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OA-ECBVC: A Cooperative Collision-free Encirclement and Capture Approach in Cluttered Environments

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arXiv 2023年

作者： Wang, Xinyi Ding, Yulong Chen, Yizhou Han, Ruihua Xi, Lele Chen, Ben M. The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong The Department of Control Science and Engineering Tongji University Shanghai China The Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China Department of Computer Science The University of Hong Kong Hong Kong Hebei University of Science and Technology Shijiazhuang China

This article investigates the practical scenarios of chasing an adversarial evader in an unbounded environment with cluttered obstacles. We propose a Voronoi-based decentralized algorithm for multiple pursuers to encircle and capture the evader by reacting to collisions. An efficient approach is presented for constructing an obstacle-aware evader-centered bounded Voronoi cell (OA-ECBVC), which strictly ensures collision avoidance in various obstacle scenarios when pursuing the evader. The evader can be efficiently enclosed in a convex hull given random initial configurations. Furthermore, to cooperatively capture the evader, each pursuer continually compresses the boundary of its OA-ECBVC to quickly reduce the movement space of the evader while maintaining encirclement. Our OA-ECBVC algorithm is validated in various simulated environments with different dynamic systems of robots. Real-time performance of resisting uncertainties shows the superior reliability of our method for deployment on multiple robot platforms. Copyright © 2023, The Authors. All rights reserved.

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Extended Kalman Filter Based Modified Elman-Jordan Neural Network for control and Identification of Nonlinear Systems

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Extended Kalman Filter Based Modified Elman-Jordan Neural Ne...

2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020

作者： Sen, Gokcen Devlet Gunel, Gulay Oke Guzelkaya, Mujde Istanbul Technical University Department of Control and Automation Engineering Istanbul Turkey

ISBN: (纸本)9781728191362

In this paper, the Extended Kalman Filter (EKF) is used for online training of a recurrent neural network (RNN) model since the EKF outperforms the first order gradient-based algorithms as a second order method. The modified Elman-Jordan Neural Network model with one hidden layer is adopted as the RNN structure. Self-connections are added in context units to investigate their effects. Then, this model is utilized for identification and online control of a nonlinear single input single output (SISO) process model. The performance of the proposed structure is evaluated by simulation results. The effects of some parameters and the number of hidden units to the performance are also examined. © 2020 IEEE.

关键词： Recurrent neural networks

Learning Multi-Frequency Partial Correlation Graphs

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arXiv 2023年

作者： D’Acunto, Gabriele Di Lorenzo, Paolo Bonchi, Francesco Sardellitti, Stefania Barbarossa, Sergio The Department of Computer Control and Management Engineering Sapienza University of Rome 00185 Italy Centai Institute Turin Italy Barcelona Spain The Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome00184 Italy The Faculty of Engineering in Computer Science Universitas Mercatorum University of Italian Chambers of Commerce Rome00186 Italy

Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands. Motivated by many applications in which this differentiation is pivotal, we overcome this limitation by learning a block-sparse, frequency-dependent, partial correlation graph, in which layers correspond to different frequency bands, and partial correlations can occur over just a few layers. To this aim, we formulate and solve two nonconvex learning problems: the first has a closed-form solution and is suitable when there is prior knowledge about the number of partial correlations;the second hinges on an iterative solution based on successive convex approximation, and is effective for the general case where no prior knowledge is available. Numerical results on synthetic data show that the proposed methods outperform the current state of the art. Finally, the analysis of financial time series confirms that partial correlations exist only within a few frequency bands, underscoring how our methods enable the gaining of valuable insights that would be undetected without discriminating along the frequency domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

HiPE: Hierarchical Initialization for Pose Graphs

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arXiv 2022年

作者： Guadagnino, Tiziano Di Giammarino, Luca Grisetti, Giorgio The Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome Italy

