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学者姓名：刘海滨
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Abstract ：
This study proposes a novel adaptive distributed recurrent SAC (Soft Actor-Critic) control method to address the leader-follower formation control problem of omnidirectional mobile robots. Our method successfully eliminates the reliance on the complete state of the leader and achieves the task of formation solely using the pose between robots. Moreover, we develop a novel recurrent SAC reinforcement learning framework that ensures that the controller exhibits good transient and steady-state characteristics to achieve outstanding control performance. We also present an episode-based memory replay buffer and sampling approaches, along with a unique normalized reward function, which expedites the recurrent SAC reinforcement learning formation framework to converge rapidly and receive consistent incentives across various leader-follower tasks. This facilitates better learning and adaptation to the formation task requirements in different scenarios. Furthermore, to bolster the generalization capability of our method, we normalized the state space, effectively eliminating differences between formation tasks of different shapes. Different shapes of leader-follower formation experiments in the Gazebo simulator achieve excellent results, validating the efficacy of our method. Comparative experiments with traditional PID and common network controllers demonstrate that our method achieves faster convergence and greater robustness. These simulation results provide strong support for our study and demonstrate the potential and reliability of our method in solving real-world problems.
Keyword ：
formation control formation control recurrent SAC recurrent SAC reinforcement learning reinforcement learning leader-follower leader-follower
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| GB/T 7714 | Li, Mingfei , Liu, Haibin , Xie, Feng et al. Adaptive Distributed Control for Leader-Follower Formation Based on a Recurrent SAC Algorithm [J]. | ELECTRONICS , 2024 , 13 (17) . |
| MLA | Li, Mingfei et al. "Adaptive Distributed Control for Leader-Follower Formation Based on a Recurrent SAC Algorithm" . | ELECTRONICS 13 . 17 (2024) . |
| APA | Li, Mingfei , Liu, Haibin , Xie, Feng , Huang, He . Adaptive Distributed Control for Leader-Follower Formation Based on a Recurrent SAC Algorithm . | ELECTRONICS , 2024 , 13 (17) . |
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Abstract ：
Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, "5+X" was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications.
Keyword ：
Production scheduling Production scheduling Variable batches Variable batches Multiple varieties Multiple varieties EMA-DCPM EMA-DCPM Mixed production lines Mixed production lines
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| GB/T 7714 | Wang, Long , Liu, Haibin , Xia, Minghao et al. A machine learning based EMA-DCPM algorithm for production scheduling [J]. | SCIENTIFIC REPORTS , 2024 , 14 (1) . |
| MLA | Wang, Long et al. "A machine learning based EMA-DCPM algorithm for production scheduling" . | SCIENTIFIC REPORTS 14 . 1 (2024) . |
| APA | Wang, Long , Liu, Haibin , Xia, Minghao , Wang, Yu , Li, Mingfei . A machine learning based EMA-DCPM algorithm for production scheduling . | SCIENTIFIC REPORTS , 2024 , 14 (1) . |
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Abstract ：
Motion blur can easily affect the quality of QR code image, making it difficult to recognize QR codes on moving objects. This paper proposes an algorithm for the recognition of motion-blurred QR codes based on generative adversarial network and attention mechanism. Firstly, a multi-scale feature extraction framework for motion defuzzification is designed using deep convolutional neural networks, and enhanced multi-scale residual blocks and multi-scale feature extraction modules are utilized to capture rich local and global features. Secondly, the efficient channel attention module is added to strengthen the weights of effective features and suppress invalid features by modeling the correlations between channels. In addition, training stability is achieved through the use of the WGAN-div loss function, leading to the generation of higher quality samples. Finally, the proposed algorithm is evaluated through qualitative and quantitative comparisons with several recent methods on both the GOPRO public dataset and a self-constructed QR code dataset, respectively. The experimental results demonstrate that, compared to the other methods, the proposed algorithm has shown significant improvements in both processing time and recognition accuracy when dealing with the task of severe motion-blurred QR code recognition.
