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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Stability Analysis of Connected Vehicle Platoons with Markovian Packet Losses

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IEEE Internet of Things Journal 2025年

作者： Wu, Yilin Zhao, Xiaoyan Shen, Zhiping Liu, Yonggui Li, Zeming Guangdong University of Education School of Computer Science Guangzhou510310 China Henan Normal University School of Mathematics and Statistics Xinxiang453007 China South China University of Technology Key Laboratory of Autonomous Systems and Network Control Ministry of Education The College of Automatic Science and Engineering Guangzhou510640 China

This article focuses on discussing the effects of inter-vehicular communication on the performance of platoon control. Different from the existing results established under a critical assumption that the fading channels are independent, this paper models the inter-vehicular communication as Markovian packet loss channel which can capture the temporal interdependencies of packet losses. Our aim is to decide whether there exists a distributed controller such that the underlying vehicle platoon can achieve mean square stability (MSS) over Markovian packet loss channels. First, for the scenario with identical Markovian packet losses, we begin by deriving a necessary and sufficient condition for the platoon MSS, which is established by utilizing the stability principles of Markov jump linear systems. Additionally, we propose a numerically verifiable criterion for MSS, which can be validated through the feasibility of linear matrix inequalities (LMIs). Furthermore, based on the modified Riccati inequality (MRI), we provide an analytical sufficient condition that ensures MSS for vehicle platoons. This condition is expressed in terms of the information flow topology (IFT) and the statistical parameters of Markov channels. Second, for the case with nonidentical packet dropouts, we introduce a Markovian loss channel based switching (MLCBS) model. Building upon this model, we further propose a sufficient conditon related to IFT and the transition probability matrix, and a distributed controller that is not only easy to implement but also enables the achievement of MSS for vehicle platoons with nonidentical packet dropouts. Ultimately, numerical experiments are performed to validate the obtained outcomes. © 2014 IEEE.

关键词： Intelligent Transportation systems Resource-Constrained networks Vehicular networks

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A Recurrent Neural network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

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Barrier Offset Varying-Parameter Dynamic Learning network for Solving Dual-Arms Human-Like Behavior Generation

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Zhang, Zhijun Zhang, Mingyang Guo, Jinjia He, Haotian School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Key Library of Autonomous Systems and Network Control Ministry of Education China Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China Guangzhou510335 China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shanxi University of Technology Hanzhong723001 China School of Information Science and Engineering Changsha Normal University Changsha410100 China School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China School of Computer Science and Engineering Jishou University Jishou416000 China

Enabling robots to imitate human actions and perform tasks with high precision while avoiding potential obstacles in the environment can effectively enhance the interaction between social robots and humans. In this paper, to achieve higher precision trajectory tracking and obstacle avoidance for dual-arm humanoid robots, the barrier offset varying-parameter dynamic learning neural (BOVDL) network method is proposed and applied to dual-arm humanoid behavior generation scheme. To do so, a dual-arms humanoid robot model is set up, and transformed into a constrained time-varying quadratic programming (TVQP) problem. Secondly, by using lagrangian multiplier method and Karush-Kuhn-Tuchker condition, the inequality constrained TVQP is converted as a time-varying equation with a barrier parameter. Thirdly, a varying-parameter dynamic learning network is presented to solve the time-varying equation with a barrier parameter. Computer simulation experiments are conducted to verify the feasibility, accuracy, and safety of the proposed BOVDL network method. Experimental results show that all 14 joints of the humanoid robot's arms are within the motion range of each real human arm's physical constraints. The maximum position error and velocity error between the desired trajectory and the actual trajectory of the end effector are less than 10-6m magnitude and 10-7m magnitude, respectively, representing a reduction of 5 orders of magnitude compared to the traditional varying-parameter convergent-differential neural network. Furthermore, the proposed method also enables the dual-arm humanoid robot to avoid collisions with obstacles while performing tasks, demonstrating the superiority of the proposed BOVDL network scheme. © 2016 IEEE.

关键词： Anthropomorphic robots

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Node coordination mechanism based on distributed estimation and control in wireless sensor and actuator networks

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控制理论与应用（英文版） 2013年第4期11卷 570-578页

作者： Lei MO Bugong XU Key Laboratory of Autonomous Systems and Network Control Ministry of EducationCollege of Automation Science and EngineeringSouth China University of Technology

Wireless sensor and actuator networks （WSANs） have a wide range of applications. To perform effective sensing and acting tasks, multiple coordination mechanisms among the nodes are required. As attempt in this direction, this paper describes collaborative estimation and control algorithms design for WSANs. First, a sensor-actuator coordination model is proposed based on distributed Kalman filter in federated configuration. This method provides the capability of fault tolerance and multi-source information fusion. On this basis, an actuator-actuator coordination model based on even-driven task allocation is introduced. Actuators utilize fused sensory information to adjust their action that incur the minimum energy cost to the system subject to the constraints that user＇s preferences regarding the states of the system are approximately satisfied. Finally, according to system requirements, a distributed algorithm is proposed to solve the task allocation problem. Simulations demonstrate the effectiveness of our proposed methods.