Pose graph optimization is a non-convex optimization problem encountered in many areas of robotics perception. Its convergence to an accurate solution is conditioned by two factors: the non-linearity of the cost function in use and the initial configuration of the pose variables. In this paper, we present HiPE, a novel hierarchical algorithm for pose graph initialization. Our approach exploits a coarse-grained graph that encodes an abstract representation of the problem geometry. We construct this graph by combining maximum likelihood estimates coming from local regions of the input. By leveraging the sparsity of this representation, we can initialize the pose graph in a non-linear fashion, without computational overhead compared to existing methods. The resulting initial guess can effectively bootstrap the fine-grained optimization that is used to obtain the final solution. In addition, we perform an empirical analysis on the impact of different cost functions on the final estimate. Our experimental evaluation shows that the usage of HiPE leads to a more efficient and robust optimization process, comparing favorably with state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Cost functions

Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study

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arXiv 2023年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Jamalipour, Abbas The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The School of Electrical and Information Engineering University of Sydney Australia

With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse, and digital twin, due to their outstanding ability to represent complex patterns and generate plausible samples. In this article, we explore the applications of DGMs in a crucial task, i.e., improving the efficiency of wireless network management. Specifically, we firstly overview the generative AI, as well as three representative DGMs. Then, a DGM-empowered framework for wireless network management is proposed, in which we elaborate the issues of the conventional network management approaches, why DGMs can address them efficiently, and the step-by-step workflow for applying DGMs in managing wireless networks. Moreover, we conduct a case study on network economics, using the state-of-the-art DGM model, i.e., diffusion model, to generate effective contracts for incentivizing the mobile AI-Generated Content (AIGC) services. Last but not least, we discuss important open directions for the further research. © 2023, CC BY.

关键词： Wireless networks

Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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Science China(Information Sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

A data-driven fault diagnosis method for photovoltaic modules 8

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A data-driven fault diagnosis method for photovoltaic module...

8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Jiurong, Yang Xingjian, Sun Zhuoran, Ma Xiaojuan, Han China Electric Power Research Institute Co. Ltd Safety and Quality Department Beijing China North China Electric Power University School of Control and Computer Engineering Beijing China State Grid Jibei Electric Power Co. Ltd Intelligent Distribution Network Center China

ISBN: (纸本)9798350328813

The existing fault diagnosis of photovoltaic modules mainly focuses on a single fault, while in actual operation, the impact of climate and environmental conditions often leads to multiple faults occurring simultaneously. In response to the above issues, a data-driven composite fault diagnosis method for photovoltaic modules is proposed in this paper. The simulation model of Photovoltaic system is built, and the fault characteristic parameters of photovoltaic modules under different states are extracted according to the output characteristic curves under the three states of normal, single fault and composite fault. The composite fault diagnosis model of photovoltaic modules based on hybrid kernel Extreme learning machine is established. The parameters of hybrid kernel Extreme learning machine are optimized by using adaptive strategy to improve sparrow search algorithm, The accuracy of diagnosis model of hybrid kernel Extreme learning machine is improved. The effectiveness of the proposed method is verified through a simulation example of the actual operation data of a photovoltaic power plant. The simulation results show that this method can accurately diagnose single and composite faults of photovoltaic modules, and has certain engineering application prospects. © 2023 IEEE.

关键词： data driven fault diagnosis photovoltaic module sparrow search algorithm

3DOS: Towards 3D Open Set Learning – Benchmarking and Understanding Semantic Novelty Detection on Point Clouds

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arXiv 2022年

作者： Alliegro, Antonio Borlino, Francesco Cappio Tommasi, Tatiana Department of Control and Computer Engineering Politecnico di Torino Italy Italian Institute of Technology Italy

In recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies focus on canonical closed-set conditions, neglecting the intrinsic open nature of the real-world. This limits the abilities of robots and autonomous systems involved in safety-critical applications that require managing novel and unknown signals. In this context exploiting 3D data can be a valuable asset since it provides rich information about the geometry of perceived objects and scenes. With this paper we provide the first broad study on 3D Open Set learning. We introduce 3DOS: a novel testbed for semantic novelty detection that considers several settings with increasing difficulties in terms of semantic (category) shift, and covers both in-domain (synthetic-to-synthetic, real-to-real) and cross-domain (synthetic-to-real) scenarios. Moreover, we investigate the related 2D Open Set literature to understand if and how its recent improvements are effective on 3D data. Our extensive benchmark positions several algorithms in the same coherent picture, revealing their strengths and limitations. The results of our analysis may serve as a reliable foothold for future tailored 3D Open Set methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics