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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Chen Chen Xiangyang Zhu State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China Meta Robotics Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Objective. Decoding motor unit activities from surface electromyography (sEMG) has been demonstrated as a promising technique for human-machine interfacing (HMI) in the past decade. However, the current decoding methods have difficulty tracking motor unit discharges across experiments, limiting the motor unit-based HMI within the intra-trial analysis. This study focused on motor unit decoding across multi-days and aimed to demonstrate the feasibility of pretrained separation vectors for motor unit tracking. Methods. One subject participated in the experiment that lasted five days and performed the isometric elbow flexion at four contraction levels daily. The high-density sEMG signals of 64 channels were recorded from the biceps brachii and decomposed into motor unit spike trains (MUSTs) based on the convolutional kernel compensation algorithm. The separation vectors were kept to decompose the sEMG signals in the subsequent days. The multichannel motor unit action potential (MUAP) waveforms were extracted and compared to validate the tracking performance across days. Main results. On average, 20±6 MUSTs were identified from each contraction intensity, among which 3±2 MUSTs were successfully tracked across five days. The MUSTs tracked across 2–4 days were l0±4, 6±4, and 4±3. Conclusion and Significance. These results indicate the feasibility of motor unit tracking across multiple days based on the pretrained separation vectors, extending the potential applications of neural decoding in HMI.

关键词： Source separation Limiting Surface discharges Human-machine systems Motors Vectors Discharges (electric) Decoding Kernel Robots

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DogGet: A Legged Manipulation system in Human Environments 12

DogGet: A Legged Manipulation System in Human Environments

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

作者： Linghan, Meng Cong, Wang Shengguo, Cui Qifeng, Zhang Baoqi, Zhai 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 Guangzhou511458 China Key Laboratory of Marine Robotics Liaoning Province Shenyang110169 China University of the Chinese Academy of Sciences Beijing100049 China Guangdong Institute of Intelligent Unmanned System Guangzhou511458 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The problem of population ageing in today's world is becoming increasingly serious, and the capacity gap in the service industry for elderly people is increasing. Especially in the current epidemic environment, the deartificialization of the service industry is imminent. For service tasks of seniors, there is no doubt that legged robots are the best choice for service tasks due to there flexibility and agility. However, complex service tasks require a mobile robotic system that integrates functions such as localization, navigation, recognition, and grasping. To this end, we propose a mobile robotic system to accomplish some daily service work such as finding and grasping some articles for daily use and then delivering them to a proposed place. We show that the system is universally applicable and highly efficient in daily environments. Videos of the experiments can be viewed at https://***/xPeBzDf6D_k or https://***/video/BV1QZ4yli7qx. © 2022 IEEE.

关键词： HTTP

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter 8

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Lin, Qiguang Yan, Chaojie Li, Qiang Ling, Yonggen Zheng, Yu Lee, Wangwei Wan, Zhaoliang Huang, Bidan Liu, Xiaofeng College of IoT Engineering Hohai University Jiangsu Key Laboratory of Special Robotic Technology Jiangsu Changzhou213022 China Zhejiang University Institute of Cyber-System and Control State Key Laboratory of Industrial Control and Technology Hangzhou China Bielefeld33619 Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350300178

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks. © 2023 IEEE.

关键词： Extended Kalman filters

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A Novel Thrust Allocation Method for Underwater Robots 12

A Novel Thrust Allocation Method for Underwater Robots

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

作者： Zheng, Xin-Hui Wang, Cong Yang, Xue-Jiao Tian, Qi-Yan Zhang, Qi-Feng Zhai, Bao-Qi 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 Guangzhou511458 China Key Laboratory of Marine Robotics Liaoning Province Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Institute of Intelligent Unmanned System Guangzhou511458 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Underwater vehicles with normal thruster configuration methods are unsatisfying in position-keeping tasks because of their limited manoeuvrability. To this end, this paper proposes an underwater ve-hicle with a novel thruster configuration method and solves the thrust allocation problem by the sequential quadratic programming(SQP) algorithm. Comparisons between the performance of the proposed thruster configuration method and the most widely used config-uration method are given based on the UUVSimulator and Gazebo. Numerical simulations show that vehicle with the proposed thrust configuration method has better performance in manoeuvrability and energy consumption. © 2022 IEEE.

