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11th International Conference on Mechatronics and robotics Engineering, ICMRE 2025

作者： Pang, Linjun Li, Haiming Gao, Peiyuan Li, Haotian Zhang, He Zhao, Jie Harbin Institute of Technology State Key Lab of Robotics and Systems Harbin China

ISBN: (纸本)9798331509293

Microsurgical robot calibration is essential for achieving high precision in surgical operations. This paper proposes a high-precision calibration approach combining Iterative Least Squares (ILS) for geometric error correction and Particle Swarm Optimization-optimized Least Squares Support Vector Machine (PSO-LSSVM) for non-geometric error compensation. Experimental results show that the proposed method reduces positioning errors from 3.2541 mm to 0.2310 mm through geometric calibration, achieving a 92.90 % accuracy improvement, and further reduces the error to 0.0749 mm after non-geometric error compensation. These results underscore the effectiveness of the proposed approach in enhancing the precision of robotic systems used in microsurgery, © 2025 IEEE.

关键词： Error compensation

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A High-precision Calibration Method for Microsurgical Robots: Combining Iterative Least Squares and PSO-Optimized Support Vector Machine

A High-precision Calibration Method for Microsurgical Robots...

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Mechatronics and robotics Engineering (ICMRE), International Conference on

作者： Linjun Pang Haiming Li Peiyuan Gao Haotian Li He Zhang Jie Zhao State Key Lab of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

关键词： Support vector machines Accuracy Medical robotics Error compensation Kinematics Microsurgery Calibration Iterative methods Reliability Particle swarm optimization

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Anti-Degeneracy Scheme for Lidar SLAM based on Particle Filter in Geometry Feature-Less Environments

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IEEE robotics and Automation Letters 2025年第7期10卷 6784-6791页

作者： Li, Yanbin Zhang, Wei Zhang, Zhiguo Shi, Xiaogang Li, Ziruo Zhang, Mingming Xie, Hongping Chi, Wenzheng Beijing University of Posts and Telecommunications School of Electronics Engineering Beijing100876 China China Agricultural University Key Lab of Smart Agriculture Systems Beijing100083 China Beihang University School of Integrated Circuit Science and Engineering Beijing100191 China State Grid Jiangsu Electric Power Co.Ltd Construction Branch Jiangsu226000 China Soochow University Robotics and Microsystems Center School of Mechanical and Electric Engineering Suzhou215021 China

Simultaneous localization and mapping (SLAM) based on particle filtering has been extensively employed in indoor scenarios due to its high efficiency. However, in geometry feature-less scenes, the accuracy is severely reduced due to lack of constraints. In this article, we propose an anti-degeneracy system based on deep learning. Firstly, we design a scale-invariant linear mapping to convert coordinates in continuous space into discrete indexes, in which a data augmentation method based on Gaussian model is proposed to ensure the model performance by effectively mitigating the impact of changes in the number of particles on the feature distribution. Secondly, we develop a degeneracy detection model (DD-Model) using residual neural networks (ResNet) and transformer which is able to identify degeneracy by scrutinizing the distribution of the particle population. Thirdly, an adaptive anti-degeneracy strategy is designed, which first performs fusion and perturbation on the resample process to provide rich and accurate initial values for the pose optimization, and use a hierarchical pose optimization combining coarse and fine matching, which is able to adaptively adjust the optimization frequency and the sensor trustworthiness according to the degree of degeneracy, in order to enhance the ability of searching the global optimal pose. Finally, we demonstrate the optimality of model, as well as the improvement of image matrix method and GPU on the inference time through ablation experiments. The effectiveness of our method is verified by comparing with SOTA methods. © 2016 IEEE.

