Tag: LABREN

We are excited to share our latest research entitled โ€œDisentangling Contact Location for Stretchable Tactile Sensors from Soft Waveguide Ultrasonic Scatter Signalsโ€ collaborated by Zhiheng Li and Yuan Lin, has been accepted by Advanced Intelligent Systems!

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Flexible tactile sensors have garnered significant attention due to their flexible, lightweight, and comfortable nature, catering to the growing demands in various applications such as human-machine interfaces, healthcare, and robotics. However, it remains a challenge to achieve precise contact location sensing that is decoupled from sensor strain and touching forces. Thus, this paper proposes a novel data-driven approach for force contact location sensing (FCLS), based on scatter signals (SS) of the ultrasonic waveguide, with the influence of sensor strain and forces. This method utilizes deep CNNs to fuse local and global features of the soft waveguide ultrasonic SS and an MLP to perform regression modeling on the fused features and FCL, thereby obtaining FCL information. The experimental results indicate that the accuracy of the proposed FCLS method has an MAE loss of 0.627 mm and an MRE loss of 3.19%.

The full paper will be available at: https://lnkd.in/gKr22Uvi

Co-Authors: Zhiheng Li (CUHK), Yuan Lin (SJTU), Peter B. Shull (SJTU) and Hongliang Ren (CUHK). The first two authors contributed equally to this work.

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Our latest research work titled โ€œA Real-Time Self-Sensing Approach to Sensor Array Configuration Fusing Prior Knowledge for Reconfigurable Magnetic Tracking Systemsโ€ has been accepted by IEEE/ASME Transactions on Mechatronics!

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Impact:

The reconfigurability of magnetic tracking systems (MTSs) allows for its application in workspaces of different sizes. For instance, with appropriate configuration adjustments, it can be utilized for tasks such as tongue tracking in the head, tracheal intubation navigation in the neck, and even muscle tracking in the legs and arms. However, the dynamic changes in sensor array configuration, known as deformation, caused by posture changes during long-duration examinations, impose significant challenges on MTSs that heavily rely on magnetometer poses.

Here we propose a real-time self-sensing method based on the sensor array structural model and magnetic dipole model, which simultaneously estimates the magnet pose and the hinge angles on the sensor array. The self-sensing capability opens up a new way for perceiving the morphology of origami robots and measuring the curvature of flexible catheters

Paper: https://lnkd.in/dnmcvusm

Authors: Shijian Su; Xindi Yang; Zhen Li; Hongliang Ren

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We are happy to share our journal paper titled โ€œAMagPoseNet: Real-Time 6-DoF Magnet Pose Estimation by Dual-Domain Few-Shot Learning from Prior Modelโ€ published in IEEE Transactions on Industrial Informatics!

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Impact: Traditional magnetic tracking approaches based on mathematical models and optimization algorithms are computationally intensive, depend on initial guesses, and do not guarantee convergence to a global optimum. Here we propose an annular magnet pose estimation network (called AMagPoseNet) based on dual-domain few-shot learning from a prior mathematical model, featuring the following advantages:

1) Higher localization accuracy (1.87ยฑ1.14 mm, 1.89ยฑ0.81ยฐ), especially in the near field;

2) Enhanced robustness, as AMagPoseNet is just a single feed-forward neural network that does not rely on initial guesses and avoids the risk of falling into local optima;

3) Lower computational latency (2.08ยฑ0.02 ms) since the magnet pose is directly regressed from a single feed-forward network rather than iterative optimization;

4) Real-time estimation of 6-DoF pose if discriminative magnetic field features are provided.

Paper: https://lnkd.in/gXBSQ-hG

Dataset: https://lnkd.in/gXEFfwfA

Authors: Shijian Su; Sishen YUAN; Mengya Xu; Huxin Gao; Xiaoxiao Yang; and Prof Hongliang Ren

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Excited to share our journal paper entitled โ€œMagnetic Tracking With Real-Time Geomagnetic Vector Separation for Robotic Dockable Chargingโ€ published in IEEE Transactions on Intelligent Transportation Systems! ๐ŸŽ‰

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Great collaboration between the Chinese University of Hong Kong and the Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences. ๐Ÿค

The superposition of the geomagnetic vector and the magnetic field vector generated by the permanent magnet (PM) leads to the degrading of magnetic tracking performance. Here we present a real-time geomagnetic-vector-separation method to estimate the PM pose and geomagnetic vector simultaneously. This advancement promises to revolutionize autonomous robotic operations, offering a robust solution for seamless and reliable self-charging mechanisms, with far-reaching implications for various industries.