Keyword ：
Motion deblurring Motion deblurring Attention mechanism Attention mechanism Generative adversarial network Generative adversarial network QR code identification QR code identification
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| GB/T 7714 | Dong, Hao , Liu, Haibin , Li, Mingfei et al. An Algorithm for the Recognition of Motion-Blurred QR Codes Based on Generative Adversarial Networks and Attention Mechanisms [J]. | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS , 2024 , 17 (1) . |
| MLA | Dong, Hao et al. "An Algorithm for the Recognition of Motion-Blurred QR Codes Based on Generative Adversarial Networks and Attention Mechanisms" . | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 17 . 1 (2024) . |
| APA | Dong, Hao , Liu, Haibin , Li, Mingfei , Ren, Fujie , Xie, Feng . An Algorithm for the Recognition of Motion-Blurred QR Codes Based on Generative Adversarial Networks and Attention Mechanisms . | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS , 2024 , 17 (1) . |
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Abstract ：
It is very difficult for manufacturing enterprises to achieve automatic coordination of multiproject and multilevel planning when they are unable to make large-scale resource adjustments. In addition, planning and coordination work mostly relies on human experience, and inaccurate planning often occurs. This article innovatively proposes the PERT-RP-DDPGAO algorithm, which effectively combines the program evaluation and review technique (PERT) and deep deterministic policy gradient (DDPG) technology. Innovatively using matrix computing, the resource plan (RP) itself is used for the first time as an intelligent agent for reinforcement learning, achieving automatic coordination of multilevel plans. Through experiments, this algorithm can achieve automatic planning and has interpretability in management theory. To solve the problem of continuous control, the second half of the new algorithm adopts the DDPG algorithm, which has advantages in convergence and response speed compared to traditional reinforcement learning algorithms and heuristic algorithms. The response time of this algorithm is 3.0% lower than the traditional deep Q-network (DQN) algorithm and more than 8.4% shorter than the heuristic algorithm.
Keyword ：
multilevel plan multilevel plan multiproject multiproject PERT-RP-DDPGAO PERT-RP-DDPGAO resource planning resource planning automatic coordination automatic coordination
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| GB/T 7714 | Wang, Long , Liu, Haibin , Xia, Minghao et al. A Multiproject and Multilevel Plan Management Model Based on a Hybrid Program Evaluation and Review Technique and Reinforcement Learning Mechanism [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (17) . |
| MLA | Wang, Long et al. "A Multiproject and Multilevel Plan Management Model Based on a Hybrid Program Evaluation and Review Technique and Reinforcement Learning Mechanism" . | APPLIED SCIENCES-BASEL 14 . 17 (2024) . |
| APA | Wang, Long , Liu, Haibin , Xia, Minghao , Wang, Yu , Li, Mingfei . A Multiproject and Multilevel Plan Management Model Based on a Hybrid Program Evaluation and Review Technique and Reinforcement Learning Mechanism . | APPLIED SCIENCES-BASEL , 2024 , 14 (17) . |
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Abstract ：
Atomic-level manufacturing is fundamentally concerned with the precise removal, addition, and migration of material at the atomic and close-to-atomic scale (ACS). Tip-based electrochemical deposition, a quintessential ACS electrochemical additive manufacturing technique, offers promising prospects for achieving bottom-up fabrication of metallic micro/nano structures. However, the complex physicochemical reactions involved in electrodes lead to a limited understanding of the mechanisms underlying atomic electrodeposition and structural evolution. For the first time, this study proposes electric double-layer controlled electrochemical kinetics and investigates the effect of direct current (DC) and pulse current (PC) on nickel atomic electrodeposition using molecular dynamics (MD) simulations. The findings reveal that compared to DC electrodeposition, PC electrodeposition results in more orderly deposition morphology, improved surface smoothness, reduced dislocation density, and lower crystal distortion, with these effects being particularly pronounced under low pulse duty ratio conditions. In addition, the pulse frequency significantly influences the morphology and structure of the deposit. The high pulse frequency yields smoother surfaces with local protrusions, while the low frequency favors the formation of orderly and dense structures excepting slightly increased roughness. This study provides critical insights into understanding the microscopic mechanisms of atomic-scale electrodeposition processes and achieving atomically controlled tip-based electrochemical additive manufacturing of micro/nanodevices.
