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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhao, Kaidong Li, Yanjie Liu, Zihan Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China School of Mechanical Engineering and Automation The Harbin Institute of Technology Shenzhen Shenzhen518055 China

ISBN: (纸本)9789819642069

This paper presents a meta-reinforcement learning approach for quadrotor UAV attitude control, addressing the limitations of traditional control methods in complex, dynamic, and uncertain environments. Our method integrates the advantages of context learning, Gaussian sampling, and multi-task training to rapidly adapt to diverse aircraft parameters and environmental conditions. Specifically, we utilize historical information quadruples as contextual data to accurately estimate current task characteristics through real-time observation and learning. In the latent space, we employ Gaussian sampling to effectively handle unknown UAV parameters. To validate the method’s efficacy, we conducted a series of experiments in the Bullet simulator, including single attitude tracking, resultant acceleration control, and continuous trajectory tracking. Experimental results demonstrate that our approach significantly outperforms traditional PID controllers and domain randomization-based SAC methods in terms of stability, rapid response, and trajectory tracking precision. Through multi-task training, our controller has developed the ability to swiftly adapt to novel situations. This research provides a robust and flexible solution for adaptive control of quadrotor UAVs, showing promise for more efficient and stable flight control in complex and dynamically changing environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Traffic sign recognition system based on YOLOv8-ConvNeXt

Traffic sign recognition system based on YOLOv8-ConvNeXt

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Fan, Chubin School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

In the context of road traffic safety, intelligent driver assistance systems are particularly important. During everyday driving, drivers are influenced by factors such as weather and fatigue, which can prevent them from quickly and accurately reacting to current traffic sign information. Therefore, to enable faster and more precise recognition, this paper designs an efficient traffic sign detection method. Image enhancement technology, particularly the Retinex algorithm, is introduced to improve the visual effect of images and enhance the separability of features. The introduction of the ConvNeXt V2 network further improves the detection speed and accuracy of the YOLOv8 model. Additionally, a self-collected dataset was supplemented to lay a good foundation for subsequent training. © 2025 SPIE.

关键词： Road and street markings

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HSV-enhanced canny-hough algorithm for robust lane line recognition

HSV-enhanced canny-hough algorithm for robust lane line reco...

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi Xu, Xiong School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Lane line recognition plays an important role in road safety and intelligent transport system. This paper focuses on the key technology of lane line recognition, which is divided into three parts: firstly, the original image is preprocessed, including de-distortion and grey scale, to reduce noise and interference. Then, lane line information extraction is explored in terms of colour features and edge detection. By analysing the RGB and HSV colour modes and their conversion, the colour features are used to improve the accuracy and robustness of recognition. Meanwhile, the lane line edges are extracted by combining the improved Canny algorithm and commonly used edge detection operators, and the methods of threshold segmentation and Hough transform are applied to further describe the geometric features of lane lines. The direction of vehicle motion is determined by calculating the polar coordinate equation and vanishing point coordinates. Finally, this paper verifies the lane line recognition algorithm based on HSV-improved Canny-Hough through simulation experiments, and proves that it is more effective and accurate in lane line recognition compared with traditional methods. © 2025 SPIE.

关键词： Edge detection

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Uncertain Pushing Adaptive Coordinated Control for the Human-Exoskeleton-Walker System

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IEEE robotics and automation Letters 2025年第7期10卷 7086-7093页

作者： Xinhao Zhang Chen Yang Jingting Zhang Chaobin Zou Guangkui Song Rui Huang Muhammad Umar Farooq Hong Cheng Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

