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IEEE International Conference on Image Processing

作者： Sabeethan Kanagasingham Andrew R. Mills Visakan Kadirkamanathan Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This paper addresses the problem of reconstructing image sequences from a rolling shutter camera-based thermal image acquisition system that integrates the image field over an exposure time. It proposes a novel approach to extend the distributed Kalman filtering framework for intra-frame reconstruction. This accounts for the exposure effect through a state-augmentation model, while respecting the row-byrow image acquisition process. Additionally, two alternative rolling shutter scan strategies: interlaced-x and random, are explored to mitigate delays in observing abrupt changes inherent in sequential rolling shutter scans. Simulation results demonstrate that the proposed approach effectively accommodates exposure and achieves reliable intra-frame reconstruction quality. The interlaced-x scan strategy, with x equal to the size of the image partition block, emerges as the preferred choice, highlighting improved performance in recovering from sudden events. The augmented distributed Kalman filter offers a scalable solution to enhance temporal resolution and overall reliability of thermal imaging of dynamic thermal processes.

关键词： Image resolution Simulation Thermal variables measurement Three-dimensional printing Size measurement Time measurement Steady-state

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Improving Workplace Safety in the Cargo Industry through Posture Monitoring using Mediapipe and Machine Learning 17

Improving Workplace Safety in the Cargo Industry through Pos...

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17th International Conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Cobb, Buket Sonbas Candan, Fethi Harran University Department of Electrical-Electronics Engineering Sanliurfa63190 Turkey University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350338904

This research reveals the potential of Mediapipe with machine learning methods in addressing the critical need for real-time posture monitoring, offering a promising solution for creating secure work environments and improving health and safety conditions not only in the cargo industry but also in other fields, such as healthcare, sports, and industry. The findings indicate that the Random Forest Classifier demonstrates the highest accuracy in classifying untrained movements, indicating its potential for developing a posture correction warning system. Further research is needed to validate the findings and explore real-world applications. Overall, this study contributes to the advancement of workplace safety and sets the foundation for future developments in posture monitoring systems using Mediapipe and machine learning using Python programming. © 2023 IEEE.

关键词： Python

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A Lightweight Target Following Architecture for Indoor Mobile Robot

A Lightweight Target Following Architecture for Indoor Mobil...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Fuli Wang Windo Hutabarat Ashutosh Tiwari Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Manufacturing efficiency and transport operations are being significantly improved by mobile robots. As the implementation of a configurable, lightweight, and stateof-the-art robotic system is required for current manufacturing sectors to maximize the use of mobile robots, this paper presents a software architecture comprised of perception and action systems for human following in real time. The perception system uses the YOLOv8 computer vision model to identify and estimate human pose. Additionally, an algorithm is developed using the 3D information from a stereo camera to determine which target and whether to follow. Once the perception system perceives the desired target information, the action system can control and coordinate the robot using the ROS. Experimental results from the physical robot demonstrate the feasibility of the system architecture.

关键词： Computer vision Three-dimensional displays Service robots Software architecture Robot kinematics Computational modeling systems architecture

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Learning Input Driven Dynamic Bayesian Networks with Measurement Noise

Learning Input Driven Dynamic Bayesian Networks with Measure...

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International Conference on Machine Learning and Applications (ICMLA)

作者： David Veres Ping Li Visakan Kadirkamanathan Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum likelihood Doubly-Iterative Expectation Maximization (DI-EM) Algorithm is developed for the identification of grey-box ARMAX state-space model representations of DBNs involving known, noisy measurement processes. The grey-box model incorporates network dependencies among time series variables and exploits time series data of low longitudinal and high cross-sectional dimensions. A network learning procedure is developed using a score-based structure-selection method to find the underlying network of an input-driven dynamical system. By computing a finite data version of the Bayesian Information Criterion (BIC) for small sample sizes, the proposed method's performance is investigated on simulated and real-world data. The algorithm recovers the underlying ground-truth networks of simulated systems under finite data criteria with Jaccard Coefficient values of up to 0.84, and selects structures with improved weighted mean-squared error loss over a baseline black-box model fit on real-world data.