Keyword ：
Pulse Pulse ACSM ACSM Electrochemical additive manufacturing Electrochemical additive manufacturing Nickel atom Nickel atom
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| GB/T 7714 | Zhang, Honggang , Chen, Kai , Kang, Chengwei et al. Atomic-scale understanding of microstructural evolution in electrochemical additive manufacturing of metallic nickel [J]. | MATERIALS & DESIGN , 2024 , 245 . |
| MLA | Zhang, Honggang et al. "Atomic-scale understanding of microstructural evolution in electrochemical additive manufacturing of metallic nickel" . | MATERIALS & DESIGN 245 (2024) . |
| APA | Zhang, Honggang , Chen, Kai , Kang, Chengwei , Liu, Haibin . Atomic-scale understanding of microstructural evolution in electrochemical additive manufacturing of metallic nickel . | MATERIALS & DESIGN , 2024 , 245 . |
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Abstract ：
The genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection, and it is known for its iterative optimization capabilities in both constrained and unconstrained environments. In this paper, a novel method for GA-based dual-automatic guided vehicle (AGV)-ganged path planning is proposed to address the problem of frequent steering collisions in dual-AGV-ganged autonomous navigation. This method successfully plans global paths that are safe, collision-free, and efficient for both leader and follower AGVs. Firstly, a new ganged turning cost function was introduced based on the safe turning radius of dual-AGV-ganged systems to effectively search for selectable safe paths. Then, a dual-AGV-ganged fitness function was designed that incorporates the pose information of starting and goal points to guide the GA toward iterative optimization for smooth, efficient, and safe movement of dual AGVs. Finally, to verify the feasibility and effectiveness of the proposed algorithm, simulation experiments were conducted, and its performance was compared with traditional genetic algorithms, Astra algorithms, and Dijkstra algorithms. The results show that the proposed algorithm effectively solves the problem of frequent steering collisions, significantly shortens the path length, and improves the smoothness and safety stability of the path. Moreover, the planned paths were validated in real environments, ensuring safe paths while making more efficient use of map resources. Compared to the Dijkstra algorithm, the path length was reduced by 30.1%, further confirming the effectiveness of the method. This provides crucial technical support for the safe autonomous navigation of dual-AGV-ganged systems.
Keyword ：
dual-AGV-ganged dual-AGV-ganged path planning path planning leader-follower AGVs leader-follower AGVs genetic algorithm genetic algorithm safe steering radius safe steering radius
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| GB/T 7714 | Cai, Yongrong , Liu, Haibin , Li, Mingfei et al. A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (17) . |
| MLA | Cai, Yongrong et al. "A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm" . | APPLIED SCIENCES-BASEL 14 . 17 (2024) . |
| APA | Cai, Yongrong , Liu, Haibin , Li, Mingfei , Ren, Fujie . A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm . | APPLIED SCIENCES-BASEL , 2024 , 14 (17) . |
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Abstract ：
Multisensor (MS) data fusion is important for improving the stability of vehicle environmental perception systems. MS joint calibration is a prerequisite for the fusion of multimodality sensors. Traditional calibration methods based on calibration boards require the manual extraction of many features and manual registration, resulting in a cumbersome calibration process and significant errors. A joint calibration algorithm for a Light Laser Detection and Ranging (LiDAR) and camera is proposed based on deep learning without the need for other special calibration objects. A network model constructed based on deep learning can automatically capture object features in the environment and complete the calibration by matching and calculating object features. A mathematical model was constructed for joint LiDAR-camera calibration, and the process of sensor joint calibration was analyzed in detail. By constructing a deep-learning-based network model to determine the parameters of the rotation matrix and translation matrix, the relative spatial positions of the two sensors were determined to complete the joint calibration. The network model consists of three parts: a feature extraction module, a feature-matching module, and a feature aggregation module. The feature extraction module extracts the image features of color and depth images, the feature-matching module calculates the correlation between the two, and the feature aggregation module determines the calibration matrix parameters. The proposed algorithm was validated and tested on the KITTI-odometry dataset and compared with other advanced algorithms. The experimental results show that the average translation error of the calibration algorithm is 0.26 cm, and the average rotation error is 0.02 degrees. The calibration error is lower than those of other advanced algorithms.
Keyword ：
automatic driving automatic driving LiDAR-camera calibration LiDAR-camera calibration feature extraction feature extraction deep learning deep learning
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| GB/T 7714 | Ren, Fujie , Liu, Haibin , Wang, Huanjie . A LiDAR-Camera Joint Calibration Algorithm Based on Deep Learning [J]. | SENSORS , 2024 , 24 (18) . |
| MLA | Ren, Fujie et al. "A LiDAR-Camera Joint Calibration Algorithm Based on Deep Learning" . | SENSORS 24 . 18 (2024) . |
| APA | Ren, Fujie , Liu, Haibin , Wang, Huanjie . A LiDAR-Camera Joint Calibration Algorithm Based on Deep Learning . | SENSORS , 2024 , 24 (18) . |
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Abstract ：
Traditional manufacturing enterprises cannot adjust their production line structure in the short term. They face significant challenges in adapting to the rapidly changing market environment and meeting various variable batch production requirements. Building a suitable and convenient multi-layer planning and scheduling model is an important goal to solve the efficient operation of manufacturing enterprises. This paper proposes a planning and scheduling design that meets the needs of enterprise and the production workshop using the APERT-VC model through a top-down design methodology. APERT is an enterprise-level plan that uses attention mechanisms to collect job plan time and decomposes project plans into workshop plans through PERT technology. virtual command is workshop level plan management, which converts workshop plans into time series vectors and achieves rapid and comprehensive guidance of workshop resource planning for enterprises through multiple classification and decision-making. Through experiments, the algorithm achieved production scheduling accuracy improvement of over 30% compared to previous algorithms and a decision accuracy rate of over 90%. The first half of the new model solves the problem of collecting work time for multi variety and variable batch products, and improves the accuracy of algorithm input. The second half of the new algorithm innovatively combines image recognition technology with dispatcher behavior, achieving efficient simulation results.