Lower Limb Exoskeletons are potential in the gait training for patients with gait disorders. For patients in the early rehabilitation stages with weak upper limb strength, it is challenge to keep balance by themselves only. A mobile robotic walker is helpful to maintain the walking balance, with the help of a physician pushing behind to keep a forward walking associated with the exoskeleton. However, since the gait patterns are varying with different training tasks, how to ensure a coordinated movement between the exoskeleton and the mobile robotic walker is challenge, especially with the uncertain pushing applied by the physician. In this letter, the Uncertain Pushing Adaptive Coordinated Control (UP-ACC) approach is proposed to solve the problem, which consists of two contributions. Based on the decoupled simplified dynamics, the center of mass trajectories and footstep placements are generated through linear model predictive control. In addition, the optimal footstep placement is used for the human-like gait planning by introducing the phases of heel-strike and toe-off. The proposed method has been verified in the robot simulation platform CoppeliaSim, and the experimental results indicate its effectiveness in generating coordinated motion and human-like gait patterns for the human-exoskeleton-walker system with the external pushing from 0 to 150 N and walking speed from 0.2 m/s to 0.6 m/s.

关键词： Legged locomotion Robot kinematics Mathematical models Exoskeletons Training Kinematics Dynamics Trajectory Planning Medical services

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Fruit classification and quality detection system based on improved YOLOv8 model

Fruit classification and quality detection system based on i...

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Huang, Yukun School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

With the expansion of fruit planting scale, the inefficiency of manual labor has gradually become a bottleneck limiting the development of the industry. In recent years, with the advancement of technology, research into modern intelligent agricultural technologies has also become a research hotspot. This paper is based on the YOLOv8 model for fruit classification and quality detection. First, image data enhancement is performed using operations such as power law transformation, and random operations like translation, flipping, and scaling are applied to augment the image dataset. A dataset is then established. Next, EfficientViT is introduced to replace the backbone network, and the SA attention mechanism is incorporated into the detection head, making the model more accurate in fruit classification and quality detection. Finally, a GUI (Graphical User Interface) visualization design is implemented to achieve a user-friendly interaction and is applied in the overall system design. © 2025 SPIE.

关键词： Graphical user interfaces

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A 3D Streamline Selection Algorithm Based on Comprehensive Similarity 3

A 3D Streamline Selection Algorithm Based on Comprehensive S...

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3rd International Conference on Artificial Intelligence and Computer engineering, ICAICE 2022

作者： Hu, Xiaomei Weng, Jiahong Chai, Jianfei Zhang, Mingnan Li, Yilin Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Information Technology Office School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (纸本)9781510663473

In order to avoid the occlusion problems and missing important features of streamlines in the flow field, this paper proposes a 3D streamline selection algorithm based on comprehensive similarity. The method starts with a hierarchical clustering of streamline sets and then extracts streamlines with high similarity based on their comprehensive similarity. The comprehensive similarity of streamlines requires the calculation of the distance and contour similarity of the streamlines. In this paper, the Hausdorff distance is improved by proposing a partially matched Hausdorff distance to reduce the influence of streamline length on the similarity calculation. Then the contour similarity is calculated according to the ICP algorithm and the entropy weighting method is used to calculate the weights to obtain the combined similarity. The final comparison with the result of another algorithm shows that the flow field structure is clearer, more complete and more evenly distributed when the streamlines are selected using this algorithm. © 2023 SPIE.

关键词： Flow fields

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Research on fruit 3D point cloud segmentation based on improved YOLOv8

Research on fruit 3D point cloud segmentation based on impro...

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi Wang, Qingtang School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

With the development and progress of science and technology in our country, the continuous improvement of people's living standards, the planting area of fruits is constantly expanding, and the fruit yield is also constantly increasing, resulting in a shortage of labor resources and bringing huge challenges to fruit picking;At the same time, due to the impact of different geographical environments, climates, and other natural environments, as well as the mutual occlusion of fruits and leaves, the positioning and picking of fruits are affected, requiring a large amount of manpower to complete the picking operation. This article uses the improved YOLOv8 algorithm for instance segmentation of fruits to improve the accuracy and efficiency of instance segmentation. By extracting mask information, corresponding depth information is obtained, and the point cloud data corresponding to apples is obtained, providing assurance for the accuracy of subsequent localization. Firstly, in the backbone network, the deformable convolutional DCNv2 is replaced with the C2f module to increase attention to small targets;By adding an improved ECA attention mechanism EMCA before the Neck layer, the model efficiency is improved. Finally, by proposing WPDIoU instead of CIoU to improve the loss, the convergence speed and segmentation accuracy of the model are improved. After experimental comparison between the original YOLOv8 model and the improved YOLOv8 model proposed in this paper, the results show that the improved YOLOv8 model has higher instance segmentation accuracy, an increase of 4.2 percentage points, and has a certain improvement in fruit instance segmentation accuracy. © 2025 SPIE.