Paper: https://lnkd.in/gAbr82Dp

Authors: Shijian Su; Houde Dai; Yuanchao Zhang; Sishen YUAN; Prof Shuang Song and Prof Hongliang Ren.

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๐ŸŽ‰ Thrilled to unveil our latest breakthrough! ๐ŸŒŸ Our paper, “Dual-Stroke Soft Peltier Pouch Motor Based on Pipeless Thermo-Pneumatic Actuation” collaborated by WENCHAO YUE and Chengxi Bai, has been published in Advanced Engineering Materials (cover invitation)!

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๐Ÿ’ก Soft pneumatic actuators are at the heart of soft robotics, offering reliability, safety, and flexibility. However, conventional bulky air compressors and pipes have limited their integration and lightweight design. Enter the Peltier pouch motor (PPM), a cutting-edge soft thermoelectric-based actuator that redefines possibilities in the field.

๐Ÿ” The PPM introduces modular and dual-stroke capabilities through active phase transition of a low-boiling-point liquid, enabling pipeless thermo-pneumatic actuation. Its lightweight and stretchable design fosters hyper-modularity, paving the way for diverse degrees-of-freedom hybrid systems.

๐Ÿš€ From thermo-responsive land locomotion to submersible noise-free hovering and beyond, the PPM excels in various applications, including smart curtains control, body-temperature-driven wrist rehabilitation, and adaptive hybrid gripping. Our results showcase exceptional performance metrics, highlighting high load rates (around 400%), remarkable heat transfer efficiency (heating boost 425%, cooling boost 138%), and rapid thermal response (heating 0.57ยฐโ€‰sโˆ’1, cooling 0.29ยฐโ€‰sโˆ’1 at 4.5โ€‰V).

Paper link: https://lnkd.in/dPbyXHQd

Co-authors: WENCHAO YUE, Chengxi Bai, Prof Sam, Jiewen Lai, and Prof Hongliang Ren.

๐Ÿ”ฅ Join us on this groundbreaking journey as we push the boundaries of soft robotics with the innovative Peltier pouch motor! ๐Ÿค–โœจ

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๐Ÿ“Š **Empowering Robotic Surgery with SAM 2: An Empirical Study on Surgical Instrument Segmentation** ๐Ÿค–๐Ÿ‘จโ€โš•๏ธ

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We’re excited to share our latest research on the Segment Anything Model (SAM) 2. This empirical evaluation uncovers SAM 2’s robustness and generalization capabilities in surgical image/video segmentation, a critical component for enhancing precision and safety in the operating room.

๐Ÿ”ฌ **Key Findings**:

– In general, SAM 2 outperforms its predecessor in instrument segmentation, showing a much improved zero-shot generalization capability to the surgical domain.

– Utilizing bounding box prompts, SAM 2 achieves remarkable results, setting a new benchmark in the surgical image segmentation.

– With a single point as the prompt on the first frame, SAM 2 demonstrates substantial improvements on video segmentation over SAM, which requires point prompts on every frames. This suggests great potential in addressing video-based surgical tasks. 

– Resilience Under Common Corruptions: SAM 2 shows impressive robustness against real-world image corruption, maintaining performance under various challenges such as compression, noise, and blur.

๐Ÿ”ง **Practical Implications**:

– With faster inference speeds, SAM 2 is poised to provide quick, accurate segmentation, making it a valuable asset in the clinical setting.

๐Ÿ”— **Learn More**:

For those interested in the technical depth, our paper is available on [arXiv](https://lnkd.in/gHfdrvj3).

We’re eager to engage with the community and explore how SAM 2 can revolutionize surgical applications.

Thanks to the team contributions of Jieming YU, An Wang, Wenzhen Dong, Mengya Xu, Jie Wang, Long Bai, Hongliang Ren from Department of Electronic Engineering, The Chinese University of Hong Kong and Shenzhen Research Institute of CUHK, and Mobarakol Islam from WEISS – Wellcome / EPSRC Centre for Interventional and Surgical Sciences, UCL.

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