关键词： Deep learning

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel state Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Anti-Degeneracy Scheme for Lidar SLAM based on Particle Filter in Geometry Feature-Less Environments

arXiv

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arXiv 2025年

作者： Li, Yanbin Zhang, Wei Zhang, Zhiguo Shi, Xiaogang Li, Ziruo Zhang, Mingming Xie, Hongping Chi, Wenzheng School of Electronics Engineering Beijing University of Posts and Telecommunications Beijing100876 China Key Lab of Smart Agriculture Systems Ministry of Education China Agricultural University Beijing100083 China School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Co.Ltd Construction Branch State Grid Jiangsu Electric Power Jiangsu226000 China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China

Simultaneous localization and mapping (SLAM) based on particle filtering has been extensively employed in indoor scenarios due to its high efficiency. However, in geometry feature-less scenes, the accuracy is severely reduced due to lack of constraints. In this article, we propose an anti-degeneracy system based on deep learning. Firstly, we design a scale-invariant linear mapping to convert coordinates in continuous space into discrete indexes, in which a data augmentation method based on Gaussian model is proposed to ensure the model performance by effectively mitigating the impact of changes in the number of particles on the feature distribution. Secondly, we develop a degeneracy detection model using residual neural networks (ResNet) and transformer which is able to identify degeneracy by scrutinizing the distribution of the particle population. Thirdly, an adaptive anti-degeneracy strategy is designed, which first performs fusion and perturbation on the resample process to provide rich and accurate initial values for the pose optimization, and use a hierarchical pose optimization combining coarse and fine matching, which is able to adaptively adjust the optimization frequency and the sensor trustworthiness according to the degree of degeneracy, in order to enhance the ability of searching the global optimal pose. Finally, we demonstrate the optimality of the model, as well as the improvement of the image matrix method and GPU on the computation time through ablation experiments, and verify the performance of the anti-degeneracy system in different scenarios through simulation experiments and real experiments. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping

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JADFER: Exploring Spatial-Contextual Interaction with Joint Attention Dropping for Facial Expression Recognition

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IEEE Transactions on Affective Computing 2024年第02期16卷 655-668页

作者： Gao, Yu Ren, Weihong Jiang, Weibo Dong, Qian Nie, Wei Wu, Wenhao Liu, Honghai Harbin Institute of Technology State Key Lab of Robotics and Systems School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China

Facial Expression Recognition (FER) aims to categorize emotional expressions depicted on a human face, and is a challenging task under unconstrained conditions, such as face occlusions and pose variations. Recent methods usually adopt self attention or cross attention to explore global or local relationships among different level features. However, these methods are inclined to focus on the redundant facial regions, causing model overfitting. To address this problem, we propose a new FER model named JADFER, which drops the joint attention in the weight matrix to adaptively enhance facial expression representations. Specifically, our JADFER model consists of three components: Spatial Branch (SB), Contextual Branch (CB), and Spatial-Contextual Interaction (SCI). Firstly, SB runs N paths in parallel, where a Variety loss is designed to guide the paths of SB to focus on different discriminative regions. Meanwhile, CB abstracts the contextual facial representations using self attention with Joint Attention Dropping (JAD). Then, the SCI adopts the spatial features from SB to query the contextual representations from CB through cross attention with JAD, which regulates the attention weights by dropping the similar activations to further enhance the facial embeddings. Experimental results demonstrate that the proposed model outperforms the state-of-the-art methods on several FER benchmarks. © 2010-2012 IEEE.

关键词： Face recognition

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Relay Selection and Power Control for Mobile Underwater Acoustic Communication Networks: A Dual-Thread Reinforcement Learning Approach

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Key Lab of Industrial Computer Control Engineering of Hebei Province China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforcement-learning-based cooperation communication scheme to efficiently resist the highly dynamic communication links and strongly unknown time-varying channel states caused by the mobility of Autonomous Underwater Vehicles (AUVs). Firstly, a particular Markov decision process is developed to model the dynamic relay selection process of mobile AUV in the unknown scenario. In the developed model, an experimental statistical-based partition mechanism is proposed to cope with the greatly increasing dimension of the state space caused by the mobility of AUV, reducing the search optimization difficulty. Secondly, a dual-thread reinforcement learning structure with actual and virtual learning threads is proposed to efficiently track the superior relay action. In the actual learning thread, the proposed improved probability greedy policy enables the AUV to strengthen the exploration for the reward information of potential superior relays on the current state. Meanwhile, in the virtual learning thread, the proposed upper-confidence-bound-index-based uncertainty estimation method can estimate the action-reward level of historical states. Consequently, the combination of actual and virtual learning threads can efficiently obtain satisfactory Q value information, thereby making superior relay decision-making in a short time. Thirdly, a power control mechanism is proposed to reuse the current observed action-reward information and transform the multiple unknown parameter nonlinear joint power optimization problem into a convex optimization problem, thereby enhancing network transmission capacity. Finally, simulation results verify the effectiveness of the proposed scheme. IEEE

关键词： Convex optimization

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Extended High-gain Observer based Adaptive Control of Flexible-joint Surgical Robot

Extended High-gain Observer based Adaptive Control of Flexib...