关键词： Wireless sensor and actuator networks Federated Kalman filter Task allocation Distributed control

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Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal autonomous System

Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Auton...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Da Conceicao, Ariano Tian, Lianfang Du, Qiliang Yuan, Ling School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Research Institute of Modern Industrial Innovation South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education Guangzhou China Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou511363 China

ISBN: (纸本)9798331506797

Indoor mobile robots require reliable solutions for mapping, localization, and navigation tasks. This paper presents a mobile robot system that implements the ability to realize mapping using visual SLAM with ORB-SLAM3 and LiDAR-based mapping with Gmapping for indoor perception and navigation through a modular ROS architecture. The framework also implements autonomous navigation and an autonomous Rapidly-exploring Random Trees (RRT) exploration module with adaptive reset mechanisms. The system operates on a ROS-based platform equipped with an Intel RealSense D435i RGB-D camera and LDS-02 LiDAR scanner. Experimental validation in simulated indoor environments demonstrates the system's capabilities and the presented architecture achieves computational efficiency through selective utilization of mapping modules while maintaining mapping accuracy through complementary sensor modalities. © 2025 IEEE.

关键词： LiDAR SLAM Mapping Navigation ORBSLAM 3 ROS RRT

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Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal autonomous System

Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Auton...

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Ariano Da Conceicao Lianfang Tian Qiliang Du Ling Yuan School of Automation Science and Engineering South China University of Technology Guangzhou China Research Institute of Modern Industrial Innovation South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education Guangzhou China Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot (2024KSYS004) Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

关键词： Visualization Simultaneous localization and mapping Accuracy Laser radar Navigation Service robots Heuristic algorithms Computer architecture Mobile robots autonomous robots

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Event-driven Real-time Actuator Scheduling Strategy over Wireless Sensor and Actuator networks 33

Event-driven Real-time Actuator Scheduling Strategy over Wir...

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第三十三届中国控制会议

作者： Gao Huanli Liu Yonggui Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSouth China University of Technology

ISBN: (纸本)9789881563842

Delay-time and energy constraints have a significant impact on the design and operation of wireless sensor and actuator networks(WSANs).Aiming for a class of random sudden events,the real-time actuator scheduling strategy is proposed based on the event driven in *** proposed scheduling strategy,three performance indexes,such as reaction time,energy consumption and energy distribution of whole network are considered at the same *** translate the actuator scheduling strategy into a multi-objective optimization ***,the optimal solution of the multi-objective issues is usually not able to be *** solve the problems,we first obtain the optimal solution of a single objective,and then make each performance index try to be close to own optimization solution via normalizing the evaluation function in order to achieve multi-objective approximate optimization *** approach is called the normalization optimization *** extended simulations illustrate the proposed actuator scheduling strategy is feasible and effective in WSANs.

关键词： Wireless sensor and actuator networks,optimization,energy balance,cooperation scheduling

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Event-triggered control for discrete-time uncertain linear parameter-varying systems

Event-triggered control for discrete-time uncertain linear p...

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第三十二届中国控制会议

作者： Li Shanbin Xu Bugong Key Laboratory of Autonomous Systems and Network Control Ministry of Education of ChinaCollege of Control Science and EngineeringSouth China University of Technology

ISBN: (纸本)9781479900305

This paper addresses the co-design problems of event generator and controller for linear parameter varying systems where the parameters are not exactly known,but only estimated parameters satisfying certain uncertainty level is *** applying a quadratic input-to-state stable Lyapunov function,a sufficient condition for the existence of the event-triggered controller and event generator is established using the linear matrix *** further present a sufficient condition for linear time-invariant *** a numerical example,the effectiveness of the proposed method is illustrated.

关键词： event-triggered control input-to-state stable linear matrix inequality

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autonomous Multi-vehicle Consensus for High-order systems 36

Autonomous Multi-vehicle Consensus for High-order Systems

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第36届中国控制会议

作者： Yonggui Liu Chunjie Zhai Huanli Gao The Key Laboratory of Autonomous Systems and Network Control Ministry of Educationthe College of Automatic Science and EngineeringSouth China University of Technology

ISBN: (纸本)9781538629185

Multi-vehicle deployment can be formulated as a cooperation formation problem using the weighted consensus *** objectives are that make adjacent vehicles keep the constant spacing and all vehicles approach the common velocity and ***,it is preferred that vehicle platoon has fast convergence and strong robustness when disturbances *** achieve these goals,the third-order dynamics are established for vehicle platoon that are regarded as connected vehicle systems（CVSs） and distributed control protocols are designed to implement cooperation *** paper focuses on analysis of stability and stability margin for the *** sufficient and necessary conditions on stability of the CVSs are achieved using the algebraic graph theory and the Routh-Hurwitz stability *** the sufficient conditions,which are determined by the controller gains and the model parameter,but are independent on eigenvalues of the Laplacian matrix of the corresponding network topology,are *** characterize convergent speed of CVSs,the conditions on stability margin of CVSs are also *** are shown to verify validation of the results.

关键词： Vehicle platoon consensus connected vehicle systems leader-following control cooperative control

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