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SSRN 2023年

作者： Berx, Nicole Decré, Wilm Pintelon, Liliane Dept. Mechanical Engineering Centre for Industrial Management/Traffic and Infrastructure KU Leuven Leuven3001 Belgium Dept. Mechanical Engineering Division Robotics Automation and Mechatronics KU Leuven Leuven3001 Belgium Core Lab M&A Flanders Make@KU Leuven Heverlee3001 Belgium

The emergence of collaborative robots (cobots) has transformed the interaction between humans and robots in industrial workspaces. While cobots offer significant advantages in productivity and flexibility, their unique interaction model with humans introduces additional safety considerations. Moreover, the adoption of cobots remains limited, partly due to a lack of awareness and knowledge about their safety. To address these challenges, the Cobot Safety Readiness Assessment Tool (CSRAT) evaluates cobot safety readiness, the combination of awareness and knowledge, from a system-wide perspective. The CSRAT is grounded in maturity grid models evaluating the readiness of technological and other safety risk factors such as operator trust, privacy, and training. It allows practitioners to self-assess their cobot safety readiness and perceived importance of risk factors. Additionally, the tool aims to facilitate alignment among internal stakeholders on cobot safety. The CSRAT prototype underwent iterative development and validation by experts from academia and industry. Empirical validation results highlight the tool's usefulness, effectiveness, and usability. It successfully enhances users' awareness and knowledge of safety risk factors and serves as a conversation starter among internal *** industry practitioners, the CSRAT offers practical relevance in enhancing safety readiness, guiding decision-making, and addressing overlooked risk factors. Academically, this research contributes to the fields of human-robot interaction, safety evaluation, usability research, and cobot adoption. In conclusion, this research provides a comprehensive approach to evaluating cobot safety readiness and promoting informed decision-making for safer human-robot collaborations. While acknowledging CSRAT's value and limitations, the paper also points towards potential future research directions. © 2023, The Authors. All rights reserved.

关键词： Industry 4.0

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Safety-Critical Optimal Control for Robotic Manipulators in A Cluttered Environment

arXiv

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arXiv 2022年

作者： Ding, Xuda Wang, Han Ren, Yi Zheng, Yu Chen, Cailian He, Jianping The Dept. of Automation Shanghai Jiao Tong University Shanghai China The Dept. of Engineering Science University of Oxford Oxford United Kingdom The Tencent Robotics X Lab Shenzhen China

Designing safety-critical control for robotic manipulators is challenging, especially in a cluttered environment. First, the actual trajectory of a manipulator might deviate from the planned one due to the complex collision environments and non-trivial dynamics, leading to collision;Second, the feasible space for the manipulator is hard to obtain since the explicit distance functions between collision meshes are unknown. By analyzing the relationship between the safe set and the controlled invariant set, this paper proposes a data-driven control barrier function (CBF) construction method, which extracts CBF from distance samples. Specifically, the CBF guarantees the controlled invariant property for considering the system dynamics. The data-driven method samples the distance function and determines the safe set. Then, the CBF is synthesized based on the safe set by a scenario-based sum-of-square (SOS) program. Unlike most existing linearization-based approaches, our method reserves the volume of the feasible space for planning without approximation, which helps find a solution in a cluttered environment. The control law is obtained by solving a CBF-based quadratic program in real time, which works as a safe filter for the desired planning-based controller. Moreover, our method guarantees safety with the proven probabilistic result. Our method is validated on a 7-DOF manipulator in both real and virtual cluttered environments. The experiments show that the manipulator is able to execute tasks where the clearance between obstacles is in millimeters. © 2022, CC BY-NC-SA.

关键词： Quadratic programming

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Semiconductor Manufacturing Industry: Assessment, Challenges, and Future Trends 2

Semiconductor Manufacturing Industry: Assessment, Challenges...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Musa, Yasmin Tantawi, Khalid Mikhail, Maged Ma, Jeffrey Alharthi, Dalal Flatt, Larry Potter, Lyn Motlow State Community College Department of Career Readiness SmyrnaTN United States Department of Engineering Management and Technology University of Tennessee at Chattanooga TN United States Purdue University Northwest Hammond Department of Engineering Technology IN United States Saint Louis University Saint Louis Department of Mechanical Engineering MO United States College of Applied Science and Technology University of Arizona Tuscan AZ United States Robotics and Automation Center Motlow State Community College McMinnvilleTN United States Chattanooga State Community College Chattanooga State Community College Dept. of Engineering Systems Tech. ChattanoogaTN United States