关键词： Energy utilization

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Simulation and Experimental Study of Piezoelectric Stack Energy Harvester with Arch Beam Applied to the Railway system

SSRN

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SSRN 2022年

作者： Min, Zhaowei Hou, Chengwei Sui, Guangdong Shan, Xiaobiao Xie, Tao State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

As one of the most important modes of transportation, the safety of running vehicles and railway track is significant. It is essential to power sensors that detect and track health in remote areas. The vibration energy of the track structure is enormous and stable, which is not limited by weather factors such as solar and wind power. A new type of arch beam piezoelectric stack energy harvester for railway systems is studied in this paper. Through simulation analysis and experimental verification of the energy harvester, the influences of external resistance, load, pre-pressure, and load frequency on the energy harvesting performance of the piezoelectric energy harvester are discussed. When the frequency is less than 6Hz, the energy capture efficiency is greatly affected by the frequency. When the frequency exceeds 6Hz, the frequency has little effect, and the load dramatically affects the energy capture efficiency. The pre-pressure is not the more significant, the better, but there is an optimal value at 4.5kN. The energy capture circuit can be combined with the supercapacitor to power the sensor, forming a self-power device for real-time monitoring of track health and driving safety. © 2022, The Authors. All rights reserved.

关键词： Arches

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An RRT-Based Motion Planning Method for Hyper-Redundant Manipulators in Confined Spaces

An RRT-Based Motion Planning Method for Hyper-Redundant Mani...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Deng, Jiangqin Li, Ziqing Zheng, Yang Gu, Guoying School of Mechanical Engineering State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai200240 China Meta Robotics Institute Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665481090

Hyper-redundant manipulators have redundant degrees of freedom, bringing additional difficulties to the motion planning. Generally, existing motion planning methods generate paths without considering joint angle limitation of hyper-redundant manipulators. And the generated paths are discretized, leading to discrete errors. In this paper, we present an autonomous motion planner to generate paths, which can be followed by hyper-redundant manipulators with analytical solutions. Starting from the ending pose in the workspace, the rapidly exploring random tree can expand to multiple entrances with limited curvature of arc segments, which ensures that the joint angle limitation is satisfied. Meanwhile, the generated path consists of arc segments, which makes the generated paths can be followed with analytical solutions. Several simulations are conducted to demonstrate the aforementioned advantages. For further validation of the planner's effectiveness, a hyper-redundant manipulator system is used to follow the generated path with follow-the-leader motion. © 2022 IEEE.

关键词： Motion planning

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Laser Implicit Interaction Technology Based on Object Affordance for Wmra

SSRN

引用

SSRN 2022年

作者： Liu, Yaxin Liu, Yan Yao, Yufeng Zhong, Ming State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

It is particularly important for the disabled to operate the wheelchair mounted robotic arm (WMRA) with as few limb movements as possible. This human-robot implicit interaction problem for the elderly and handicapped has attracted extensive attention. Several studies have documented that laser pointer interaction is an attractive method enabling humans to unambiguously point out objects and pick them up. However, how to further convey user intention and instruct the robot to complete complex tasks based on this method has yet to be studied. Therefore, based on the idea of object affordance, this paper proposes an implicit intention interaction technology using a laser pointer. The user could point to the object in the scene through the residual limb movement ability and the WMRA will reason the action intention and assemble the task content according to the intention. Firstly, a laser semantic identification algorithm based on the YOLOv4 & the SVM is proposed to identify laser points in the scene, which can effectively avoid the error operation caused by hand twitch. Then, an automatic execution technology based on the concept of object affordance is proposed to perform household tasks, which would identify the implicit action intention of the selected objects based on the conditional random field and assemble the mapping action of intention based on the finite state machine. Finally, the feasibility of the proposed implicit interaction technology is verified on the WMRA platform. The evaluation results show that the user only needs a simple operation and the WMRA can recognize the user's desired intentions and perform household tasks automatically. Compared with previous methods, the proposed method can output the desired intention faster and significantly reduce the user's limb involvement time (about 85%) in operating the WMRA under the same task. © 2022, The Authors. All rights reserved.

