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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Lu, Zhenyu Yue, Tianqi Zhao, Zhou Si, Weiyong Wang, Ning Yang, Chenguang University of the West of England Bristol Robotics Laboratory Bristol United Kingdom University of Bristol Department of Engineering Mathematics Bristol United Kingdom School of Computer Science Central China Normal University Wuhan China

ISBN: (纸本)9798350331820

Tactile sensors can be used for motion detection and object perception in robot manipulation. The contact detection within the camera's visual inspection area has been well-developed, but perception outside the field of view of the camera is overlooked. In this paper, we present a new tendon-linked tactile sensor, MechTac, to achieve perceptions inside and outside the field of view. The MechTac is an evolution of the typical TacTip sensor with the following two advantages. 1) The ability to provide perception outside the field of view. This is achieved by using a network of braided tendons to transfer deformation from the blind perception regions (TacSide) to the visual areas (TacTip). 2) The tactility of the TacSide and TacTip is reflected by the movements of multiple papillae pins and visible markers on the pin tips on the inner surface of the TacTip. The pins and markers are differentially sensitive to various touch features, which is similar to the differentiated perceptual ability of humans. TacTip is more sensitive to small touches, corresponding to the fingertip, while the TacSide is less sensitive but has a larger perceptual area, corresponding to the middle part of the finger. Moreover, we propose a new deep learning method to decompose the mixed information affected by the TacSide and the TacTip. A modified DenseNet121 was specifically designed for object perception at the TacTip and localization at the TacSide. The experimental results show that prediction accuracy reaches about 98% for object perception or localization and over 99% for the case requiring two functions. © 2023 IEEE.

关键词： Cameras

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The posterior selection method for hyperparameters in regularized least squares method

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Control Theory and technology 2024年第2期22卷 184-194页

作者： Yanxin Zhang Jing Chen Yawen Mao Quanmin Zhu School of Science Jiangnan UniversityWuxi214122JiangsuChina The Science and Technology on Near-Surface Detection Laboratory Wuxi214122JiangsuChina Department of Engineering Design and Mathematics University of the West of EnglandBristolBS161QYUK

The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.

关键词： Regularization method Hyperparameter System identification Least squares algorithm

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Towards Automation in Pleural Aspiration: Design, Development and Verification*

Towards Automation in Pleural Aspiration: Design, Developmen...

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Mediterranean Conference on Control and Automation (MED)

作者： Anxing Jiang Athanasios Martsopoulos Steven Walker Antonia Tzemanaki School of Engineering Mathematics University of Bristol UK Bristol Robotics Laboratory Bristol Medical School University of Bristol UK

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

This paper introduces a novel automated system for small-volume pleural effusion aspiration procedures. The traditional reliance on clinician experience in manual pleural aspiration can lead to fatigue, insufficient medical resources, as well as variability in outcomes due to human errors and complications during the procedure. The proposed system uses automation to ensure a consistent and standardised approach, aiming at improving procedure efficacy and safety. The setup includes a medical syringe supplemented by 3D-printed components, driven by a linear actuator, and an infrared proximity sensor for the closed-loop system. We controlled the fluid aspirated speed, achieving a minimum plunger movement speed of about 2 mm/s and measuring plunger displacement as a proxy for aspirated liquid volume. Experiments verified the system's performance across different speeds, with a mean error of 2 mm in the closed-loop tests.

关键词： Actuators Automation Liquids Volume measurement System performance Manuals Displacement measurement

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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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Competition for roadside camera monocular 3D object detection

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National Science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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DexiTac: Soft Dexterous Tactile Gripping

arXiv

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arXiv 2024年

作者： Lu, Chenghua Tang, Kailuan Yang, Max Yue, Tianqi Li, Haoran Lepora, Nathan F. The School of Engineering Mathematics and Technology Bristol Robotics Laboratory University of Bristol Bristol United Kingdom The Department of Mechanical and Electrical Engineering Harbin Institute of Technology Harbin150000 China

Grasping objects—whether they are flat, round, or narrow and whether they have regular or irregular shapes—introduces difficulties in determining the ideal grasping posture, even for the most state-of-the-art grippers. In this article, we presented a reconfigurable pneumatic gripper with fingers that could be set in various configurations, such as hooking, supporting, closuring, and pinching. Each finger incorporates a dexterous joint, a rotating joint, and a customized plug-and-play visuotactile sensor, the DigiTac-v1.5, to control manipulation in real time. We propose a tactile kernel density manipulation strategy for simple and versatile control, including detecting grasp stability, responding to disturbances and guiding dexterous manipulations. We develop a double closed-loop control system that separately focuses on secure grasping and task management, demonstrated with tasks that highlight the capabilities above. The gripper is relatively easy to fabricate and customize, offering a promising and extensible way to combine soft dexterity and tactile sensing for diverse applications in robotic manipulation. © 2024, CC BY.

