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Affine Formation Maneuver Control for Multiagent Systems Based on Unknown Input Reconstruction

IEEE Access 2025年 13卷 102311-102322页

作者： Liduo Hu Wei Zhang Chenhang Yan Yuhang Cai Hao Chen Zhi Hu Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing China

Disturbances can significantly impact the dynamic performance and stability of a system, making them a key focus in stability analysis. However, direct measurement of disturbances is often challenging due to technological and cost limitations. These disturbances introduce uncertainties that complicate multi-agent coordination, particularly in formation control tasks. Consequently, designing effective control strategies for multi-agent systems becomes both challenging and crucial. This paper addresses the affine transformation formation control problem of multi-agent systems in the presence of unknown disturbances. We begin by using a state-space model to describe agent dynamics and employing an unknown input observer to estimate both the state and the unknown disturbances. Based on these estimates, we redesign the control law to ensure that tracking errors converge to an adjustable residual set. This distributed control approach allows agents to operate without global information, thereby reducing communication requirements. Finally, a simulation example demonstrates the feasibility and effectiveness of the proposed control law.

关键词： Formation control Observers Laplace equations Directed graphs Vectors Multi-agent systems Protocols Matrix decomposition Aerospace electronics Vehicle dynamics

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A novel image edge detection algorithm based on Multi-scale Hybrid Wavelet Transform

A novel image edge detection algorithm based on Multi-scale ...

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2022 International Conference on Neural Networks, Information, and Communication engineering, NNICE 2022

作者： Xia, Shanhong Li, Minghang Heilongjiang Water Traffic Rescue Center Heilongjiang Province China The Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9781510655171

The classical wavelet transform in image processing is easy to lose the details, and the edge of the image is often easily disturbed by the noise which leads to poor extraction effect. In order to overcome the shortcomings of the classical image edge detection methods, an edge detection method based on multi-scale hybrid wavelet transform is proposed. A series of operations such as B-spline filter, Gaussian filter, multi-scale detection and adaptive threshold removal with pseudo-edge contour are carried out to remove the complex noise of image and to obtain the precise edge. Due to the importance of underwater image edge extraction in underwater target recognition, this method is applied to the edge detection of underwater images. The experimental results show that the method based on multi-scale hybrid wavelet transform can suppress the image noise and extract the accurate edge contour of the underwater image, which proves the effectiveness. © 2022 SPIE.

关键词： Wavelet transforms

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Multiple Jumping and Perching of a Miniature Mobile Robot

Multiple Jumping and Perching of a Miniature Mobile Robot

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Hu, Longlong Tang, Lingqi Chen, Hao Li, Yao Li, Bing Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China Machine Theory and Intelligent Unmanned Systems of Guangdong Key University Laboratory of Mechanism Shenzhen518055 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

Many insects and miniature robots can traverse through narrow spaces via jumping motion;however, successful jumping and landing remains challenging in small-scale. No jumping robot within 90 g have yet demonstrated multiple jumping and perching successfully. Here, we demonstrate continuous jumping and perching of a miniature robot. The origami-inspired structure was manufactured by smart composite microstructure technology. A novel jumping mechanism was designed to load adjustable jump energy actuated by only one motor. A perching mechanism with two adhesive pads was designed to passively couple with the jumping mechanism, achieving perching on the ground and automatically detaching before the next jump. The jumping mechanism achieved adjustable jumping energy, corresponding to jumping height from 0.7 cm to 36.4 cm. The perching mechanism provided successful landing within a wide range (when velocity angle according to vertical direction =18, the velocity magnitude 3.4m/s, the body angle 30). Unlike typical jumping robots performing uncontrollable landing and free-rolling, our robot kept its body up-righted during the whole movement and avoided free-rolling after landing. The self-contained prototype had 18 g of mass, 6.5 cm of body height, and 36.4 cm of maximum jumping height. Finally, an eccentric motor was integrated to perform multiple omnidirectional jumping and perching movements;this represents significant improvement of motion ability for small-scale mobile robots. © 2024 IEEE.

关键词： Landing

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Comparative analysis of techniques in long reach passive optical networks: Overview and design

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Journal of Optical Communications 2024年第s1期45卷 s2327-s2331页

作者： Sachdeva, Shippu Shukla, Manoj Kumar Sindhwani, Manoj Kumar, Abhishek Adhikari, Manoj Singh School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India Department of Robotics and Automation Symbiosis Institute of Technology Symbiosis International University Pune India

Long reach PON is an attractive option for addressing the growing bandwidth needs of internet applications and also serving multiple ONUs. Due to newly augmented technologies such as 4K/8K television, video meetings/conferencing, and online gaming, end-user bandwidth demands are exponentially rising day by day. Wavelength division multiplexing-based passive optical network (WDM-PON), which can deliver high data rates over long distances, is the ultimate solution to the growing bandwidth demand. It is possible to reduce the number of active cabinets and central offices while increasing the number of optical network units (ONUs) with the use of passive optical networks (PONs). At higher bit rates, researchers faced lot of issues like increased pulse width due to increased distance, burst loss, fault detection, etc. In this paper, we have discussed few challenges faced in passive optical networks. Also, we have discussed about the methodology to overcome these issues. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Passive optical networks

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Zonotope-based fault estimation for Lipschitz nonlinear systems 3

Zonotope-based fault estimation for Lipschitz nonlinear syst...

