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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Gao, Jianqi Li, Yanjie Ye, Zhaohui Wu, Xinyu Department of Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics and School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Multi-agent path finding (MAPF) is an essential issue for warehouse automation, where multiple agents plan collision-free paths from the start to goal positions. Reinforcement learning (RL) has been employed to develop partially observable distributed MAPF methods that can be scaled to any number of agents. However, existing RL-based MAPF methods still have some limitations in handling redundant information and avoiding deadlock, resulting in a low success rate or longer makespan. This paper proposes a Priority-aware Communication & Experience learning method (PCE), which combines RL with a novel priority-aware multi-agent communication and a new priority-aware deadlock experience replay to tackle this challenge. To be specific, our innovation encompasses two-fold. Our proposed communication mechanism aims to handle redundant information, which establishes a dynamic communication topology based on agents' priorities and proposes a two-head priority-aware graph attention network to aggregate information. In order to help the agent avoid deadlock, we prioritize the expert experience that solves the deadlock when performing experience replay. We conduct multiple simulation experiments on warehouse-like structured grid maps. Compared with the state-of-the-art RL-based MAPF methods, PCE performs significantly better with a higher success rate and lower makespan in small and large MAPF and higher average throughput in the lifelong MAPF, which can further improve the efficiency of warehouse automation. Finally, we validate PCE using three Turtlebot3-Burger robots, which shows that PCE can be applied in real warehouse automation scenarios. IEEE

关键词： Aggregates

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Learning Agile Quadrotor Flight in Restricted Environments with Safety Guarantees

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-8页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Wu, Xinyu Department of Control Science and Engineering Harbin Institute of Technology Shenzhen and Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the increasing requirement for agile and efficient controllers in safety-critical scenarios, controllers that exhibit both agility and safety are attracting attention, especially in the aerial robotics domain. This paper focuses on the safety issue of Reinforcement Learning (RL)-based control for agile quadrotor flight in restricted environments. To this end, we propose a unified Adaptive Safety Predictive Corrector (ASPC) to certify each output action of the RL-based controller in real-time, ensuring its safety while maintaining agility. Specifically, we develop the ASPC as a finite-horizon optimal control problem, formulated by a variant of Model Predictive Control (MPC). Given the safety constraints determined by the restricted environment, the objective of minimizing loss of agility can be optimized by reducing the difference between the actions of RL and ASPC. As the safety constraints are decoupled from the RL-based control policy, the ASPC is plug-and-play and can be incorporated into any potentially unsafe controllers. Furthermore, an online adaptive regulator is presented to adjust the safety bounds of the state constraints with respect to the environment changes, extending the proposed ASPC to different restricted environments. Finally, simulations and real-world experiments are demonstrated in various restricted environments to validate the effectiveness of the proposed ASPC. IEEE

关键词： Reinforcement learning

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Real-Time Break-Through Detection in Craniotomy Using Long Short-Term Memory Networks

Real-Time Break-Through Detection in Craniotomy Using Long S...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Chen, Yunle Shen, Xingjian Meng, Xianghe Xie, Hui Harbin Institute of Technology State key Laboratory of Robotics and Systems 2 Yikuang Nangang District Harbin150080 China

ISBN: (纸本)9781665481090

An important step before the implantation of an invasive brain-computer interface (BCI) is craniotomy, which provides direct access to the brain. During the craniotomy, the precision of cranial drilling is critical, as any deviation or exces-sive penetration can pose significant risks, potentially leading to severe brain injury. Traditional methods of force signal analysis lack effective feedback in complex situations, leading to a risk of over-drilling and potential brain damage. Here, we proposes a Long Short-Term Memory (LSTM) neural network to detect the break-through of the drill bit in real-time. Thrust force data from five drilling experiments on a fancy rat skull specimen are collected. The force data are then split into 0.2-second intervals corresponding to different stages of the drilling process. An LSTM network is trained with these labeled datasets to detect changes in the drilling stage, thereby determining the break-through point. The trained classicfication network gets a accuracy of 96%. Practical drilling experiments on a fancy rat skull specimen validates the feasibility and accuracy of the break-through detection algorithm. The experimental results demonstrate that the method can achieve an over-penetration distance within 15 μm, which is safe for performing craniotomy in rats. Given that this method ensures safety for the stringent requirements of rat craniotomy, it can be expected to provide even greater safety for human craniotomy procedures. © 2024 IEEE.

关键词： Risk assessment

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S2H-GNN: Learning Soft to Hard Feature Matching with Sparsified Graph Neural Network

S2H-GNN: Learning Soft to Hard Feature Matching with Sparsif...

