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IEEE International Conference on robotics and Automation (ICRA)

作者： Qiao Wang Dikai Liu Marc G. Carmichael Chin-Teng Lin Faculty of Engineering and Information Technology Robotics Institute University of Technology Sydney Ultimo NSW Australia Faculty of Engineering and Information Technology Australian Artificial Intelligence Institute School of Computer Science University of Technology Sydney Ultimo NSW Australia

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

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Platform Urbanism for Resident Safety: A Real-Time Predictive Microclimate Risk Monitoring and Alert System

SSRN

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SSRN 2025年

作者： Hossain, Sk Tahsin Yigitcanlar, Tan Nguyen, Kien Xu, Yue City 4.0 Lab School of Architecture and Built Environment Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia School of Electrical Engineering and Robotics Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia School of Computer Science Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia

Urban microclimates significantly impact public health, liveability, and emergency preparedness, yet traditional weather alert systems often rely on centralised data that fails to capture the spatial variability of conditions within cities. This study presents an innovative platform that integrates ArcGIS GeoEvent Server with AIoT-driven predictive analytics to deliver hyper-localised weather alerts. The platform’s adaptable design enables the incorporation of location-specific AI/ML-based predictive models and context-aware notifications, tailored to at-risk populations based on their location, health conditions, and disabilities. Featuring three GeoEvent Services and two ArcGIS Notebook Schedules, the system facilitates real-time data ingestion, processing, predictive modelling, and dissemination. A case study in Brisbane, Australia, demonstrates its capacity to address microclimatic variability, revealing significant differences in thermal stress and precipitation patterns across suburbs. Unlike traditional city-wide alerts, the platform provides targeted notifications prioritising vulnerable groups such as children, the elderly, and individuals with chronic conditions or disabilities. This approach enhances urban resilience and offers a scalable framework for applications in health monitoring, emergency response, and public safety. © 2025, The Authors. All rights reserved.

关键词： Emergency services

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Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

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IEEE International Conference on robotics and Biomimetics

作者： Hang Zhou Haichao Liu Hongliang Lu Jun Ma Yiding Ji Robotics and Autonomous Systems Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering School of Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Intelligent Transportation Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark.

关键词： Training Trajectory planning Artificial neural networks Benchmark testing System recovery Safety Planning Autonomous vehicles Robots Predictive control

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Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures

arXiv

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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Modeling and Risk Analysis of Cooperative Adaptive Cruise Control Systems Based on Petri Nets and Distributed Edge Intelligence

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IEEE Internet of Things Journal 2025年第12期12卷 19995-20006页

作者： Yu, Wangyang Cheng, Yumeng Fang, Xianwen Zhai, Xiaojun Wang, Yinglong Jing, Hongyuan Shaanxi Normal University Chang'an District Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China Shaanxi Normal University School of Computer Science Chang'an District Xi'an710119 China Anhui University of Science and Technology Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui232001 China University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Beijing Union University College of Robotics Beijing100101 China

Fueled by advancements in intelligent transportation systems, the Internet of Vehicles (IoV) seeks to connect smart vehicles, road infrastructure, and users into a unified network, enhancing traffic efficiency and reducing accident risks. Centralized cloud data collection raises concerns about privacy and communication overhead. To address these, Distributed Edge Intelligence (DEI) reduces transmission costs and improves privacy by implementing machine learning at the network edge. In this context, Cooperative Adaptive Cruise Control (CACC) systems, combined with DEI in the IoV framework, enhance transportation system intelligence through real-time data processing and decentralized decision-making. This paper proposes a modeling and analysis method for CACC systems based on Petri nets. The datasets are automatically generated using tools developed by our team, and machine learning methods are utilized to perform risk prediction analysis on the CACC model. From the perspective of Petri nets synchronization, we propose risk mitigation strategies from a design standpoint. The research results show that the proposed method significantly reduces signal accumulation and enhances synchronization in CACC systems. This improvement provides new theoretical support and technical guidance for the design and implementation of CACC systems, ultimately enhancing their safety and reliability. © 2025 IEEE.