关键词： Random processes

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Ultrahigh energy density and rapid response dielectric elastomer for advanced soft robots

引用

Chemical Engineering Journal 2025年 515卷

作者： Du, Chengzhen Yan, Peinan Zou, Jiang Guo, Yutong Pang, Zimo Li, Sijia Chen, Yujie Fan, Qunfu Liu, Hezhou State Key Laboratory of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Robotics Institute State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China National Engineering Research Center of Special Equipment and Power System for Ship and Marine Engineering Shanghai200030 China

Dielectric elastomer actuators (DEAs) require high energy density to simultaneously achieve high mechanical output while enabling compact and lightweight actuator design. However, the realization of high energy density is constrained by the high driving electric field and the significant hysteretic relaxation. Herein, we present a DE featuring a bimodal-network structure and multiple hydrogen bonds interactions, which possesses outstanding properties, including a low Young's modulus, optimal dielectric properties, and low mechanical loss. The DE achieves a maximum actuation strain of 200 % at a low driving electric field of 60 V μm−1 with a rapid response. Remarkably, it exhibits an output force of 0.95 N and an ultrahigh specific energy of 283 J kg−1, approximately 7 times that of natural muscle. A series of soft robots with different structures and functions are fabricated to showcase the immense potential of our DE in various applications. This work provides a novel strategy to fabricate high performance artificial muscles for soft robotics. © 2025 Elsevier B.V.

关键词： Dielectric properties of solids

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Centra-Net: A Centralized Network for Visual Localization Spanning Multiple Scenes

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IEEE Transactions on Multimedia 2025年

作者： Jiang, Zhiqiang Dai, Kun Wang, Ke Xie, Tao Fan, Zhendong Li, Ruifeng Kang, Peng Zhao, Lijun Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin150006 China Harbin Institute of Technology Zhengzhou Research Institute Zhengzhou450000 China Jianghuai Advance Technology Center Hefei230000 China

We present Centra-Net, a centralized network that concurrently optimizes visual localization over numerous scenes under heterogeneous dataset domains. Centra-Net exemplifies storage efficiency by amalgamating multiple models with task-shared parameters into a singular cohesive structure. Technically, we develop a basic feature extraction unit (BFEU) with two parallel branches: one dedicated to local feature extraction and the other adept at adaptively generating a task-specific attention mask for feature calibration, thus bolstering its feature extraction capability across diverse scenes. Based on the BFEU, we introduce a filter-wise sharing mechanism (FSM) that adaptively determines parameter sharing within the unit, thus facilitating fine-grained parameter allocation. The key insight of FSM resides in reconceptualizing the parameter sharing of the unit as a learnable paradigm, enabling the determination of shared parameters to be made post-training. Finally, we suggest a complexity-prioritized gradient algorithm (CPGA) that capitalizes on task complexity to attain a harmonious learning space for various tasks, thus safeguarding optimal performances across all tasks. Through rigorous experiments on numerous benchmarks, Centra-Net demonstrates a notable edge over existing state-of-the-art works while operating with a significantly reduced parameter footprint. © 1999-2012 IEEE.

关键词： Deep learning Feature extraction Task complexity Visual localization

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YOWO-Plus: An Incremental Improvement

arXiv

引用

arXiv 2022年

作者： Yang, Jianhua State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

In this technical report, we would like to introduce our updates to YOWO, a real-time method for spatio-temporal action detection. We make a bunch of little design changes to make it better. For network structure, we use the same ones of official implemented YOWO, including 3D-ResNext-101 and YOLOv2, but we use a better pretrained weight of our reimplemented YOLOv2, which is better than the official YOLOv2. We also optimize the label assignment used in YOWO. To accurately detection action instances, we deploy GIoU loss for box regression. After our incremental improvement, YOWO achieves 84.9% frame mAP and 50.5% video mAP on the UCF101-24, significantly higher than the official YOWO. On the AVA, our optimized YOWO achieves 20.6% frame mAP with 16 frames, also exceeding the official YOWO. With 32 frames, our YOWO achieves 21.6 frame mAP with 25 FPS on an RTX 3090 GPU. We name the optimized YOWO as YOWO-Plus. Moreover, we replace the 3D-ResNext-101 with the efficient 3D-ShuffleNet-v2 to design a lightweight action detector, YOWO-Nano. YOWO-Nano achieves 81.0 % frame mAP and 49.7% video frame mAP with over 90 FPS on the UCF101-24. It also achieves 18.4 % frame mAP with about 90 FPS on the AVA. As far as we know, YOWO-Nano is the fastest state-of-the-art action detector. Our code is available on https://***/yjh0410/PyTorch_YOWO. © 2022, CC BY.

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