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

作者： Xie, Tao Li, Ruifeng Jiang, Zhiqiang Dai, Kun Wang, Ke Zhao, Lijun Li, Shuozhan Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin150006 China -HIT Research Institute of Engineering and Technology Quanzhou China

ISBN: (纸本)9798350327182

In this work, we introduce a soft-to-hard network (S2H-GNN) for precise local feature matching between image pairs. The core of S2H-GNN is a weighted context aggregation algorithm that enables nodes to receive information from their neighbors in order to form a more global understanding. S2HGNN leverages a sparsified graph neural network to enhance the features, consisting of the following modules alternately: (1) Inducing Point Initial Module that uses cosine correlation to identify highly scored keypoints as Inducing points. (2) Message Passing Module that integrates the features of original keypoints into those of Inducing points, which are further leveraged to exchange messages through self attention and cross attention. (3) Attention Upsample Module that propagates the feature of Inducing points to that of original keypoints, obtaining discriminative feature representation. Additionally, we propose an assignment layer to obtain a soft assignment matrix, which views feature matching as a graphy matching problem. Finally, we introduce a novel gap loss to guide S2H-GNN in learning a discriminative cognition of matching and non-matching keypoints, ultimately obtaining the hard assignment matrix. Experimental results demonstrate that S2H-GNN achieves comparable or higher performance in image matching and homography estimation tasks. © 2023 IEEE.

关键词： Graph neural networks

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On-Orbit Thermal Analysis of Ring Truss-Type Deployable Antenna 23rd

On-Orbit Thermal Analysis of Ring Truss-Type Deployable Ante...

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23rd IFToMM China International Conference on Mechanism and Machine Science and Engineering, IFToMM CCMMS 2022

作者： Zhang, Xiong Kang, Xi Huang, Hailin Li, Bing State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China

ISBN: (纸本)9789811993978

As space antenna tends to be large-scale, deployable antenna is more and more widely used. As a key part of antenna structure, the antenna profile will be deformed due to uneven heating during on-orbit operation, which affects the service performance of the antenna. In this paper, the thermal effect mode of space environment on the on-orbit antenna is analyzed firstly, and the differential heat conduction equation is obtained by taking the profile as research object. Then, thermal simulation of the ring truss-type deployable antenna in the sun-synchronous orbit is carried out by using the NX-TMG software. The temperature characteristics of the antenna profile and the influence law of material parameters on the profile temperature during on-orbit operation are analyzed. It is concluded that temperature of antenna profile changes in one orbital period. The selection of low absorption rate and high density materials can reduce the profile temperature difference, which provides the reference for ring truss-type deployable antenna design. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Mechanical design and analysis of a novel variable stiffness actuator with symmetrical pivot adjustment

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Frontiers of Mechanical Engineering 2021年第4期16卷 711-725页

作者： Yiwei LIU Shipeng CUI Yongjun SUN State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

The safety of human-robot interaction is an essential requirement for designing collaborative ***,this paper aims to design a novel variable stiffiiess actuator(VSA)that can provide safer physical human-robot interaction for collaborative *** A follows the idea of modular design,mainly including a variable stiffiiess module and a drive *** variable stiffiiess module transmits the motion from the drive module in a roundabout maimer,making the modularization of VS A *** the key component of the variable stiffiiess module,a stiffness adjustment mechanism with a symmetrical structure is applied to change the positions of a pair of pivots in two levers linearly and simultaneously,which can eliminate the additional bending moment caused by the asymmetric *** design of the doubledeck grooves in the lever allows the pivot to move freely in the groove,avoiding the geometric constraint between the ***,the VSA stiffiiess can change from zero to infinity as the pivot moves from one end of the groove to the *** facilitate building a manipulator in the future,an expandable electrical system with a distributed structure is also *** calibration and control experiments are performed to evaluate the physical performance of the designed *** results show that the VSA stiffiiess is close to the theoretical design ***,the VSA with a proportional-derivative feedback plus feedforward controller exhibits a fast response for stiffiiess regulation and a good performance for position tracking.

关键词： variable stiffiiess actuator variable stiffness module drive module symmetrical structure doubledeck grooves expandable electrical system

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Fabrication of dense SiBCN monolith at a lower temperature and its high-temperature performance

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Journal of Advanced Ceramics 2024年第8期13卷 1198-1211页

作者： Zi-Bo Niu Daxin Li Dechang Jia Zhihua Yang Kunpeng Lin Yan Wang Paolo Colombo Ralf Riedel Yu Zhou Institute for Advanced Ceramics School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150080China Key Laboratory of Advanced Structural-Function Integrated Materials and Green Manufacturing Technology Ministry of Industry and Information TechnologyHarbin 150080China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150080China Department of Industrial Engineering University of PadovaPadova 3513iItaly Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity Park 16802USA Institute of Materials Science Darmstadt University of TechnologyDarmstadt 64287Germany School of Materials Science and Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

