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arXiv 2024年

作者： Hu, Yang Ghadimi, Pezhman Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Dublin 4 Ireland

In light of the Industry 4.0 era, the global pandemic, and wars, interest in deploying digital technologies to increase supply chain resilience (SCRes) is rising. The utilization of recommender systems as a supply chain (SC) resilience measure is neglected, although these systems can enhance SC resilience. To address this problem, this research proposed a data-driven supply chain disruption response framework based on intelligent recommender system techniques. A prototype implementation was conducted to validate the developed framework through a practical use case. Results show that the proposed framework can be implemented as an effective SC disruption mitigation measure in the SCRes response phase and help SC participants better react after the SC disruption. © 2024, CC BY-NC-SA.

关键词： Decision support systems

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An Efficient Method for Solving Routing Problems With Energy Constraints Using Reinforcement Learning

An Efficient Method for Solving Routing Problems With Energy...

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International Conference on Ubiquitous Robots (UR)

作者： Haggi Do Hakmo Son Jinwhan Kim Department of Mechanical Engineering Mobile Robotics and Intelligence Laboratory Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

With the increasing popularity of electric vehicles (EV s), the research field of planning efficient routes for these vehicles is gaining growing attention. As there are a limited number of charging stations for EVs compared to gas stations for fossil fuel vehicles, EV routing requires careful consideration of energy constraints and replenishment. The classical traveling salesperson problem (TSP) and vehicle routing problem (VRP) are known to be NP-hard, which means that the electric vehicle routing problem (EVRP), a similar problem with added energy constraints, is computationally even more challenging. Recently, reinforcement learning (RL) is being suggested as an effective tool that can alleviate the computational burden of challenging problems. This paper presents a RL-based method for solving routing problems with energy constraints. Multi-head attention mechanisms are employed for both the encoder and decoder, and a masking scheme is applied at the decoding phase in order to compute a feasible solution and minimize the energy constraint violation. This method generates an efficient route in which all task nodes are visited while meeting the energy requirements by visiting the charging stations when needed. The performance of the methodology is demonstrated through a Monte Carlo simulation, and the results are discussed and analyzed.

关键词： Monte Carlo methods Reinforcement learning Charging stations Routing Electric vehicles Transformers Decoding

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Iterative Shaping of Multi-Particle Aggregates Based on Action Trees and VLM

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IEEE robotics and Automation Letters 2025年第7期10卷 7102-7109页

作者： Lee, Hoi-Yin Zhou, Peng Duan, Anqing Yang, Chenguang Navarro-Alarcon, David The Hong Kong Polytechnic University Department of Mechanical Engineering Kowloon Hong Kong The Great Bay University School of Advanced Engineering Dongguan 523000 China Mohamed Bin Zayed University of Artificial Intelligence Department of Robotics Abu Dhabi United Arab Emirates University of Liverpool Department of Computer Science Liverpool L693BX United Kingdom

In this paper, we address the problem of manipulating multi- particle aggregates using a bimanual robotic system. Our approach enables the autonomous transport of dispersed particles through a series of shaping and pushing actions using robotically controlled tools. Achieving this advanced manipulation capability presents two key challenges: high-level task planning and trajectory execution. For task planning, we leverage Vision Language Models (VLMs) to enable primitive actions such as tool affordance grasping and non-prehensile particle pushing. For trajectory execution, we represent the evolving particle aggregate's contour using truncated Fourier series, providing efficient parametrization of its closed shape. We adaptively compute trajectory waypoints based on group cohesion and the geometric centroid of the aggregate, accounting for its spatial distribution and collective motion. Through real-world experiments, we demonstrate the effectiveness of our methodology in actively shaping and manipulating multi- particle aggregates while maintaining high system cohesion. © 2016 IEEE.

关键词： action trees multi-particle manipulation Robot manipulation shape control VLM

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INVESTIGATING SUSTAINABLE PRODUCTION PLANNING OF AN INDUSTRIAL PROGRESSIVE DIE STAMPING PROCESS USING DISCRETE EVENT SIMULATION

INVESTIGATING SUSTAINABLE PRODUCTION PLANNING OF AN INDUSTRI...

