SMAWs in parallel and an ABS fiber rib with a scaffold structure embedded into the PDMS matrix, allowing for the conversion of the linear small deformations of the SMAW into large bending-expanding motions of the biomimetic muscle. The robot consists of two independently and alternately excited segments that form worm gaits. The soft muscle with multiple thin SMAWs and the multi-segment design enable better heat dissipation, which improves the actuation frequency and the robotic locomotion ability. Experimental results show that the bimimetic robot actuated by SMAWs can reach a speed of 24.33mm/s for a 5Hz PWM signal and 7.33mm/s with a load of three times its weight, outperforming robots of the same scale size and SMA actuation. These results demonstrate that the multi-segment structure with multiple thin SMAWs of the biomimetic robot provides a feasible solution for applying SMA to robotic higher frequency actuation. © 2023 IEEE.

关键词： Biomimetics

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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1716-1729页

作者： Weiliang Zuo Jingmin Xin Changnong Liu Nanning Zheng Akira Sano IEEE the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence the National Engineering Research Center for Visual Information and Applicationsand the Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’an 710049China the Data Center China Construction BankBeijing 100033China the Department of System Design Engineering Keio UniversityYokohama 223-8522Japan

Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still *** paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost *** establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is *** the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.

关键词： Capon beamformer direction-of-arrival(DOA)estimation large-sample mean-squared-error(MSE) subspace-based methods uniform linear array

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