关键词： Image segmentation

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PID-fuzzy switching-based strategy to heading control for remote operated vehicle

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Neural Computing and Applications 2024年 1-17页

作者： Xie, Baolong He, Shuping Cao, Xiang Wang, Honghai Stojanovic, Vladimir Shi, Kaibo School of Electrical Engineering and Automation Anhui University Hefei230039 China School of Artificial Intelligence Anhui University Hefei230039 China School of Computing and Artificial Intelligence Chaohu University Chaohu238024 China Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil Engineering University of Kragujevac Kraljevo36000 Serbia School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China

Accurate heading control is the premise for underwater vehicles to complete underwater operations. In order to improve the performance of heading control, this paper proposes a heading control strategy based on PID-fuzzy switching. First, the force analysis of the steering motion was carried out to derive the dynamics model of the steering motion. Then, the whole heading control process is divided into two phases, steering phase and holding phase. Fuzzy control is used in the steering phase, and PID control is used in the holding phase. By setting the automatic switching switch, the control system can switch freely between the two to meet the needs of system adjustment under different errors. Finally, the performance of the established heading control system is verified through motion control simulations and pool experiments, and the proposed control strategy is compared with other methods. The results show that the control strategy can effectively achieve ROV heading control with good steady-state accuracy, and the overall performance has been greatly improved. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fuzzy control

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Animal image recognition system based on Yolov8-AFPN

Animal image recognition system based on Yolov8-AFPN

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Li, Ning School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Object detection technology has been widely applied in the conservation and monitoring of wildlife in recent years. However, due to the complexity of field environments, the collected images of wild animals often suffer from severe lighting effects, partial blurriness, and significant environmental differences. These issues greatly impact the efficiency and accuracy of object recognition. To overcome these challenges, this paper employs Gaussian filtering as an image enhancement technique and introduces an Asymptotic Feature Pyramid Network (AFPN) to improve the YOLOv8 model. The AFPN extracts features from the bottom up and uses adaptive spatial fusion operations to optimize the feature fusion process, significantly enhancing the detection performance of animal image recognition models. © 2025 SPIE.

关键词： Image enhancement

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Study on gesture recognition based on improved YOLOV8

Study on gesture recognition based on improved YOLOV8

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi Zhang, Hengrui School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Based on YOLOV8 hand gesture signal recognition in today's hand gesture signal recognition field has a great development and application prospects, which improve the detection accuracy of the hand gesture signal is the most important thing. Hand gesture signal recognition and detection and other areas of target detection is slightly different, hand gesture in the whole picture often belongs to a smaller target, and hand gesture signal recognition of complex application scenarios, In an effort to enhance the detection precision of hand gesture signal, this paper in initial YOLOV8 algorithm based on YOLOV8 algorithm: in YOLOV8 backbone network tail to join the Multi-Scale Convolutional Attention (MSCA) module is added to the tail of YOLOV8 backbone network;Large Selective Kernel Network (LSKNet) module is included into the front of YOLOV8 detection head;and the loss function of YOLOV8 is exchanged for Wise-IoU. The improved YOLOV8 is evaluated against initial YOLOV8 in control experiments, and the experimental results indicate that all the exactness measurements of YOLOV8 are enhanced in the detection and identification of hand gesture signal, and it can be proved that this paper's algorithm can enhance the exactness of YOLOV8. The experimental conclusions indicate that the improved YOLOV8 has enhanced all the accuracy measures in the recognition and detection of hand gesture signal, and it can be demonstrated that the improved YOLOV8 suggested in this paper has certain effect in the recognition and detection of hand gesture signal. © 2025 SPIE.

关键词： Palmprint recognition

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