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arXiv 2025年

作者： Pei, Ziqian Wang, Jintao Zhang, Pingping Shi, Zheng Yang, Guanghua Ma, Shaodan State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau Macau999078 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

In this letter, we investigate a dynamic reconfigurable distributed antenna and reflection surface (RDARS)driven secure communication system, where the working mode of the RDARS can be flexibly configured. We aim to maximize the secrecy rate by jointly designing the active beamforming vectors, reflection coefficients, and the channel-aware mode selection matrix. To address the non-convex binary and cardinality constraints introduced by dynamic mode selection, we propose an efficient alternating optimization (AO) framework that employs penalty-based fractional programming (FP) and successive convex approximation (SCA) transformations. Simulation results demonstrate the potential of RDARS in enhancing the secrecy rate and show its superiority compared to existing reflection surface-based schemes. Copyright © 2025, The Authors. All rights reserved.

关键词： Secure communication

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CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots

CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Jonas le Fevre Sejersen Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined road infrastructure. The CAMETA framework consists of three components: a path planning layer generating potential path suggestions, a multi-agent ETA prediction layer predicting the arrival times for all agents based on the paths, and lastly, a path selection layer that calculates the accumulated cost and selects the best path. The novelty of the CAMETA framework lies in the heterogeneous map representation and the heterogeneous graph neural network architecture. As a result of the proposed novel structure, CAMETA improves the generalization capability compared to the state-of-the-art methods that rely on structured road infrastructure and historical data. The simulation results demonstrate the efficiency and efficacy of the multi-agent ETA prediction layer, with a mean average percentage error improvement of 29.5% and 44% when compared to a traditional path planning method (A *) which does not consider conflicts. The performance of the CAMETA framework shows significant improvements in terms of robustness to noise and conflicts as well as determining proficient routes compared to state-of-the-art multi-agent path planners.

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Self-Organized Sensor Eggs for Decentralized Localization and Sensing on Vulcano Island – A Glimpse into Future Space Exploration with Swarms

Self-Organized Sensor Eggs for Decentralized Localization an...

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International Conference on Information Fusion

作者： Fabio Broghammer Thomas Wiedemann Siwei Zhang Armin Dammann Christian Gentner Petar Djuric Institute of Communications and Navigation German Aerospace Center (DLR) Germany Perception for Intelligent Systems Munich Institute of Robotics and Machine Intelligence TU Munich Germany Department of Electrical and Computer Engineering Stony Brook University USA

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized “sensor eggs”. These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite Systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.

关键词： Location awareness Volcanic activity Probability density function Robot sensing systems Particle filters Transceivers Sensors Timing Surface cracks Ultra wideband technology

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking

PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Micha Heiß Jakob Grimm Hansen Dengyun Li Michał Kozłowski Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This paper proposes PredictiveSLAM, a novel extension to ORB-SLAM2, which extracts features from specific regions of interest (ROI). The proposed method was designed with the risk posed both to humans and robotic systems in large-scale industrial sites in mind. The ROI are determined through an object detection network trained to detect moving human beings. The method detects and removes humans from feature extraction, predicting their potential future trajectory. This is done by omitting a specific ROI from extraction, deemed to be occluded in consecutive time steps. Two masking methods -static object and moving object trajectories - are proposed. This approach improves tracking accuracy and the performance of SLAM by removing the dynamic features from the reference for tracking and loop closures. The method is tested on data collected in a laboratory environment and compared against a state-of-the-art ground truth system. The validation data was collected from real-time experiments which aimed at simulating the typical human worker behaviours in industrial environments using an unmanned aerial vehicle (UAV). This study illustrates the advantages of the proposed method over earlier approaches, even with a highly dynamic camera setup on a UAV working in challenging environments.

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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2023 China Automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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Adaptive Robust Invariant Extended Kalman filtering for Biped Robot

Adaptive Robust Invariant Extended Kalman filtering for Bipe...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Gao, Chengzhi Xie, Ye Zhu, Shiqiang Huang, Guanyu Kong, Lingyu Xie, Anhuan Gu, Jason Zhang, Dan Shao, Jun Qian, Haofu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang University Zhejiang Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada York University North YorkM3J 1P3 Canada School of Mechanical Engineering Zhejiang University Hangzhou310007 China

