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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Social Optimization in Noncooperative Games under Central Regulation

arXiv

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arXiv 2025年

作者： Du, Kaixin Meng, Min Li, Xiuxian Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China

This paper proposes a novel optimization problem building on noncooperative games under central regulation, which can be formulated as a bilevel structure. In the low-level, each player competes to minimize its own cost function that depends not only on the strategies of all players, but also on an intervention decision of the central regulator, while the central regulator located at the high-level attempts to achieve the social optimum, that is, to minimize the sum of cost functions of all players through an adjustable intervention decision. In this setting, under the intervention of the central regulator, the low-level players perform in a noncooperative game and aim to seek the Nash equilibrium, which indeed is related with the regulator’s decision. Meanwhile, the objective of the regulator is to choose a decision such that the social cost, i.e., the sum of cost functions of all players is minimum. This formulated bilevel social optimization problem is proven to be constrained, nonconvex and nonsmooth. To address this intricate problem, an inexact zeroth-order algorithm is developed by virtue of the smoothing techniques, allowing for the Nash equilibrium of the low-level game to be computed in an inexact manner. Levering the properties of smoothing techniques, it is rigorously shown that the devised algorithm achieves a sublinear convergence rate for computing a stationary point of a related optimization problem with a smoothed objective. Moreover, the sublinear convergence rate in the scenario where the exact equilibrium of the low-level game is available is also discussed. Finally, numerical simulations are conducted to demonstrate the efficiency of theoretical findings. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Distributed Generalized Nash Equilibria Learning for Online Stochastic Aggregative Games

arXiv

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arXiv 2025年

作者： Du, Kaixin Meng, Min Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Department of Control Science and Engineering College of Electronics and Information Engineering National Key Laboratory of Autonomous Intelligent Unmanned System Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper investigates online stochastic aggregative games subject to local set constraints and time-varying coupled inequality constraints, where each player possesses a time-varying expectation-valued cost function relying on not only its own decision variable but also an aggregation of all the players’ variables. Each player can only access its local individual cost function and constraints, necessitating partial information exchanges with neighboring players through time-varying unbalanced networks. Additionally, local cost functions and constraint functions are not prior knowledge and only revealed gradually. To learn generalized Nash equilibria of such games, a novel distributed online stochastic algorithm is devised based on push-sum and primal-dual strategies. Through rigorous analysis, high probability bounds on the regret and constraint violation are provided by appropriately selecting decreasing stepsizes. Moreover, for a time-invariant stochastic strongly monotone game, it is shown that the generated sequence by the designed algorithm converges to its variational generalized Nash equilibrium (GNE) almost surely, and the time-averaged sequence converges sublinearly with high probability. Finally, the derived theoretical results are illustrated by numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nash equilibrium

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A Hierarchical Deep Reinforcement Learning Strategy for Collective Pursuit-Evasion Game with Partial Observations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Wang, Xiaoxiao Yi, Peng Hong, Yiguang Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200092 China Tongji University School of Electronics and Information Shanghai200070 China Tongji University Control Science and Engineering Department Shanghai200070 China National Key Laboratory of Autonomous Intelligent Unmanned Systems China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China

This paper addresses the problem of Collective Pursuit-Evasion Game (C-PEG) with partial observation, aiming to enhance cooperation among cluster pursuers, reduce the game space, and optimize the overall pursuer strategy efficiency in complex dynamic environments. Firstly, we consider a distributed communication model suitable for cluster collaboration and model the multi-agent cooperative task as a Collective Decentralized Partially Observable Markov Decision Process (C-Dec POMDP). Then we propose a Hierarchical Regularization Game Multi-agent Deep Deterministic Policy Gradient (HRG-MADDPG) algorithm, which includes High-Level Strategy (HLS) and Low-Level Strategy (LLS). The HLS takes the current observation from the pursuers’ sensors and the pursuers’ status as inputs, and outputs target allocation strategies for the coalitions and individuals to guide the LLS in executing specific pursuit tasks. The LLS is further divided into centralized training and decentralized execution phases. In the centralized training phase, the guiding strategy provided by the HLS and the introduced regularization auxiliary term are combined to promote decision-making through the fusion of favorable situations and auxiliary terms. In the decentralized execution phase, a reliable policy improvement method based on partially observable information is designed. All in all, the HRG-MADDPG algorithm provides a reliable method for collective pursuit-evasion. It is trained in a single environment and further validated in multiple different test environments. Simulation results confirm the effectiveness of the proposed method, showing the adaptability, scalability, and rapid responsiveness of the proposed framework in various practical applications. © 2020 IEEE.