关键词： Adaptive cruise control

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Grid Impedance Adaptive VSG Control Based on Accurate Small-Signal Modelling

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IEEE Transactions on Industry Applications 2025年

作者： Wang, Minyang Yang, Yuxin Zhang, Yuchen Ambia, Mir Nahidual Ahmed, Al-Durra Zhang, Daming Meng, Ke Zhang, Ge University of New South Wales School of Elec Eng and Telco Faculty of Engineering NSW Australia Queensland University of Technology School of Electrical Engineering and Robotics Brisbane Australia University of Sydney Sydney Australia Principal Grid Connection Engineer at Nordex Oceania Australia Khalifa University Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates ElectraNet Pty Ltd Adelaide Australia University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia

The wide-spread integration of renewable energy has increased inverter-based generation, weakening grid strength and inertia, bringing challenges to power system stability. To address these issues, virtual synchronous generator (VSG) control has emerged as a modern grid-forming (GFM) technology that can autonomously provide active and reactive power to support grid frequency and voltage. However, when a VSG inverter operates under strong grid conditions, power oscillations may occur under a fixed parameter setting, making the grid difficult to reach a steady state. Under such circumstances, traditional stability analysis based on the rate of change of frequency (RoCoF) is insufficient, advanced analysis methods based on the changes in system impedance and further improvements are required. This paper proposed an accurate small-signal model of the VSG inverter along with an impedance identification method for grid impedance. Additionally, an improved VSG control adaptive to grid impedance is proposed based on the knowledge of small-signal model and system impedance. All proposed methods have been tested on a single-machine-infinite-bus (SMIB) system and the stability results demonstrate their effectiveness under both weak and strong grid scenarios. © 1972-2012 IEEE.

关键词： Synchronous generators

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Learning Optimal "Pigovian Tax" in Sequential Social Dilemmas

arXiv

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arXiv 2023年

作者： Hua, Yun Gao, Shang Li, Wenhao Jin, Bo Wang, Xiangfeng Zha, Hongyuan School of Computer Science and Technology East China Normal University Shanghai200092 China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen China School of Software Engineering Tongji University Shanghai201804 China School of Computer Science and Technology East China Normal University Shanghai200062 China

In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how others perceive them, resulting in selfish behaviors that undermine global performance. The externality theory, defined as "the activities of one economic actor affect the activities of another in ways that are not reflected in market transactions," is applicable to analyze the social dilemmas in MARL. One of its most profound non-market solutions, "Pigovian Tax", which internalizes externalities by taxing those who create negative externalities and subsidizing those who create positive externalities, could aid in developing a mechanism to resolve MARL's social dilemmas. The purpose of this paper is to apply externality theory to analyze social dilemmas in MARL. To internalize the externalities in MARL, the Learning Optimal Pigovian Tax method (LOPT), is proposed, where an additional agent is introduced to learn the tax/allowance allocation policy so as to approximate the optimal "Pigovian Tax" which accurately reflects the externalities for all agents. Furthermore, a reward shaping mechanism based on the approximated optimal "Pigovian Tax" is applied to reduce the social cost of each agent and tries to alleviate the social dilemmas. Compared with existing state-of-the-art methods, the proposed LOPT leads to higher collective social welfare in both the Escape Room and the Cleanup environments, which shows the superiority of our method in solving social dilemmas. Copyright © 2023, The Authors. All rights reserved.

关键词： Taxation

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Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

arXiv

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arXiv 2025年

作者： Chen, Qiguang Qin, Libo Liu, Jinhao Peng, Dengyun Guan, Jiannan Wang, Peng Hu, Mengkang Zhou, Yuhang Gao, Te Che, Wanxiang LARG China Research Center for Social Computing and Interactive Robotics China Harbin Institute of Technology China School of Computer Science and Engineering Central South University China The University of Hong Kong Hong Kong Fudan University China

Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like "overthinking" and "test-time scaling." This survey seeks to fill this gap by offering a unified perspective on Long CoT. (1) We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms. (2) Next, we explore the key characteristics of Long CoT: deep reasoning, extensive exploration, and feasible reflection, which enable models to handle more complex tasks and produce more efficient, coherent outcomes compared to the shallower Short CoT. (3) We then investigate key phenomena such as the emergence of Long CoT with these characteristics, including overthinking, and test-time scaling, offering insights into how these processes manifest in practice. (4) Finally, we identify significant research gaps and highlight promising future directions, including the integration of multi-modal reasoning, efficiency improvements, and enhanced knowledge frameworks. By providing a structured overview, this survey aims to inspire future research and further the development of logical reasoning in artificial intelligence1 © 2025, CC BY-SA.

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Fuzzy-Based Automatic Epileptic Seizure Detection Framework

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computers, Materials & Continua 2022年第3期70卷 5601-5630页

作者： Aayesha Muhammad Bilal Qureshi Muhammad Afzaal Muhammad Shuaib Qureshi Jeonghwan Gwak Shaheed Zulfikar Ali Bhutto Institute of Science and Technology IslamabadPakistan Department of Computer Science&IT University of Lakki MarwatKPKPakistan Stockholm University StockholmSweden Department of Computer Science School of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical *** the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-ictal *** accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.

关键词： Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization

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