In this study,a crack-free pyrolysis process of partially cured precursor powder compacts was developed to prepare dense silicon boron carbonitride(SiBCN)monoliths at much lower temperatures(1300℃),thereby circumventing the challenges of sintering densification(>1800℃).Unlike the elastic fracture in over-cured precursors or the viscoelastic deformation in under-cured precursors,the partially cured precursor,exhibiting elastic-plastic deformation behavior,facilitates limited nanoscale pore formation in a dense structure,achieving a balance between crack-free pyrolysis and *** to SiBCN derived from the over-cured precursor(σ=~159 MPa,K_(IC)=1.9 MPa:m^(1/2),Vickers hardness(HV)=7.8 GPa,and E=122 GPa),the resulting SiBCN monolith exhibited significantly improved mechanical properties(σ=~304 MPa,K_(IC)=3.7 MPa-m12,HV=10.6 GPa,and E=161 GPa)and oxidation *** addition,this study investigated the high-temperature performance of SiBCN monoliths,including crystallization and oxidation,and determined the oxidation kinetics induced by pore structure healing and the different oxidation mechanisms of Si-C-N and B-C-N clusters in the amorphous *** to its unique composition and structure,the SiBCN ceramic oxide layer exhibits exceptional self-healing effects on repairing the nanoporous system in the initial stage and shows outstanding high-temperature stability during prolonged oxidation,mitigating adverse effects from bubble formation and *** to the nanoporous structure,the oxidation rate is initially controlled by gas diffusion following a linear law before transitioning to oxide layer diffusion characterized by a parabolic ***,due to different valence bond configurations,Si-C-N transforms into an amorphous SiCNO structure after phase separation,unlike the nucleation and growth of residual B-N-C.

关键词： silicon boron carbonitride(SiBCN)ceramics crack-free pyrolysis amorphous structure oxidation mechanisms kinetic law

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Sustainable Seawater Desalination and Energy Management:Mechanisms,Strategies,and the Way Forward

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Research 2024年第3期2023卷 595-611页

作者： Meng Wang Yen Wei Ruoxin Li Xin Wang Chengyu Wang Nanqi Ren Shih-Hsin Ho State Key Laboratory of Urban Water Resource and Environment School of EnvironmentHarbin Institute of TechnologyHarbin 150001China Department of Chemistry Tsinghua UniversityBeijing 100084China Key Laboratory of Bio-based Material Science&Technology(Northeast Forestry University) Ministry of EducationHarbin 150040China.

Solar-driven desalination systems have been recognized as an effective technology to address the water ***,evaporators prepared based on advanced manufacturing technologies have emerged as a promising tool in enhancing ocean energy *** this review,we discussed the thermal conversion,energy flow,salt deposition mechanisms,and design strategies for solar-driven desalination systems,and explored how to improve the desalination performance and energy use efficiency of the systems through advanced manufacturing *** future perspectives,we determined the feasibility of coupling solar-driven solar desalination systems with multi-stage energy utilization systems and emerging artificial intelligence technologies,for which conclusions are given and new directions for future desalination system development are ***,exciting opportunities and challenges in the face of basic research and practical implementation are discussed,providing promising solutions and blueprints for green and novel desalination technologies while achieving sustainable development.

关键词： Sustainable Solar directions

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An adaptive Kalman filter for physiological tremor suppression in robot-assisted minimally invasive surgery

An adaptive Kalman filter for physiological tremor suppressi...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Yang, Daolong Jiang, Li Xu, Kun Robotics Institute of School of Mechanical Engineering and Automation Beijing100191 China Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin150008 China

ISBN: (纸本)9798350325706

Physiological tremors of surgeons' hands bring vibration to the tip of surgical instruments, which may severely damage the accuracy of surgical procedures and even put patients' lives at risk. Thus robot-assisted surgery using a teleoperation system was introduced to solve this problem. Many filter algorithms were implemented into the robot-assisted surgery system to suppress the inevitable tremors, but always overlooking the traits of hand motion. An innovation/residual-based floating forgetting-factor adaptive Kalman filter (IFFAKF) is proposed to address this problem. A floating forgetting-factor is brought into the innovation/residual-based adaptive estimation algorithm to adjust the process noise covariance (Q) and measurement noise covariance (R) of the system, thus generating estimation of voluntary hand movement and suppressing the vibration at the tip of the surgical instrument caused by the physiological tremor during surgeries. The proposed filter has been contrasted with various filters in simulations and experiments. The results confirm that the proposed filter has obvious advantages in terms of time delay, estimation error, and tremor suppression performance. © 2023 IEEE.

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