Keyword ：
variable batches variable batches APERT-VC APERT-VC multiple varieties multiple varieties mixed production lines mixed production lines machine learning machine learning
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| GB/T 7714 | Wang, Long , Liu, Haibin , Xia, Minghao et al. Research on a multilevel scheduling model for multi variety and variable batch production environments based on machine learning [J]. | FRONTIERS IN ENERGY RESEARCH , 2023 , 11 . |
| MLA | Wang, Long et al. "Research on a multilevel scheduling model for multi variety and variable batch production environments based on machine learning" . | FRONTIERS IN ENERGY RESEARCH 11 (2023) . |
| APA | Wang, Long , Liu, Haibin , Xia, Minghao , Wang, Yu , Li, Mingfei . Research on a multilevel scheduling model for multi variety and variable batch production environments based on machine learning . | FRONTIERS IN ENERGY RESEARCH , 2023 , 11 . |
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Abstract ：
Humans are experts at making decisions for challenging driving tasks with uncertainties. Many efforts have been made to model the decision-making process of human drivers at the behavior level. However, limited studies explain how human drivers actively make trustworthy sequential decisions to complete interactive driving tasks in an uncertain environment. This paper argues that human drivers intently search for actions to reduce the uncertainty of their perception of the environment, i.e., perceptual uncertainty, to a low level that allows them to make a trustworthy decision easily. This paper provides a proof-of-concept framework to empirically reveal that human drivers' perceptual uncertainty decreases when executing interactive tasks with uncertainties. We first introduce an explainable-artificial intelligence approach (i.e., SHapley Additive exPlanation, SHAP) to determine the salient features on which human drivers base decisions. Then, we use entropy-based measures to quantify the drivers' perceptual changes in these ranked salient features across the decision-making process, reflecting the changes in uncertainties. The validation and verification of our proposed method are conducted in the highway on-ramp merging scenario with congested traffic using the INTERACTION dataset. Experimental results support that human drivers intentionally seek information to reduce their perceptual uncertainties in the number and rank of salient features of their perception of environments to make a trustworthy decision.
Keyword ：
Task analysis Task analysis Merging Merging Vehicles Vehicles human driver human driver interaction interaction Uncertainty Uncertainty Decision making Decision making Data models Data models Trustworthy decision-making Trustworthy decision-making uncertainty uncertainty Predictive models Predictive models
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| GB/T 7714 | Wang, Huanjie , Liu, Haibin , Wang, Wenshuo et al. On Trustworthy Decision-Making Process of Human Drivers From the View of Perceptual Uncertainty Reduction [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 , 25 (2) : 1625-1636 . |
| MLA | Wang, Huanjie et al. "On Trustworthy Decision-Making Process of Human Drivers From the View of Perceptual Uncertainty Reduction" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 25 . 2 (2023) : 1625-1636 . |
| APA | Wang, Huanjie , Liu, Haibin , Wang, Wenshuo , Sun, Lijun . On Trustworthy Decision-Making Process of Human Drivers From the View of Perceptual Uncertainty Reduction . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 , 25 (2) , 1625-1636 . |
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Abstract ：
In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance.
Keyword ：
Conv-dueling network model Conv-dueling network model Deep reinforcement learning Deep reinforcement learning Scheduling problem Scheduling problem Dynamic disturbance Dynamic disturbance
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| GB/T 7714 | Xia, Minghao , Liu, Haibin , Li, Mingfei et al. A dynamic scheduling method with Conv-Dueling and generalized representation based on reinforcement learning [J]. | INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS , 2023 , 14 (4) : 805-820 . |
| MLA | Xia, Minghao et al. "A dynamic scheduling method with Conv-Dueling and generalized representation based on reinforcement learning" . | INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS 14 . 4 (2023) : 805-820 . |
| APA | Xia, Minghao , Liu, Haibin , Li, Mingfei , Wang, Long . A dynamic scheduling method with Conv-Dueling and generalized representation based on reinforcement learning . | INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS , 2023 , 14 (4) , 805-820 . |
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