关键词： Hierarchical systems

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Practical Applicability of PLA for Industrial Wireless Networks: Gaps and Directions

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IEEE Network 2025年

作者： Liang, Wei Wei, Fuyou Wang, Qi Zhan, Ming Wang, Ke Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shandong Normal University Jinan250014 China Taizhou University School of Electronics and Information Engineering Zhejiang Taizhou China

The rapid transformation of Industrial Cyber-Physical System (ICPS) has introduced significant cyber security challenges, particularly in the context of ICPS. Wireless networks are a critical component of these systems, but their open nature makes them vulnerable to security threats. Physical Layer Authentication (PLA) algorithms based on Channel State Information (CSI) are essential complements to existing security technologies in wireless networks. However, their practical application faces challenges such as restricted sampling rates and uneven sampling interval, which result in low-precision CSI, significant random phase offsets, and signal distortion. This study aims to explore the feasibility of deploying and applying existing representative algorithms on industrial wireless commercial devices. According to characteristics of authentication algorithms and the adopted authentication features, we selected five of the latest physical layer authentication algorithms including supervised, semi-supervised, and unsupervised algorithms and deployed them on commercial devices that comply with the WIA-FA standard. We conducted systematic experiments focusing on the motion parameters of devices, the CSI data sampling frequency, the interference caused by worker movement, the surface materials of the devices, and the types of attacks. This study explores the ability of these algorithms to resist attacks and environmental interference in commercial devices and industrial scenarios. Extensive experimental data were collected, and the results indicate that existing authentication algorithms still need improvement to meet the requirements of industrial applications. The experiment findings and the summarized application challenges of these algorithms provide significant guidance for future research and practical industrial implementations. © 1986-2012 IEEE.

关键词： Authentication

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Aero-Thermal Radiation Effects Simulation Technique for Star Sensor Imaging under Hypersonic Conditions

引用

IEEE Sensors Journal 2025年

作者： Liu, Haonan Sun, Ting Yang, Kang Xing, Fei Wang, Xi Wu, Siyao Peng, Yicheng Tian, Ye Beijing Information Science and Technology University College of Instrument Science and Opto-electronics Engineering Laboratory of Intelligent Microsystems Beijing100192 China Tsinghua University Department of Precision Instrument Beijing100084 China Beijing Institute of Control and Electronic Technology Beijing100038 China

In order to investigate the effect of aero-thermal radiation on star sensor detection capability in near-space hypersonic flight platforms, this paper proposes a star sensor simulation imaging technique that couples aerodynamic flow field thermal radiation with target star signals. First, a regional conical imaging sampling model is constructed to preprocess the flow field data, significantly improving computational efficiency. Second, a radiation transfer equation for non-equilibrium flow fields is introduced to establish an energy transfer model based on the star sensor’s optical link. Finally, the detection performance of the star sensor under different flight conditions is quantitatively analyzed, and some simulation results are compared with flight test data for validation. The results indicate that the simulation model deviates by less than 30%. The method proposed in this paper provides important data and theoretical support for star sensor navigation accuracy analysis and extreme condition assessment. © 2001-2012 IEEE.

关键词： Radiation damage

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AI Enabled Automatic Mobile Robot intelligent Navigation in Construction with Obstacle Awareness

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IEEE Transactions on Automation Science and engineering 2025年

作者： Zhang, Yinlong Liu, Yuanhao Cui, Yunge Zeng, Ziming Liang, Wei Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing101408 China Zeekr Group Zeekr Intelligent Driving Department Shanghai110078 China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen518055 China

In the construction industry, the integration of artificial intelligence (AI) and robotics has led to significant advancements in automating various tasks. One critical aspect is the intelligent navigation of automatic mobile robot (AMR) within construction sites, where dynamic environments pose challenges such as inaccurate robot state estimation, irregular and textureless obstacle detection. To solve these issues, this paper designs an AI enabled obstacle-aware AMR intelligent navigation approach while ensuring robot safety and efficiency. Specifically, the robot is equipped with the complementary RGB camera, inertial measurement unit (IMU) and wheel encoder to estimate the states (i.e., positions, orientations and velocities), which have been tightly fused in the optimization framework. Furthermore, the RGB-Depth images are jointly fed into the AI model. It combines of Mask-RCNN and multi-scale attention network, to detect and segment the obstacles, and estimate the corresponding relative depth. It should be noted that the system incorporates obstacle awareness mechanisms to dynamically switch the robot's velocities in response to obstacles in the environment, ensuring smooth and collision-free movement. Experimental results demonstrate the effectiveness and robustness of the proposed AI-enabled navigation system in real-world construction scenarios, showcasing its potential to enhance productivity and safety in construction automation. © 2004-2012 IEEE.

关键词： Construction industry

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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

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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Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular robotics

arXiv

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arXiv 2025年

作者： Yao, Tianliang Ban, Madaoji Lu, Bo Pei, Zhiqiang Qi, Peng Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electrical and Electronic Engineering Faculty of Engineering The University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Jiangsu Suzhou China School of Oriental Pan-Vascular Devices Innovation College University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes. © 2025, CC BY.

关键词： Reinforcement learning

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