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Formation control and path planning of multi-robot systems via large language models

science China(Information sciences) 2025年第5期68卷 72-88页

作者： Dong XUE Xuanjie ZHOU Ming WANG Fangzhou LIU Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology Research Institute of Intelligent Control and Systems School of Astronautics Harbin Institute of Technology

Existing path planning and coordination control methods for multi-robot systems(MRS) typically rely on predefined rules and rudimentary algorithms. However, these methods often struggle to adapt flexibly to complex environments and to adjust motion targets appropriately. To address this challenge, this study presents a large language model(LLM)-assisted framework. By integrating textual descriptions of complex motion constraints, robot information, and local environmental data as inputs, LLMs generate motion objectives and translate them into executable control commands for the robots, thereby achieving coordinated control and path planning. This framework facilitates the generation, maintenance, and reshaping of formations in MRSs during path planning, applicable to both obstacle-free and obstacle-avoidance environments. Simulation results demonstrate that LLM-based control strategies enhance the autonomy, adaptability, flexibility, and robustness of MRS by processing complex information, making intelligent decisions, adapting to environmental changes, and handling disturbances and uncertainties.

关键词： multi-robot systems formation control path planning obstacle avoidance large language models

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Calibration of the Solar X-ray Detector on-board the Macao science Satellite-1B for Soft X-ray Detection

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science China Earth sciences 2025年第1期68卷 11-26页

作者： Man-Hei NG Xiaoping ZHANG Jianwu CHEN Yongqiang SHI Fuchang ZUO Wenxi PENG Weichun FU Liansheng LI Pengwei LUO Chi-Long TANG Guozheng ZHANG Zhiwu MEI Li WANG Cunhui LI Jiaming OU Yanyan Xu State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao999078China Space Optoelectronic Measurement and Perception Laboratory Beijing Institute of Control EngineeringBeijing100190China Institute of High Energy Physics Chinese Academy of SciencesBeijing100049China Science and Technology on Vacuum Technology and Physics Laboratory Lanzhou Institute of PhysicsLanzhou730000China China National Space Administration(CNSA) Macao Center for Space Exploration and ScienceMacao999078China

The Solar X-ray Detector(SXD)on-board the Macao science Satellite-1B(MSS-1B)was successfully launched via the Chinese Long March-2C rocket on 21 May 2023,and commenced operations in early June of the same *** MSS-1B/Soft X-ray Detection Units(SXDUs)employ two silicon drift detectors(SDDs),providing a wide range of energy spectra spanning from 0.7 to 24 ***,the SXDUs deliver a high-resolution capability of 0.14 keV@5.9 keV and operate with a time cadence of 1 ***,we perform thorough calibrations of the MSS-1B/SXDUs,employing a combination of ground experiments and *** addition,quantitative analysis comparing the flux measurements obtained by the MSS-1B/SXDUs to the data collected by the Geostationary Operational Environmental Satellite(GOES),provides compelling evidence of their ***,the preliminary spectral analysis results showcase the robustness and expected performance of the MSS-1B/SXDUs,unlocking their potential for facilitating the study of dynamic evolution of solar ***,the innovative MSS-1B/Solar X-ray Detector facilitates concurrent observations of solar soft and hard X-rays,thereby making valuable contributions to the advancements in solar research.

关键词： Macao science Satellite-1B(MSS-1B) Calibration Solar X-Ray Detector Spectrometer Soft X-rays Solar flares Solar abundances

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Fully Distributed Model-Free Adaptive Sliding Mode control for MASs With Hybrid-Attacked Topology

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IEEE Transactions on Automation science and Engineering 2025年 22卷 12336-12346页

作者： Duan, Shitao Chen, Guangdeng Zhou, Qi Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. © 2004-2012 IEEE.

关键词： Network topology

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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Combine deep learning and Bayesian analysis to separate overlapping gravitational wave signals

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science China(Physics,Mechanics & Astronomy) 2025年第5期Mechanics & Astronomy) .卷 209-223页

作者： Cunliang Ma Weiguang Zhou Zhoujian Cao Mingzhen Jia Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS

Future gravitational wave(GW) observatories, such as the Einstein Telescope, are anticipated to encounter overlapping GW signals, presenting considerable obstacles to GW data processing techniques, including signal identification and parameter estimation. In this letter, we propose a scheme of combining deep learning and Bayesian analysis to disentangle overlapping GW signals. The deep learning part takes a data-driven approach that employs an encoder-separation-decoder framework which is powerful enough to extract the shape of the signal even when the GW signals completely align. The Bayesian analysis part takes the matched filtering technique to extract the amplitude of the GW signals. Our scheme can facilitate the utilization of existing GW detection and parameter estimation methods for future instances of overlapping strain. This methodology effectively reduces biases in parameter estimation when handling multiple intertwined signals. Remarkably, this marks the first known instance where deep learning has been successfully utilized to disentangle overlapping GW signals.