ISBN: (纸本)9781665481090

The precise estimation of system states is essential for the locomotion control of biped robots to maintain balance. Currently, the estimation of system states is either based on vision data that is susceptible to the environment, or fusing data from Inertial Measurement Unit (IMU) and the kinematic calculation. Invariant Extended Kalman Filtering (IEKF) is one of the most successful fusing algorithms to estimate system state. Generally, in IEKF, the noise covariance of system state is assumed to be known. However, the noise covariance of contact point for biped is not available since the ground-contact situation normally varies and not previously known. This paper presents a new fusing algorithm-Adaptive Robust Invariant Extended Kalman Filtering (ARIEKF) to adaptively adjust the noise parameter of contact point. The proposed algorithm applied the principle of robust estimation to resist outlier effects of state, and introduced an adaptive factor for the noise covariance of state to control its outlying disturbance influences. This paper firstly completed the full state estimation of biped robot using the theory of Lie groups and invariant observer. Then, the adaptive scale factor evaluated by three-segment approach was adopted to adjust the noise covariance of contact point. Finally, both IEKF and proposed ARIEKF are applied to our biped robot-Cosmos and the accuracy of two algorithms are compared. The mean square errors of the velocity of two algorithms were evaluated using the measurements from motion capture system. Experiments demonstrated that the mean square errors of the velocity are decreased 50 percent when compared with IEKF. © 2022 IEEE.

关键词： Mean square error

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Distributed Task Assignment Method for Multiple Robots Based on Dynamic Auction Rules

Distributed Task Assignment Method for Multiple Robots Based...

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Jiajie Xu Chinyin Chen Silu Chen Qiang Liu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo China

Existing researchs on multi-robots task assignment focus on improving the task assignment efficiency without considering the task execution time and ignoring the overall task completion efficiency, which leads to its low computational efficiency in dynamic task assignment scenarios. In addition, the use of a centralized control structure requires high communication quality between robots. To address the above issues, we propose a distributed task assignment method for multiple robots based on dynamic auction rules. The method uses distributed control of robot swarms to share and dynamically update each other's task sets, employs dynamic auction rules for task bidding, adds links to adjust the task execution order, and considers the overall task completion efficiency. Finally, relevant experiments are designed, and the experimental results show that the algorithm is more efficient in terms of distribution and stability, balancing higher execution efficiency and lower motion cost.

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Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences

arXiv

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arXiv 2023年

作者： Pataki, Zador Altillawi, Mohammad Kanakis, Menelaos Pautrat, Rémi Shen, Fengyi Liu, Ziyuan Van Gool, Luc Pollefeys, Marc The Computer Vision and Geometry Lab Department of Computer Science ETH Zurich Switzerland The Computer Vision Center CVC-Barcelona The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Computer Vision Lab Department Electrical Engineering ETH Zurich Switzerland The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Center for Processing Speech and Images KU Leuven The Computer Vision Lab ETH Zurich Switzerland

Modern learning-based visual feature extraction networks perform well in intra-domain localization, however, their performance significantly declines when image pairs are captured across long-term visual domain variations, such as different seasonal and daytime variations. In this paper, our first contribution is a benchmark to investigate the performance impact of long-term variations on visual localization. We conduct a thorough analysis of the performance of current state-of-the-art feature extraction networks under various domain changes and find a significant performance gap between intra- and cross-domain localization. We investigate different methods to close this gap by improving the supervision of modern feature extractor networks. We propose a novel data-centric method, Implicit Cross-Domain Correspondences (iCDC). iCDC represents the same environment with multiple Neural Radiance Fields, each fitting the scene under individual visual domains. It utilizes the underlying 3D representations to generate accurate correspondences across different long-term visual conditions. Our proposed method enhances cross-domain localization performance, significantly reducing the performance gap. When evaluated on popular long-term localization benchmarks, our trained networks consistently outperform existing methods. This work serves as a substantial stride toward more robust visual localization pipelines for long-term deployments, and opens up research avenues in the development of long-term invariant descriptors. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Invertible Voice Conversion with Parallel Data

Invertible Voice Conversion with Parallel Data

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zexin Cai Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan PR China

This paper introduces an innovative deep learning framework for parallel voice conversion to mitigate inherent risks associated with such systems. Our approach focuses on developing an invertible model capable of countering potential spoofing threats. Specifically, we present a conversion model that allows for the retrieval of source voices, thereby facilitating the identification of the source speaker. This framework is constructed using a series of invertible modules composed of affine coupling layers to ensure the reversibility of the conversion process. We conduct comprehensive training and evaluation of the proposed framework using parallel training data. Our experimental results reveal that this approach achieves comparable performance to non-invertible systems in voice conversion tasks. Notably, the converted outputs can be seamlessly reverted to the original source inputs using the same parameters employed during the forwarding process. This advancement holds considerable promise for elevating the security and reliability of voice conversion.

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