ISBN: (纸本)9798331506995

In this work we evaluate the state of the semiconductor manufacturing industry and its challenges and trends. Future trends in the industry are analyzed from three perspectives: the evolution of Industry 4.0, the advances in semiconductor materials, and the impact of the Covid-19 Pandemic. The semiconductor manufacturing industry witnessed an acute decline in the United States and other regions in the two decades prior to the CoVid-19 pandemic. The decline was only uncovered after the chip shortage of 2021 that resulted from the severe supply chain disruption. Trends in the industry were analyzed from three perspectives: Industry 4.0, advances in materials, and the Post-pandemic era. As a result of the evolution of the fourth generation of industry (Industry 4.0), trends in semiconductor manufacturing include robotization, which caused the industry to become the largest market for industrial robotics since 2020, and an all-time peak globalization. The semiconductor industry is a very globalized industry with corporates from different parts of the world taking part in the production of the final product. Although some materials such as carbon and Gallium Nitride show promising trends to replace silicon as the material of choice. It will likely not be before two or three decades when a semiconductor material will be able to replace silicon. Challenges for the industry include the shortage of the trained-workforce, and the added inter-country restrictions that may hinder the globalization of the industry. © 2025 IEEE.

关键词： Semiconductor alloys

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Design and Mechatronics of a Neurosurgical Robot for Tumor Ablation under MRI

Automation, Robotics and Communications for Industry 4.0/5.0

引用

automation, robotics and Communications for Industry 4.0/5.0 2023年 2023卷 75-76页

作者： Ding, Qingpeng Yan, Wanquan Chen, Jianghua Yan, Kim Lam, Chun Ping Cheng, Shing Shin Dept. of Mechanical and Automation Engineering The Chinese University of Hong Kong Sha Tin Hong Kong Multi-Scale Medical Robotics Center Hong Kong Science Park N.T Hong Kong

Straight rigid instruments are still dominant tools used in most existing surgical cases in the human brain. The lack of distal dexterity and compatibility with the magnetic resonance imaging (MRI) limit the usage of these instruments in image-guided minimally invasive surgeries. Continuum robots built with MRI compatibility can potentially lead to wide adoption of surgical robots in intraoperative procedures. This paper introduces a 2-DoF 3D-printed continuum end effector and modular shape memory alloy (SMA) actuators for real-time tip motion in 3D workspace. The robot takes advantage of modular design to decouple linkage from the actuators to the end effector. This modular architecture facilitates establishment of a sterile interface and maintenance of the entire robotic system. Design of the robot and the control system are described, and the performance under MRI is evaluated experimentally. © 2023, International Frequency Sensor Association (IFSA). All rights reserved.

关键词： End effectors

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DROPO: Sim-to-Real Transfer with Offline Domain Randomization

arXiv

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arXiv 2022年

作者： Tiboni, Gabriele Arndt, Karol Kyrki, Ville Dept. of Control and Computer Engineering Politecnico di Torino Francesco Ferrucci Street 112 Torino10141 Italy Intelligent Robotics Group Dept. of Electrical Engineering and Automation Aalto University Maarintie 8 Espoo02150 Finland

In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation;however, finding optimal randomization distributions can be difficult. In this paper, we introduce DROPO, a novel method for estimating domain randomization distributions for safe sim-to-real transfer. Unlike prior work, DROPO only requires a limited, precollected offline dataset of trajectories, and explicitly models parameter uncertainty to match real data using a likelihood-based approach. We demonstrate that DROPO is capable of recovering dynamic parameter distributions in simulation and finding a distribution capable of compensating for an unmodeled phenomenon. We also evaluate the method in two zero-shot sim-to-real transfer scenarios, showing successful domain transfer and improved performance over prior methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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A review of energy efficiency and Machine learning analysis for additive manufacturing of direct laser metal deposition

引用

Materials Today: Proceedings 2024年

作者： P. Panneer Selvam S. Prabhakaran B. Vinod T. Jishnu Dept. of Robotics and Automation Engineering PSG College of Technology Coimbatore 641004 India

Additive Manufacturing (AM) is an innovative industrial process that utilizes layering of materials to create unique products with an unprecedented level of flexibility. To maximize the benefits of AM, it is crucial to understand the precise utilization of laser energy. This review provides a concise overview of the Directed Laser Metal Deposition (DLMD) process, which includes an analysis of input parameters and the effective use of laser power, along with the impact of laser synchronous preheating processes. Various input parameters such as travel speed, hatch spacing, powder feed rate, and the standard of distance influence the calculation of volumetric energy density (VED). VED is important for Clad bed geometry, including dilution (D), height (H), width (W) of AM wall, and clad bead stability (ΔH). In the field of manufacturing, Machine Learning (ML) is a commonly used subset of Artificial Intelligence (AI) to improve the quality of processes, expedite production, and reduce costs. Specifically, in Metal Additive Manufacturing (MAM), ML techniques are leveraged to optimize various process parameters, resulting in the production of high-quality, defect-free products. This review provides a detailed explanation of the step-by-step implementation process of ML and its classification within a fundamental structure.

