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Human-machine shared control:New avenue to dexterous prosthetic hand manipulation

Science China(Technological Sciences) 2021年第4期64卷 767-773页

作者： YANG DaPeng LIU Hong State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150080China Artificial Intelligence Laboratory Harbin Institute of TechnologyHarbin 150080China

At present,albeit the dexterous hand prostheses of multiple degrees of freedom(DOFs)have become prosperous on the market,the user’s demand on intuitively operating these devices have not been well addressed so that their acceptance rate is relatively *** unintuitive control method and inadequate sensory feedback are frequently cited as the two barriers to the successful application of these dexterous ***,driven by the wave of artificial intelligence(AI),a series of shared control methods have emerged,in which"bodily function"(myoelectric control)and"artificial intelligence"(local autonomy,computer vision,etc.)are tightly integrated,and provided a new conceptual solution for the intuitive operation of dexterous *** this paper,the background and development trends of this type of methods are described in detail,and the potential development directions and the key technologies that need breakthroughs are *** practice,we instantiate this shared control strategy by proposing a new method combining simultaneous myoelectric control,multi-finger grasp autonomy,and augmented reality(AR)feedback *** method"divides"the human sophisticated reach-and-grasp task into several subtasks,and then"conquers"them by using different strategies from either human or machine *** is highly expected that the shared control methods with hybrid human-machine intelligence could address the control problem of dexterous prostheses.

关键词： robotic hand dexterous prostheses shared control computer vision deep learning

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Design methodology,synthesis,and control strategy of the high-speed planetary rover

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Frontiers of Mechanical Engineering 2024年第2期19卷 59-74页

作者： Renchao LU Haibo GAO Zhen LIU Runze YUAN Zongquan DENG State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China

The planned missions to explore the surfaces of the Moon and Mars require high exploration efficiency,thus imposing new demands on the mobility system of planetary *** this paper,a design method for a high-speed planetary rover(HPR)is proposed,and the representative configurations are modeled and ***,the influence of the planetary surface environment on the design of HPRs is analyzed,and the design factors for HPRs are determined by studying a single-wheel ***,a design methodology for HPRs is *** adaptive suspension mechanisms of a four-wheeled rover are synthesized using the all-wheel-attachment condition and position and orientation characteristics theory,which are expressed in the form of a graph theory for the increase in elastic components and active ***,a dynamic model is built,and a simulation is carried out for the proposed *** validity of the proposed method and rover is verified,thus highlighting their potential application in future planetary exploration.

关键词： design methodology spectrum analysis high-speed planetary rover type synthesis

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Numerical simulation of the vacuum casting process of solid propellants 3

Numerical simulation of the vacuum casting process of solid ...

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2024 3rd International Conference on Acoustics, Fluid Mechanics and Engineering, AFME 2024

作者： Gao, Zhe Wei, Ming Li, Wensen Chen, Cunzhuang Li, Changle Zhao, Jie State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150000 China Hubei Institute of Aerospace Chemical Technology Xiangyang441003 China

Solid propellant is one of the main components of rocket engines, and the casting of low-viscosity propellant slurry usually uses a vacuum casting process. During the vacuum casting process, the rheological properties of solid propellant slurry are complex, and the theoretical analytical method cannot intuitively and accurately analyze its flow characteristics. Therefore, this paper numerically simulates the casting process through simulation software, uses the Herschel-Bulkley model to characterize the changes in the flow characteristics of solid propellant slurry during the vacuum casting process, and studies the effects of different hopper angles and anti-break vacuum plug shapes on the slurry's rheological properties. By analyzing parameters such as slurry mass flow rate, velocity, residual amount in the hopper, and surface pressure of the plug, the appropriate hopper angle and the bottom edge angle of the anti-break vacuum plug are selected. Finally, the slurry leveling characteristics under suitable hopper and plug conditions are analyzed. The results indicate that a hopper angle of 140° and an anti-break vacuum plug bottom angle of 120° are optimal. The entire vacuum casting process takes approximately 42 seconds, and the overall slurry leveling is good. As the casting progresses, the slurry gradually compacts and levels out, and the bubbles at the bottom of both sides of the combustion chamber gradually disappear. © Published under licence by IOP Publishing Ltd.

关键词： Hoppers

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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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PCE: Multi-Agent Path Finding via Priority-Aware Communication & Experience Learning

IEEE Transactions on Intelligent Vehicles

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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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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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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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On-Orbit Thermal Analysis of Ring Truss-Type Deployable Antenna 1

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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: (数字)9789811993985

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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