关键词： Heuristic algorithms Computational modeling Time series analysis Noise Machine learning Data models Bayes methods State-space methods Noise measurement Dynamical systems

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Pulsating Fluidic Sensor for Sensing of Location, Pressure and Contact Area

Pulsating Fluidic Sensor for Sensing of Location, Pressure a...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Joanna Jones Marco Pontin Dana D. Damian Department of Automatic Control and Systems Engineering Sheffield Biomedical Robotics Laboratory University of Sheffield United Kingdom Sheffield Robotics University of Sheffield UK Insigneo Institute for in Silico Medicine University of Sheffield UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Designing information-rich and space-efficient sensors is a key challenge for soft robotics, and crucial for the development of safe soft robots. Sensing and understanding the environmental interactions with a minimal footprint is especially important in the medical context, where portability and unhindered patient/user movement is a priority, to move towards personalized and decentralized healthcare solutions. In this work, a pulsating fluidic soft sensor (PFS) capable of determining location, pressure and contact area of press events is shown. The sensor relies on spatio-temporal resistance changes driven by a pulsating conductive fluid. The sensor demonstrates good repeatability and distinction of single and multiple press events, detecting single indents of sizes greater than 1 cm, forces larger than 2 N, and various locations across the sensor, as well as multiple indents spaced 2 cm apart. Furthermore, the sensor is demonstrated in two applications to detect foot placement and grip location. Overall, the sensor represents an improvement towards minimizing electronic hardware, and cost of the sensing solution, without sacrificing the richness of the sensing information in the field of soft fluidic sensors.

关键词： Presses Fluids Soft sensors Medical services Soft robotics Robot sensing systems Hardware

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Enhancing Thin-Film Wafer Inspection With A Multi-Sensor Array And Robot Constraint Maintenance

arXiv

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arXiv 2025年

作者： Sánchez-Arriaga, Néstor Eduardo Canzini, Ethan Espley-Plumb, Nathan John Farnsworth, Michael Pope, Simon Leyland, Adrian Tiwari, Ashutosh Department of Automatic Control & Systems Engineering University of Sheffield Sheffield United Kingdom Department of Materials Science & Engineering University of Sheffield Sheffield United Kingdom Airbus Robotics Airbus UK Chester United Kingdom

Thin-film inspection on large-area substrates in coating manufacture remains a critical parameter to ensure product quality;however, extending the inspection process precisely over a large area presents major challenges, due to the limitations of the available inspection equipment. An additional manipulation problem arises when automating the inspection process, as the silicon wafer requires movement constraints to ensure accurate measurements and to prevent damage. Furthermore, there are other increasingly important large-area industrial applications, such as Roll-to-Roll (R2R) manufacturing where coating thickness inspection introduces additional challenges. This paper presents an autonomous inspection system using a robotic manipulator with a novel learned constraint manifold to control a wafer to its calibration point, and a novel multi-sensor array with high potential for scalability into large substrate areas. We demonstrate that the manipulator can perform required motions whilst adhering to movement constraints. We further demonstrate that the sensor array can perform thickness measurements statically with an error of 0.5◦ from the calibration point whilst monitoring the RMSE and R2 over 1406 data points. These features are potentially useful for detecting displacement variations in R2R manufacturing processes. © 2025, CC BY-NC-ND.

关键词： Silicon wafers

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Digital framework for metallic subtractive process planning: Liger optimisation case study 57

Digital framework for metallic subtractive process planning:...