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IEEE International Conference on robotics and Biomimetics (IEEE ROBIO)

作者： Zou, Shuizhong Pan, Bo Fu, Yili Guo, Shuxiang State Key Lab. of Robotics and Systems Harbin Inst. of Tech China Harbin Inst. of Tech China

ISBN: (纸本)9781509043644

Due to the surgical robot flexible joint state vectors are not all measurable, in order to reduce the effect of the modeling errors and nonlinear factors of the flexible-joint manipulator caused by gravity and friction, an extended high-gain observer (EHGO) based adaptive control algorithm is designed to perform the position tracking control of the flexible-joint manipulator. First, gravity and friction are fed forward to reduce the nonlinearity of the robotic manipulator. Secondly, an extended high gain observer is used to observe the angular velocity of the joint and correct compensation errors of gravity and friction in real time. Finally, EHGO based adaptive controller is used to eliminate the inertia parameter measurement errors of the dynamic model, and to achieve high precision position tracking control. Simulation experiments show that the proposed control method can improve the position tracking accuracy of the flexible-joint manipulator and restrain the flexible-joint vibration obviously, and thus verifies that the designed controller can meet the requirements of accurate tracking control of the surgical robot.

关键词： Surgical Robot Adaptive control Extended High Gain Observer Flexible-joint manipulator Manipulator

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Recent Developments in Metallic Degradable Micromotors for Biomedical and Environmental Remediation Applications

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Nano-Micro Letters 2024年第3期16卷 1-35页

作者： Sourav Dutta Seungmin Noh Roger Sanchis Gual Xiangzhong Chen Salvador Pané Bradley J.Nelson Hongsoo Choi Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu 42988Republic of Korea DGIST‑ETH Microrobotics Research Center DGISTDaegu 42988Republic of Korea Multi‑Scale Robotics Lab Institute of Robotics and Intelligent SystemsETH Zurich8092 ZurichSwitzerland Institute of Optoelectronics State Key Laboratory of Photovoltaic Science and TechnologyShanghai Frontiers Science Research Base of Intelligent Optoelectronics and PerceptionFudan UniversityShanghai 200433People’s Republic of China

Synthetic micromotor has gained substantial attention in biomedicine and environmental ***-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen *** novel feature has projected degradable metallic micromotors for active in vivo drug delivery *** addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and *** propulsion efficiency and life span govern the overall performance of a degradable metallic *** we review the design and recent advancements of metallic degradable ***,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical ***,we discuss micromotors’efficacy in detecting and destroying environmental ***,we address the limitations and future research directions of degradable metallic micromotors.

关键词： Magnesium Zinc Iron Biodegradable microrobot Biomedical Environmental

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OROCOS-based generic control system for a 6 DOF industrial manipulator

OROCOS-based generic control system for a 6 DOF industrial m...

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2016 IEEE Workshop on Advanced robotics and its Social Impacts, IEEE ARSO 2016

作者： Cheruiyot, Gilbert K. Zhu, Xiaoxiao Cao, Qixin Research Institute of Robotics School of Mechanical and Power Engineering Shanghai Jiaotong University China State Key Lab of Mechanical Systems and Vibration Research Institute of Robotics Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781509040773

This paper presents the design and implementation of a PC-based control application retrofitted to an existing industrial manipulator. The system uses the OROCOS component-based framework for real-time functionality as well as its infrastructure for software component interaction. This together with the use some of the provided libraries and built-in development tools allowed for a rapid development approach. Direct addressing of a PCI-based I/O card peripheral hardware is described and proposed as a solution to the perennial challenge of hardware I/O in Linux partly occasioned by a lack of support for device drivers. The result is a fully functional control application that provides standard manipulator functionality as well as on-board analysis tools and capacity for sensor-based extended functionality. Moreover the resulting scheme is based on generic hardware that is available commercially off-the-shelf and can thus be replicated in its entirety. © 2016 IEEE.

关键词： Industrial manipulators

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