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Safe Control of Robotic systems Under Nonparametric Uncertainty Using Gaussian Process Regression 9

Safe Control of Robotic Systems Under Nonparametric Uncertai...

引用

9th ieee international conference on Advanced Robotics and Mechatronics (ICARM)

作者： Zhang, Jiayi Liu, Yueyue Wu, Xiaoyu Fan, Qigao Jiangnan Univ Sch Internet Things Engn Inst Automat Wuxi 214122 Jiangsu Peoples R China Natl Univ Singapore Dept Biomed Engn Singapore 119077 Singapore

ISBN: (纸本)9798350385731;9798350385724

This paper addresses the safety control problem for robotic systems under nonparametric uncertainty conditions by proposing a control scheme based on Gaussian Process Regression (GPR). Initially, leveraging historical data collected online, the GPR is employed to learn nonparametric uncertainty and time-varying disturbances. Subsequently, by employing a feedback linearization based on Lyapunov theory, we can obtain the system Global Uniform Ultimate Boundedness (GUUB). Furthermore, considering safety constraints, an additional layer is introduced to the feedback controller using Control Barrier Functions (CBF). The control input is minimally adjusted based on Quadratic Programming (QP) to satisfy optimization requirements while maintaining safety. The paper establishes, in a high probability sense, the boundedness of the closed-loop system and the forward invariance of the state safety domain. The efficacy of the proposed control approach is validated through simulation on trajectory tracking and obstacle avoidance under nonparametric uncertainty. The results confirm the validity of the control approach in handling safety and performance concerns simultaneously.

关键词： Quadratic programming

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Formalising Safety Requirements for Robotic Autonomous systems in Highly Regulated Domains 32

Formalising Safety Requirements for Robotic Autonomous Syste...

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32nd ieee international Requirements engineering conference (RE)

作者： Benjumea, Diana C. Univ Manchester Dept Comp Sci Manchester England

ISBN: (纸本)9798350395129;9798350395112

This research addresses the challenge of ensuring the safe operation of Robotic Autonomous systems (RAS) in highly regulated domains through the formalisation of safety requirements, with a specific focus on the UK nuclear safety regime as a use case of our approach that serves as a feasibility study. Driven by the growing need to deploy robots for safety and efficiency in hazardous environments, we seek to develop a systematic approach to address this challenge comprehensively. The main objectives include exploring how to derive formal properties, which are practical to verify, from functional safety requirements for RAS within the context of RE. The development of a rules-based Safety system is proposed to demonstrate this approach, which aims to be compliant with safety standards and relevant good practice. The proposed framework defines safety requirements based on existing safety protocols and industry practice. This work involves eliciting and formalising requirements for the Safety system, and integrating it with an autonomous robot. Through a real-world application involving an inspection robot in the UK nuclear industry, this research aims to demonstrate the practicality of this approach. we emphasise the importance of safety assurance throughout the life cycle of RAS, from hazard analysis to requirements elicitation and beyond.

关键词： Safety Requirements Autonomous Robotic systems

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Co-Operation of a Dual-Arm Robotic Avatar Through Body Integration of Multi-Person

Co-Operation of a Dual-Arm Robotic Avatar Through Body Integ...

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2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Sato, Tsugumi Yukawa, Hikari Minamizawa, Kouta Tanaka, Yoshihiro Nagoya Institute of Technology Department of Electrical and Mechanical Engineering Nagoya466-8555 Japan Graduate School of Media Design Keio University Tokyo108-8345 Japan

ISBN: (纸本)9798350337020

Humans have spatiotemporal limitations. Robotic avatar technology enables the expansion of an individual's innate physical characteristics, physical capabilities, and perceptual and cognitive abilities. Our research group has focused on scalability and co-creativity through co-operation and collaboration between humans, and has developed a system in which two operators are physically integrated into a single robotic arm. In particular, to enable more human-like tasks, such as receiving and delivering, we extended the system to a dual-arm robotic avatar and built a system in which multiple persons can integrate their bodies, with the expectation of further task efficiency and human extension. Specifically, we investigated the workability and operator cognition of the dual-arm robotic avatar when it was operated by one, two, or three individuals. The results showed that stability improved with the three-person operation. Furthermore, it was found that co-operation among multiple persons can overcome the original individual abilities for operation. In the three-person condition, all participants obtained a higher feeling of control than the actual amount of manipulation, which has potential applications in skill transfer. The three-person condition tended to be better, validating the usefulness of multi-person body integration. © 2023 ieee.