关键词： gravitational wave separation machine learning Einstein Telescope parameter estimation

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Dynamic Regret for Distributed Online Composite Optimization

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IEEE Transactions on Automatic control 2025年第5期70卷 3056-3071页

作者： Hou, Ruijie Yu, Yang Li, Xiuxian Tongji University Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai201800 China Tongji University 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 Institute of Intelligent Science and Technology Shanghai201210 China

This article focuses on online composite optimization over multiagent networks. In the distributed setting, each agent has its own local loss function, which consists of a convex, strongly convex or strongly convex and smooth function, and a time-varying nonsmooth regularizer. Two distributed online algorithms are proposed and corresponding dynamic regrets are analyzed. Two proposed algorithms are based on signs of relative states. The first algorithm obtains (Formula presented) dynamic regret bound when each local loss is a general convex composite function, where CT is the path variation. If CT can be estimated in advance for convex or strongly convex local loss with a time-varying nonsmooth regularizer, then dynamic regret bounds are, respectively, in the order of (Formula presented) and o (log T(1 + CT)). The second algorithm is based on the first one, especially for handling the local loss composed of a strongly convex and smooth function with a nonsmooth regularizer, and then obtains o (1 + CT) dynamic regret bound. In the end, numerical results are given to support the theoretical findings. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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RIS Enabled SCLAM: An Approach for Simultaneous Radio Communication, Localization, and Mapping

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IEEE Internet of Things Journal 2025年第10期12卷 14663-14676页

作者： Zhao, Jinqiu Bai, Zhiquan Guo, Shuaishuai Ma, Dejie Li, Na Kwak, Kyung Sup Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Information Science and Engineering Qingdao266237 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Control Science and Engineering Jinan250061 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan250200 China INHA University Graduate School of Information Technology and Telecommunications Incheon22212 Korea Republic of

Radio-based simultaneous localization and mapping (SLAM) facilitates unmanned systems to fulfill self-localization and navigation in complex environments. However, the existing studies overlooked communication between devices, which may lead to low efficiency and high costs when performing SLAM tasks. Reconfigurable intelligent surface (RIS) can satisfy the growing demands of users and improve SLAM accuracy in dynamic and complex environments. This paper proposes a RIS enabled simultaneous radio communication, localization, and mapping (SCLAM) system, where an unmanned aerial vehicle (UAV) can perform concurrent communication and SLAM with the help of RIS by utilizing communication signals from base station (BS). The proposed method enables the UAV to simultaneously accomplish SLAM and ensure communication with the BS, while also enhancing the spectrum utilization efficiency. We derive the Bayesian Fisher information matrix (BFIM) for joint SLAM and symbol detection of the proposed system, in which the trade-off between the BFIM of SLAM and the upper bound of the ergodic mutual information is illustrated. Then, a weighted factor based BFIM is presented to further achieve a performance trade-off between SLAM and communication. We formulate an optimization problem of joint BS active beamforming and RIS passive beamforming to maximize the log determinant of weighted BFIM. Numerical results verify the superiority of the proposed SCLAM system on position error bound (PEB), mapping error bound (MEB), and spectral efficiency (SE). The performance trade-off between communication and SLAM is also discussed and explored. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Kolmogorov-Arnold Networks for Enhancing Gravitational Wave Glitch Classification Performance 24

Kolmogorov-Arnold Networks for Enhancing Gravitational Wave ...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Cai, Yiting Ma, Cunliang Xiao, Wanhai Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Detecting gravitational waves is essential for the success of the LIGO and Virgo experiments, requiring thorough comprehension of how these instruments are affected by environmental and instrumental noise. In particular, the data recorded by the LIGO/Virgo detectors frequently include transient noise events, referred to as"glitches". These glitches can interfere with real gravitational wave signals and, in some cases, their characteristics may resemble those of actual gravitational wave signals, leading to misidentification. Identifying and differentiating these glitches plays a vital role in improving the precision and dependability of gravitational wave observations. LIGO’s initial observation of gravitational waves marked a transformative achievement in expanding our comprehension of the universe. However, because of the detectors’ exceptional sensitivity, they also pick up many other noise sources that can interfere with the observation of gravitational waves. To address this challenge, numerous classification methods have been proposed;however, as model complexity increases, so does the risk of overfitting, requiring more computational resources and resulting in significantly longer training and inference times. The Kolmogorov-Arnold Network (KAN), with its unique high-dimensional mapping and powerful feature extraction capabilities, demonstrates greater robustness and accuracy than traditional methods, thus improving classification performance. In this paper, we explore multi-view classification of gravitational wave glitches using KAN networks, where the linear and convolutional layers of conventional neural networks are replaced with KAN-based structures, and residual connections are incorporated within the KAN modules. © 2024 Copyright held by the owner/author(s).

关键词： Gravitational effects

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Edge Gradient Exploration Attack based on Graph Neural Networks 24

Edge Gradient Exploration Attack based on Graph Neural Netwo...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Yang, Shuxin Ge, Ran Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Graph Neural Networks (GNNs) have achieved remarkable success in various graph-structured tasks due to their powerful graph representation learning capabilities. By aggregating and transforming information from the neighboring nodes, GNNs explore the structure of the graph and the features of the nodes. However, recent theoretical and empirical analyses have revealed that GNNs are particularly vulnerable to adversarial attacks. Among these, gradient-based attacks have been widely studied for their inherent efficiency and effectiveness. Nevertheless, due to the discrete nature of graph data, gradient-based attacks often result in suboptimal outcomes. Furthermore, the portability of some previously proposed attack algorithms requires further improvement. To address these two issues, we propose a novel Multi-Network Exploratory Attack (MNEA) aimed at enhancing gradient-based graph perturbations and improving their transferability. MNEA consists of two phases: the generation phase and the reconstruction phase. In the generation phase, multiple networks are utilized to generate a variety of candidate edges, preventing the generated perturbed edges from being too targeted toward a single model, which could lead to decreased transferability. The reconstruction phase introduces multiple edges for recombination, with the objective of avoiding misleading information potentially provided by the maximum gradient. Experimental results that are conducted on three publicly available benchmark datasets demonstrate that MNEA significantly outperforms baseline methods in terms of performance. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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