关键词： Closed loop control systems

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Pose and shear-based tactile servoing

arXiv

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arXiv 2023年

作者： Lloyd, John Lepora, Nathan F. School of Engineering Mathematics and Technology University of Bristol United Kingdom Bristol Robotics Laboratory University of Bristol United Kingdom

Tactile servoing is an important technique because it enables robots to manipulate objects with precision and accuracy while adapting to changes in their environments in real-time. One approach for tactile servo control with high-resolution soft tactile sensors is to estimate the contact pose relative to an object surface using a convolutional neural network (CNN) for use as a feedback signal. In this paper, we investigate how the surface pose estimation model can be extended to include shear, and utilize these combined pose-and-shear models to develop a tactile robotic system that can be programmed for diverse non-prehensile manipulation tasks, such as object tracking, surface following, singlearm object pushing and dual-arm object pushing. In doing this, two technical challenges had to be overcome. Firstly, the use of tactile data that includes shear-induced slippage can lead to error-prone estimates unsuitable for accurate control, and so we modified the CNN into a Gaussian-density neural network and used a discriminative Bayesian filter to improve the predictions with a state dynamics model that utilizes the robot kinematics. Secondly, to achieve smooth robot motion in 3D space while interacting with objects, we used SE(3) velocity-based servo control, which required re-deriving the Bayesian filter update equations using Lie group theory, as many standard assumptions do not hold for state variables defined on non-Euclidean manifolds. In future, we believe that pose and shear-based tactile servoing will enable many object manipulation tasks and the fully-dexterous utilization of multi-fingered tactile robot hands. © 2023, CC BY.

关键词： Convolutional neural networks

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MagicTac: A Novel High-Resolution 3D Multi-layer Grid-Based Tactile Sensor

arXiv

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arXiv 2024年

作者： Fan, Wen Li, Haoran Zhang, Dandan The Department of Bioengineering Imperial College London United Kingdom The School of Engineering Mathematics and Technology Bristol Robotics Laboratory University of Bristol United Kingdom

Accurate robotic control over interactions with the environment is fundamentally grounded in understanding tactile contacts. In this paper, we introduce MagicTac, a novel high-resolution grid-based tactile sensor. This sensor employs a 3D multi-layer grid-based design, inspired by the Magic Cube structure. This structure can help increase the spatial resolution of MagicTac to perceive external interaction contacts. Moreover, the sensor is produced using the multi-material additive manufacturing technique, which simplifies the manufacturing process while ensuring repeatability of production. Compared to traditional vision-based tactile sensors, it offers the advantages of i) high spatial resolution, ii) significant affordability, and iii) fabrication-friendly construction that requires minimal assembly skills. We evaluated the proposed MagicTac in the tactile reconstruction task using the deformation field and optical flow. Results indicated that MagicTac could capture fine textures and is sensitive to dynamic contact information. Through the grid-based multi-material additive manufacturing technique, the affordability and productivity of MagicTac can be enhanced with a minimum manufacturing cost of £4.76 and a minimum manufacturing time of 24.6 minutes. Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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Down the Rabbit Hole: Exploiting Airflow Interactions for Morphologically Intelligent Retracting Vacuum Grippers

Down the Rabbit Hole: Exploiting Airflow Interactions for Mo...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Loong Yi Lee Silvia Terrile Ajmal Roshan Tianqi Yue Saekwang Nam Jonathan Rossiter the Bristol Robotics Laboratory School of Engineering Mathematics and Technology University of Bristol UK Graduate School of Data Science Kyung-pook National University Daegu Korea

Suction cups and closed bellows actuators are commonly used in industrial pick and place tasks. They can be combined and actuated by a single negative pressure line, providing a simple coupling between suction and actuation. However, the capabilities of this - seemingly very simple - system go beyond just being able to grasp and retract an object. By varying the morphology of the suction cup and by tuning airflow between the two serially connected elements, retraction of the bellows changes and functional embodied behaviours emerge. This work explores the novel concept of flexible Sucking & Retracting Bellows (SuReBellows) comprised of a suction cup and a long-stroke bellows actuator, and how they can be tuned for morphologically intelligent behaviours. Our primary investigation explores how the retraction of the bellows can be tuned to enable “exploration” on the surface of an unknown object in order to make a good vacuum seal contact for grasping. Several suction cup designs were tested in grasping a general object set, by varying the compliance and airflow between the bellows and the suction cup. The results show that SuReBellows can explore the surface of objects and find grasp points just by cycling one pressure signal for actuation, with the search pattern and adaptability encoded in its morphology. The grasping force of the system was then investigated. Finally, demonstrations of interesting applications such as bin picking and grasping in confined environments are shown.

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