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Safety and Comfort in Autonomous Braking system with Deep Reinforcement Learning

Safety and Comfort in Autonomous Braking System with Deep Re...

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2022 ieee international conference on systems, Man, and Cybernetics, smc 2022

作者： Fanti, Maria Pia Mangini, Agostino Marcello Martino, Daniele Olivieri, Ignazio Parisi, Fabio Popolizio, Francesco Polytechnic University of Bari Electrical and Information Engineering Bari Italy

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Safety issues related to autonomous vehicles are of great concern both in the academy and industry, identifying the braking system performance as a crucial research field. In this work, an autonomous braking system based on deep reinforcement learning is proposed, employing an intelligent agent trained in city scenarios to manage both pedestrians' safety and passengers' comfort. The agent is modelled via the deep deterministic policy gradient algorithm in a software environment and its performance is tested showing good results in maximizing both pedestrians' safety and passengers' comfort. © 2022 ieee.

关键词： Autonomous vehicles

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Cognitive Workload and Usability of Virtual Reality Simulation for Prosthesis Training

Cognitive Workload and Usability of Virtual Reality Simulati...

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2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Park, Junho Music, Austin Delgado, Daniel Berman, Joseph Dodson, Albert Liu, Yunmei Ruiz, Jaime Huang, He Kaber, David Zahabi, Maryam Texas A&m University Wm Michael Barnes '64 Department of Industrial & Systems Engineering College StationTX United States University of Florida Department of Computer & Information Science & Engineering GainesvilleFL United States North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States North Carolina State University University of North Carolina at Chapel Hill Joint Department of Biomedical Engineering RaleighNC United States University of Florida Department of Industrial and Systems Engineering GainesvilleFL United States

ISBN: (纸本)9798350337020

Amputees use prosthetic devices to perform activities of daily living. However, some users reject their devices due to the lack of usability or high cognitive workload. Although virtual reality has been studied in this domain for training purposes, there has not been any investigation on usability and cognitive workload of using virtual reality simulations for training of prosthetic devices. The objective of this study was to compare cognitive workload and usability of using virtual reality-based simulation of electromyography based prosthetic devices and physical devices. The findings suggested that using virtual reality simulations were helpful in reducing cognitive workload and increasing perceived usability of prosthetic devices. © 2023 ieee.

关键词： cognitive workload prosthesis training usability virtual reality

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Massive Variation on Gough Stewart Platform Using PID and Model Reference Adaptive Control 7

Massive Variation on Gough Stewart Platform Using PID and Mo...

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7th international conference on Green Technology and Sustainable Development, GTSD 2024

作者： Phu, Nguyen Duy Thanh, Ha Le Nhu Ngoc Ho Chi Minh City University of Technology and Education Department of Mechatronics Engineering Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9798350386073

This paper presents a controller to adapt to the massive variation, disturbances, and unknown system parameters on a Gough-Stewart platform. This mechanism refers to a parallel robot with six legs and actuators that can powered by electric cylinders, pneumatic cylinders, or hydraulic cylinders. The Gough-Stewart platform robot is characterized by a nonlinear dynamic model, featuring numerous singularities, external disturbances, and other parametric uncertainties. Thus, designing a control system becomes more complicated. In this research, kinematic and dynamic model are considered. The Proportional - Integral - Derivative (PID) controller combined with the model reference adaptive control technique (MRAC) using MIT-rule is proposed to improve the tracking control performance of the system. Results of the numerical simulation demonstrate the effectiveness of the presented control algorithm in achieving rapid response and fast adaptation of trajectory tracking, even under the influences of mass variation, disturbances, and unknown system parameters. © 2024 ieee.

关键词： Model reference adaptive control

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On the Security and Privacy Implications of Large Language Models: In-Depth Threat Analysis 17

On the Security and Privacy Implications of Large Language M...