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18th IFAC Workshop on Time Delay systems, TDS 2024

作者： Duro, João A. Ward, Rob Rooke, Jack Pickford, Emily Bonnell, Rich Brown, Adam Purshouse, Robin C. Curtis, David Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom University of Sheffield Advanced Manufacturing Research Centre University of Sheffield RotherhamS60 5TZ United Kingdom

Metallic subtractive process planning is challenging. It requires a blend of expert knowledge, experience, and established manufacturing methods. Decision-making in this domain is multifaceted and complex, and this includes the need to satisfy multiple conflicting demands faced by the manufacturing sector today. However, with the evolving landscape of manufacturing, combined with increased adoptions of digital technologies, the traditional rules of thumb are becoming obsolete. In response, this research investigated these decision-making processes within a subset of the digital framework for subtractive process planning. In particular, it has demonstrated that an advanced open-source optimisation tool (Liger) could be integrated with commercial industrial software and applied to a machining case study. The integrated software intelligently executed and automated simulations using advanced optimisation techniques. The outputs are a range of optimised solutions that support manufacturing engineers' decision-making. One of the major benefits of the open-source software is that it provides an intuitive to use interface suitable for the non-expert in optimisation, offers a varied range of state-of-the-art multi-objective optimisation algorithms, and is capable of incorporating many different third-party types of software, models and simulations. In this paper, the Liger software has been applied to a simple case study to find the best feedrate and depths of cut that simultaneously minimise the cutting force and the time it takes to complete the machining process. The case study demonstrates proof-of-principle results that the optimisation software can be implemented in a wider process planning context with additional digital manufacturing simulation models. © 2024 The Authors.

关键词： Process planning

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Structured Observer Synthesis via Static Output Feedback

Structured Observer Synthesis via Static Output Feedback

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UKACC International Conference on control (control)

作者： Hadriano Morales Escamilla Paul Trodden Department of Automatic Control and Systems Engineering (ACSE) The University of Sheffield UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper considers the problem of structured observer synthesis for linear-time-invariant (LTI) systems. The importance of the problem is demonstrated through two motivational examples: utilizing high-fidelity black-box models as part of observers, and anti-windup gain design. The problem is connected with its control counterpart: static output feedback, and the process is illustrated through the design of an anti-windup gain for a multi-variable PID controller. A numerical example shows how the resulting closed-loop system converges faster than a recent method in the literature, with the added benefit of solving the problem with a non-iterative method, which decreases the computational effort.

关键词： Linear systems control design Computational modeling Closed box Process control Observers Numerical models

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A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner

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Cyborg and Bionic systems 2023年第1期4卷 298-308页

作者： Guanglin Ji Qian Gao Tianwei Zhang Lin Cao Zhenglong Sun School of Science and Engineering The Chinese University of Hong KongShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Automatic Control and Systems Engineering The University of SheffieldUK

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential damage by setting constraints and optimizing the insertion ***,reinforcement learning(RL)-based path planning algorithm has shown promising results in neurosurgery,but because of the trial and error mechanism,it can be computationally expensive and insecure with low training *** this paper,we propose a heuristically accelerated deep Q network(DQN)algorithm to safely preoperatively plan a needle insertion path in a neurosurgical ***,a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL *** are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN *** showed promising results of our algorithm in saving over 50 training episodes,calculating path lengths of 0.35 after normalization,which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm,***,the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.

关键词： needle surgical insertion

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Predictive Norm Optimal Iterative Learning control for High-performance Formation control Problem 63

Predictive Norm Optimal Iterative Learning Control for High-...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhang, Yueqing Chen, Bin University of Southampton Faculty of Engineering and Physical Sciences SouthamptonSO17 1BJ United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS10 2TN United Kingdom

ISBN: (纸本)9798350316339

This paper develops a predictive optimisationbased iterative learning control (ILC) strategy for the highperformance formation control problem in networked dynamical systems working repetitively. It avoids the need for exact model information in traditional methods and achieves high performance via a predictive framework incorporating a unique performance index that integrates both immediate and future performance. The proposed framework guarantees geometric convergence of the formation error norm to zero and is capable of handling both heterogeneous and non-minimum phase systems. A distributed implementation of the framework is developed using the Alternating Direction Method of Multipliers to guarantee the framework's scalability for largescale networks. Rigorous convergence analysis and numerical examples are provided to confirm its effectiveness. © 2024 IEEE.

关键词： Predictive control systems

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