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学者姓名：刘海滨
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Abstract ：
This study develops a novel end-to-end formation strategy for leader-follower formation control of mobile robots that uses onboard LiDAR sensors in non-communication environments. The main contributions of this letter are twofold: Firstly, we propose a point cloud-based LiDAR servoing control method (PCLS) aimed at ensuring mobile robots achieve the predefined formation performance without direct communication. Secondly, an innovative two-stage Soft Actor-Critic (TSSAC) algorithm is presented, specifically designed for end-to-end training of PCLS. This algorithm skillfully combines the strengths of a distance-based agent (serving as a "teacher") and a point cloud-based agent (serving as a "student"), effectively addressing the issues of slow convergence and insufficient generalization in deep reinforcement learning methods that use high-dimensional features (such as point clouds, images) as inputs. Furthermore, as part of our method, we designed a novel reward function and normalized the point cloud inputs to provide consistent incentives for the agent across diverse formation tasks, thereby facilitating better learning and adaptation to formation tasks in different environments. Finally, through extensive experiments conducted in the Gazebo simulator and real-world environments, we confirmed the effectiveness of the proposed method. Compared to other formation control strategies, our approach relies solely on onboard LiDAR sensors, without the need for additional communication devices, while ensuring excellent transient and steady-state performance.
Keyword ：
Mobile robots Mobile robots Formation control Formation control Robots Robots Leader-follower Leader-follower reinforcement learning reinforcement learning Robot kinematics Robot kinematics Training Training Point cloud compression Point cloud compression Real-time systems Real-time systems Mathematical models Mathematical models point cloud-based lidar servoing point cloud-based lidar servoing Visual servoing Visual servoing robot formation robot formation Laser radar Laser radar
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| GB/T 7714 | Li, Mingfei , Liu, Haibin , Xie, Feng et al. Point Cloud-Based End-to-End Formation Control Using a Two Stage SAC Algorithm [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2025 , 10 (3) : 2319-2326 . |
| MLA | Li, Mingfei et al. "Point Cloud-Based End-to-End Formation Control Using a Two Stage SAC Algorithm" . | IEEE ROBOTICS AND AUTOMATION LETTERS 10 . 3 (2025) : 2319-2326 . |
| APA | Li, Mingfei , Liu, Haibin , Xie, Feng , Huang, He . Point Cloud-Based End-to-End Formation Control Using a Two Stage SAC Algorithm . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2025 , 10 (3) , 2319-2326 . |
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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 ：
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 ：
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 ：
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 performance of unmanned ground vehicle (UGV) formation is crucial for large-scale material transport. In a non-communicative environment, visual perception plays a central role in formation control. However, due to unstable lighting conditions, dust, fog, and visual occlusions, developing a high-precision visual formation control technology that does not rely on external markers remains a significant challenge in UGVs. This study developed a new UGV formation controller that relies solely on onboard visual sensors and proposed a teacher-student training method, TSTMIPI, combining the PPO algorithm with imitation learning, which significantly improves the control precision and convergence speed of the vision-based reinforcement learning formation controller. To further enhance formation control stability, we constructed a belief state encoder (BSE) based on convolutional neural networks, which effectively integrates visual perception and proprioceptive information. Simulation results show that the control strategy combining TSTMIPI and BSE not only eliminates the reliance on external markers but also significantly improves control precision under different noise levels and visual occlusion conditions, surpassing existing visual formation control methods in maintaining the desired distance and angular precision.
Keyword ：
formation control formation control end-to-end end-to-end reinforcement learning reinforcement learning visual perception visual perception
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| GB/T 7714 | Li, Mingfei , Liu, Haibin , Xie, Feng . Robust Formation Control for Unmanned Ground Vehicles Using Onboard Visual Sensors and Machine Learning [J]. | DRONES , 2024 , 8 (12) . |
| MLA | Li, Mingfei et al. "Robust Formation Control for Unmanned Ground Vehicles Using Onboard Visual Sensors and Machine Learning" . | DRONES 8 . 12 (2024) . |
| APA | Li, Mingfei , Liu, Haibin , Xie, Feng . Robust Formation Control for Unmanned Ground Vehicles Using Onboard Visual Sensors and Machine Learning . | DRONES , 2024 , 8 (12) . |
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Abstract ：
This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning. Firstly, a judgment mechanism is incorporated into the DBSCAN algorithm. This mechanism is based on the standard deviation and correlation coefficient of point cloud clusters. It improves the algorithm's adaptive clustering capabilities. Secondly, the point cloud is partitioned into straight-line point cloud clusters, with each cluster generating adaptive grid cells. These adaptive cells extend the range of point cloud registration. This boosts the algorithm's robustness and provides an initial value for subsequent optimization. Lastly, cell segmentation is performed, where the number of segments is determined by the lengths of the adaptively generated cells, thereby improving registration accuracy. The proposed CSNDT algorithm demonstrates superior robustness, precision, and matching efficiency compared to classical point cloud registration methods such as the Iterative Closest Point (ICP) algorithm and the NDT algorithm.
Keyword ：
point cloud registration point cloud registration robot localization robot localization density-based spatial clustering of applications with noise (DBSCAN) density-based spatial clustering of applications with noise (DBSCAN) clustering and segmentation clustering and segmentation normal distribution transform (NDT) normal distribution transform (NDT)
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| GB/T 7714 | Liu, Haibin , Tang, Yanglei , Wang, Huanjie . Robust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation [J]. | SENSORS , 2024 , 24 (24) . |
| MLA | Liu, Haibin et al. "Robust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation" . | SENSORS 24 . 24 (2024) . |
| APA | Liu, Haibin , Tang, Yanglei , Wang, Huanjie . Robust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation . | SENSORS , 2024 , 24 (24) . |
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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 ：
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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