引用

ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green Computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Ruhlander, Luis Popp, Emilian Stylidou, Maria Khan, Sajjad Svetinovic, Davor Khalifa University Center for Secure Cyber-Physical Systems Department of Computer Science Abu Dhabi United Arab Emirates Vienna University of Economics and Business Department of Information Systems and Operations Management Vienna Austria

ISBN: (纸本)9798350351637

Large Language Models (LLMs) have gained popularity since the release of ChatGPT in 2022. These systems utilize Artificial Intelligence (AI) algorithms to analyze natural language, enabling users to have sophisticated real-time conversations with them. The existing literature on LLMs is mostly focused on system design and lacks dedicated research on investigating privacy and security issues. To safeguard the interests of various stakeholders, it is crucial to understand the associated security and privacy risks of these models. Our study utilized STRIDE and LINDDUN methodologies to investigate security and privacy threats of LLMs. We presented a detailed system model of LLMs and analyzed the potential threats, vulnerabilities, security considerations, and mitigation tactics intrinsic to the design and deployment of various system components. Our comprehensive threat assessment showcases potential threats imminent to the current generation of LLMs, such as unintentional data leakage or system misuse by malicious actors. Furthermore, our study discusses the importance of proactive security measures in LLM development, deployment, and maintenance. © 2024 ieee.

关键词： Differential privacy

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Decoding Illusion Perception: A Comparative Analysis of Deep Neural Networks in the Müller-Lyer Illusion

Decoding Illusion Perception: A Comparative Analysis of Deep...

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2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Zhang, Hongtao Yoshida, Shinichi Li, Zhen Graduate School of Engineering Kochi University of Technology Kochi Japan School of Information Kochi University of Technology Kochi Japan Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen China

ISBN: (纸本)9798350337020

Visual illusions, as powerful tools for revealing the strategies and limitations of brain visual processing, have garnered attention in neural network research. This study aims to investigate the performance of various deep neural networks (DNNs) in handling the Müller-Lyer illusion task, thereby providing insights into the mechanisms of the brain's visual system in processing visual illusions. We adopted two types of visual stimuli and recruited 12 participants for a length-matching task. Based on the participants' perceptual data, we employed multiple classical neural network models to test their responses to each type of stimuli. To analyze the differences in model performance, we employed representational dissimilarity matrices (RDM) analysis and utilized Grad-CAM techniques to visualize neural network activation maps. The results revealed that some neural networks could simulate human visual illusion phenomena but exhibit differences in representational similarity and feature preferences. These findings provide a preliminary understanding of the computational principles and mechanisms of the brain's visual system and offer insights for future research in building more robust and efficient neural network models. © 2023 ieee.

关键词： computer vision deep learning neural networks optical illusion visual system

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Strategic Path Control to Balance Risks Between Competing Teams in Graph Network 20

Strategic Path Control to Balance Risks Between Competing Te...

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ieee 20th international conference on Automation Science and engineering (CASE)

作者： Aicardi, Matteo Sacile, Roberto Zero, Enrico Univ Genoa Dept Informat Bioengn Robot & Syst Engn DIBRIS I-16145 Genoa Italy

ISBN: (纸本)9798350358513;9798350358520

This paper focuses on a network framework where a team of elements endeavors to traverse from an origin to a destination, aiming to minimize the maximum risk along their path. Within this network, each link is characterized by a specific exposure to risk, with the potential for risk amplification when encountering another team of diverse elements seeking to inflict damage on the same link. The study employs the method of successive averages to optimize the path choice, emphasizing risk mitigation and control strategies. Results are demonstrated on an exemplificative network, showcasing the convergence of the chosen path through the iterative application of the method of successive averages. This approach provides valuable insights into navigating complex networks while considering dynamic risk factors, offering practical implications for system-of-systems engineering and risk-aware decision-making.

关键词： system of systems

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Towards Interpretable, Attention-Based Crime Forecasting

Towards Interpretable, Attention-Based Crime Forecasting

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2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Ma, Yujunrong Qi, Xiaowen Nakamura, Kiminori Bhattacharyya, Shuvra S. University of Maryland Department of Electrical and Computer Engineering College Park United States University of Maryland Department of Criminology and Criminal Justice College Park United States Institute for Advanced Computer Studies University of Maryland Department of Electrical and Computer Engineering College Park United States

ISBN: (纸本)9798350337020

While the use of machine learning techniques in high stake fields, such as medical diagnosis and criminal justice, has been increasing in recent years, concerns have been raised regarding the lack of transparency and interpretability of the algorithms used. In this paper, we propose the use of interpretable attention-based ConvLSTM models for crime forecasting application. This approach combines the power of ConvLSTM models in capturing spatio-temporal patterns with the interpretability of attention mechanisms, allowing for the identification of key geographic areas in the input data that contribute to the prediction. We demonstrate the effectiveness of this approach through experiments on real-world crime data, showing that our model demonstrates high accuracy in crime predictions while providing insightful visualization that enhances the interpretability of prediction results. © 2023 ieee.

关键词： ConvLSTM Interpretable machine learning Self attention

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