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ASME 2024 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2024

作者： Borbolla, Adrián Aboujarra, Mohamed Nezar Ramanujan, Devarajan Advanced Manufacturing Engineering Grundfos A/s Bjerringbro Denmark AIRA Laboratory Smart Factory and Robotics Université Hassan 1er Settat Morocco Department of Mechanical and Production Engineering Aarhus University Aarhus Denmark Center for Digitalisation Big Data and Data Analytics Aarhus University Aarhus Denmark

ISBN: (纸本)9780791888391

This paper investigates the use of discrete event simulation (DES) as a potential decision-support tool for environmentally sustainable production planning. We propose a comprehensive methodology that integrates DES design, dynamic CO2 intensity calculations, and in-depth scenario analysis. This approach facilitates simulation-driven production planning with an emphasis on evaluating and reducing the environmental impacts of manufacturing. Our methodology is applied to the evaluation of an industrial progressive die stamping process (PDSP). The PDSP is a part of a production line in a large Danish manufacturing company. The performance of the PDSP was simulated and evaluated for two working days: 09 Jan 2024 and 10 Jan 2024. Key performance indicators like utilization rate and lead time are assessed alongside environmental sustainability metrics such as process CO2 emissions and CO2 emissions per product. Building upon this baseline, we then simulate and compare two alternative production scenarios: i) increased production capacity and ii) variations in planning orders. These scenarios are then contrasted with the baseline model. Our results demonstrate the potential of DES in identifying new production strategies. The first scenario demonstrates a 7% increase in production capacity, corresponding to a 2.6% increase in process CO2 emissions. The second scenario demonstrates a 5% reduction in process CO2 emissions while retaining the same production capacity as the baseline model. These findings illustrate how DES can uncover production plans that prioritize sustainable manufacturing. Copyright © 2024 by ASME.

关键词： Stamping

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Research on Attitude and Control of a Bionic Manta Robot

Research on Attitude and Control of a Bionic Manta Robot

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Yuntao Guan Aihu Jia Yongxi Liu Jianyang Liu Zheming Zhuang Jian S. Dai International Centre for Advanced Mechanisms and Robotics School of Mechanical Engineering Tianjin University Tianjin P R China School of Mechanical Engineering Tianjin University Tianjin P R China Mechanisms and Robotics Centre for Robotics Research Institute for Robotics Southern University of Science and Technology Shenzhen P R China

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

The bionic manta robot intertwines two contemporary areas of interest: biomimetics and sub aquatic robotics. Drawing insights from a proposed bionic manta robot, this research analyzes the motion attitude of the robot and establishes the relevant parameters. In order to achieve the smooth gait switching of the robot, a CPG controller based on Hopf oscillator is applied in this paper, and the impact of different parameters on the robot motion is studied. Ultimately, the prototype of the robot is crafted and the motion attitude is validated through pool experiments, showcasing its impressive performance capabilities.

关键词： Robot motion Attitude control Biomimetics Biological system modeling Prototypes Switches Control systems

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A ResNet-based approach for accurate radiographic diagnosis of knee osteoarthritis

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 512-521页

作者： Yu Wang Shibo Li Baoliang Zhao Jianwei Zhang Yuanyuan Yang Bing Li School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhenChina Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina TAMS Department of InformaticsUniversity of HamburgHamburgGermany

Currently X-ray images are clinically graded by experienced clinicians using the Kellgren and Lawrence(KL)scoring ***,individual scoring is subjective and error *** study proposes an approach for automated knee osteoarthritis classification based on deep neural *** knee X-ray images are first preprocessed with frequency-domain filtering and histogram normalisation,making the trabecular bone texture more obvious and benefiting the subsequent classification ***,a two-step classification strategy is proposed by extracting the joint centre based on the VGG network and classifying osteoarthritis grades based on the ResNet-50 *** addition,a rebalance operation is proposed to deal with the dataset unbalance problem,and a quick search technique is proposed to improve the iterative search efficiency for the joint *** all of these techniques,a classification accuracy of 81.41%is obtained,which is higher compared to the state-of-the-art approaches.

关键词： AMSGrad knee osteoarthritis recognition ResNet VGG

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Control of Continuum Manipulators with Shape Constraints via Deep Reinforcement Learning

Control of Continuum Manipulators with Shape Constraints via...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Nima Maghooli Omid Mahdizadeh Mohammad Bajelani S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Continuum robots, while versatile for handling complex tasks, present significant challenges in control system design. This paper introduces a novel framework that integrates position and orientation control laws to address shape constraints effectively. Specifically, a Deep Reinforcement Learning (DRL) strategy is proposed to facilitate trajectory tracking within the desired orientation. To ensure safety, the framework directly controls the end-effector's position and orientation in the workspace, avoiding unsafe zones. A centralized control law, the Modified Transpose Jacobian (MTJ), is employed to resolve the robot's redundancy without relying on inverse kinematics. The proposed approach is validated through simulations on a Tendon-Driven Continuum Robot (TDCR), demonstrating superior performance compared to similar learning-based controllers.