关键词： Direct Laser Metal Deposition Laser Synchronous Preheating Energy efficiency Machine Learning Stability And standard deviation of microhardness

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A controller for reaching and unveiling a partially occluded object of interest with an eye-in-hand robot

arXiv

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arXiv 2021年

作者： Papageorgiou, Dimitrios Koutras, Leonidas Doulgeri, Zoe The Automation & Robotics Lab Dept. of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece

In this work, a control scheme for approaching and unveiling a partially occluded object of interest is proposed. The control scheme is based only on the classified point cloud obtained by the in-hand camera attached to the robot's end effector. It is shown that the proposed controller reaches in the vicinity of the object progressively unveiling the neighborhood of each visible point of the object of interest. It can therefore potentially achieve the complete unveiling of the object. The proposed control scheme is evaluated through simulations and experiments with a UR5e robot with an in-hand RealSense camera on a mock-up vine setup for unveiling the stem of a grape. © 2021, CC BY.

关键词： Cameras

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Addressing Sample Efficiency and Model-bias in Model-based Reinforcement Learning

Addressing Sample Efficiency and Model-bias in Model-based R...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Akhil S Anand Jens Erik Kveen Fares Abu-Dakka Esten Ingar Grøtli Jan Tommy Gravdahl Dept. of Engineering Cybernetics Norwegian University of Science and Technology (NTNU) Trondheim Norway Department of Electrical Engineering and Automation (EEA) Intelligent Robotics Group Aalto University Aalto Finland Dept. of Mathematics and Cybernetics SINTEF Digital Trondheim Norway

Model-based reinforcement learning promises to be an effective way to bring reinforcement learning to real-world robotic systems by offering a sample efficient learning approach compared to model-free reinforcement learning. However, model-based reinforcement learning approaches at present struggle to match the performance of model-free ones. This work attempts to fill this gap by improving the performance of model-based reinforcement learning while further improving its sample efficiency. To improve the sample efficiency, an exploration strategy is formulated which maximizes the information gain. The asymptotic performance is improved by compensating for the model-bias using a model-free critic. We have evaluated our proposed approach on four reinforcement learning benchmarking tasks in the openAI gym framework.

关键词： Reinforcement learning Benchmark testing Data models Task analysis Robots

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OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for robotics

arXiv

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arXiv 2022年

作者： Passalis, Nikolaos Pedrazzi, S. Babuska, R. Burgard, W. Dias, D. Ferro, F. Gabbouj, M. Green, O. Iosifidis, A. Kayacan, E. Kober, J. Michel, O. Nikolaidis, N. Nousi, P. Pieters, R. Tzelepi, M. Valada, A. Tefas, A. Dept. of Informatics Aristotle University of Thessaloniki Greece Cyberbotics Switzerland Dept. of Cognitive Robotics Delft University of Technology Netherlands Dept. of Computer Science University of Freiburg Germany PAL Robotics Spain The units of Computing Sciences and Automation Technology and Mechanical Engineering Tampere University Finland Agrointelli Denmark The Department of Electrical and Computer Engineering Aarhus University Denmark

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles. Copyright © 2022, The Authors. All rights reserved.

关键词： robotics

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Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios 31

Sense-Assess-eXplain (SAX): Building Trust in Autonomous Veh...

引用

31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Gadd, Matthew De Martini, Daniele Marchegiani, Letizia Newman, Paul Kunze, Lars Oxford Robotics Institute University of Oxford Dept. Engineering Science United Kingdom Automation and Control Aalborg University Dept. Electronic Systems Denmark

This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for large-scale deployments of autonomous systems. To this end, we present how we build robots that (1) can robustly sense and interpret their environment using traditional as well as unconventional sensors;(2) can assess their own capabilities;and (3), vitally in the purpose of assurance and trust, can provide causal explanations of their interpretations and assessments. As it is essential that robots are safe and trusted, we design, develop, and demonstrate fundamental technologies in real-world applications to overcome critical barriers which impede the current deployment of robots in economically and socially important areas. Finally, we describe ongoing work in the collection of an unusual, rare, and highly valuable dataset. © 2020 IEEE.

关键词： Autonomous vehicles

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