关键词： Meters Mechatronics Shape Trajectory tracking Termination of employment Position control Kinematics Deep reinforcement learning Safety Continuum robots

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An integrated decision-making process for sustainable supplier selection and order allocation in the automotive industry

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Procedia CIRP 2024年 122卷 1036-1041页

作者： Pezhman Ghadimi Kubra Sar Amir Hossein Azadnia Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Belfield Dublin Ireland School of Business Maynooth University Maynooth Ireland

A sustainable supplier selection problem can be concisely defined as a classical supplier selection problem that incorporates environmental and social criteria to select and monitor suppliers’ performances, creating the sustainable supplier selection and order allocation (SSS&OA) problem. Finding the optimal order allocation to suppliers in the automotive industry is crucial as it can significantly improve the social, environmental, and economic performance of the whole supply chain. Therefore, this research aims to find an optimal order allocation plan for suppliers using an augmented ε-constraint optimization method in the automotive industry while considering all three dimensions of sustainability. Using a previously established comprehensive framework, this paper has adapted the solution approach of an existing bi-objective order allocation model that was used to make sourcing decisions. The results showed a higher total value of sustainable purchasing is achieved by the augmented ε-constraint optimization method compared to the traditional max-min method.

关键词： Sustainable Supply Chain Supplier Selection Order Allocation

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Wearable multichannel pulse condition monitoring system based on flexible pressure sensor arrays

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Microsystems & Nanoengineering 2022年第1期8卷 249-258页

作者： Jie Wang Yirun Zhu Zhiyong Wu Yunlin Zhang Jian Lin Tao Chen Huicong Liu Fengxia Wang Lining Sun Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric EngineeringSoochow UniversitySuzhou 215123China Micro Nano System Research Center Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education and Shanxi Province&College of Information EngineeringTaiyuan University of TechnologyTaiyuan 030024China Suzhou Institute of Nano-tech and Nanobionics Chinese Academy of SciencesSuzhou 215123China

Pulse diagnosis is an irreplaceable part of traditional Chinese medical ***,application of the traditional pulse monitoring method was restricted in the modernization of Chinese medical science since it was difficult to capture real signals and integrate obscure feelings with a modern data ***,a novel multichannel pulse monitoring platform based on traditional Chinese medical science pulse theory and wearable electronics was *** pulse sensing platform simultaneously detected pulse conditions at three pulse positions(Chi,Cun,and Guan).These signals were fitted to smooth surfaces to enable 3-dimensional pulse mapping,which vividly revealed the shape of the pulse length and width and compensated for the shortcomings of traditional single-point pulse ***,the pulse sensing system could measure the pulse signals from different individuals with different conditions and distinguish the differences in pulse *** addition,this system could provide full information on the temporal and spatial dimensions of a person’s pulse waveform,which is similar to the true feelings of doctors’*** innovative,cost-effective,easily designed pulse monitoring platform based on flexible pressure sensor arrays may provide novel applications in modernization of Chinese medical science or intelligent health care.

关键词： multichannel system replace

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Real-Time Face-Tracking Control of a 4 DOF Manipulator Using Deep Learning Detection

Real-Time Face-Tracking Control of a 4 DOF Manipulator Using...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Ali Asadi Amirreza Hassan Nejad S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This paper has presented the design, simulation, and real-time implementation of a four-degree-of-freedom (4-DOF) robotic system for face detection and tracking. The system has combined kinematic control with deep learning. It has used the Xception network for high-accuracy facial detection. A Fuzzy PID-MIJ control algorithm has optimized the trajectory planning. This has ensured smooth and precise target tracking. Both simulations and physical experiments have validated the system's real-time performance. The results have shown that the system can reliably track faces with minimal error. This approach has demonstrated significant potential for dynamic, human-interactive applications.

关键词： Deep learning Target tracking Trajectory planning Target recognition Face recognition Kinematics Real-time systems Robustness Planning Face detection

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