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2024 3rd International Conference on Aerospace and Control engineering, ICoACE 2024

作者： Ma, Siyuan Zhang, Wei Jiang, Qiang Gao, Sheng School of Automatic and Electrical Engineering Shenyang Ligong University Shenyang110158 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China China-Portugal Belt and Road Joint Laboratory on Space & Sea Technology Advanced Research Shanghai200120 China

An estimation of fault intervals using a T-N-L structured observer is suggested for Lipschitz nonlinear systems. By augmenting state vectors, the augmentation system and its observer are established, enabling the observer to concurrently estimate fault and system states. The L∞ performance is applied to enhance the resilience of the fault interval estimation to external disturbances and unanticipated inputs. An adequate requirement for the observer design is also presented. The interval estimation of states and faults is ultimately created by using a zonotope. The proposed method exhibits advantages and feasibility when compared to existing simulation techniques. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Nonlinear systems

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Object Tracking Algorithm Based on Dual Layer Attention 16th

Object Tracking Algorithm Based on Dual Layer Attention

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Li, Ziwan Liu, Shiben Cheng, Zhuxuan Yu, Zhencheng Fan, Huijie School of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819964857

The attention mechanism is an important part of the Transformer trackers. But the attention mechanism has some limitations in calculating the correlation. When the attention mechanism calculates the wrong correlation, it affects the quality of the final correlation and affects the overall tracking performance. To solve the above problems, we designed a dual layer attention module to improve the accuracy of correlation by dual layer attention, so that the attention mechanism can accomplish feature augmentation and fusion more effectively, thus improving the performance of the tracking algorithm. The proposed algorithm consists of a Swin-Transformer-based backbone network, a dual layer attention-based fusion module and a prediction branch. Experiments indicate that our algorithm achieves robust results on five publicly available datasets, running at approximately 35 FPS on the GPU, allowing for real-time tracking. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Attention Feature fusion Object tracking Transformer

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Assessment of the tomato cluster yield estimation algorithms via tracking-by-detection approaches

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Information Processing in Agriculture 2025年

作者： Qi, Zhongxian Zhang, Tianxue Yuan, Ting Zhou, Wei Zhang, Wenqiang College of Engineering China Agricultural University Beijing100083 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China Institute of Medical Robotics Shanghai Jiaotong University Shanghai200240 China

Automated vision-based detection and counting are critical for accurate tomato yield estimation, which contribute to precise yield management strategies and an efficient food supply chains. Special conditions, including background clutter, occlusion, and varying sunlight, affect the accuracy of crop detection and counting. To determine the most suitable algorithms for this yield estimation context, we herein establish a public multi-object tracking (MOT) dataset for tomato cluster counts, while evaluating and comparing state-of-the-art target detection and MOT-based algorithms. The evaluated detectors consist of YOLOv8 and RT-DETR, which represent algorithms that achieve a balance between accuracy and speed. The tracking algorithms included state-of-the-art methodologies such as SORT, DeepSort, ByteTrack, and BotSort. Initially, the performance of the detectors was rigorously evaluated, followed by a comprehensive assessment of the four tracking algorithms within a multi-target tracking database tailored for this research and structured in the MOT context. The findings reveal that YOLOv8 and RT-DETR achieve 93.6% and 94.9% results at mAP@75, respectively, with RT-DETR exhibiting fewer false detections. When combined with the RT-DETR detector, the ByteTrack-based algorithm registers the highest counting accuracy at 95.5%, whereas BotSort achieves the highest MOTA score with 84.6%. Notably, the trackers without the ReID module (i.e., SORT and ByteTrack) demonstrate greater adaptability to frame rate variations in the test videos. At a 30-fps frame rate, the incorporation of ReID modules in DeepSort and BotSort algorithms significantly enhances the MOTA metric. Looking ahead, we plan to leverage these algorithms into an autonomous inspection platform that aims to estimate crop yield in real-time. © 2025 The Author(s)

关键词： Fruits

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Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation

arXiv

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arXiv 2025年

作者： Fan, Shichao Yang, Quantao Liu, Yajie Wu, Kun Che, Zhengping Liu, Qingjie Wan, Min School of Mechanical Engineering and Automation BeiHang University China School of Computer Science and Engineering BeiHang University China Beijing Innovation Center of Humanoid Robotics China KTH Royal Institute of Technology Sweden

Recently, Vision-Language-Action models (VLA) have advanced robot imitation learning, but high data collection costs and limited demonstrations hinder generalization and current imitation learning methods struggle in out-of-distribution scenarios, especially for long-horizon tasks. A key challenge is how to mitigate compounding errors in imitation learning, which lead to cascading failures over extended trajectories. To address these challenges, we propose the Diffusion Trajectory-guided Policy (DTP) framework, which generates 2D trajectories through a diffusion model to guide policy learning for long-horizon tasks. By leveraging task-relevant trajectories, DTP provides trajectory-level guidance to reduce error accumulation. Our two-stage approach first trains a generative vision-language model to create diffusion-based trajectories, then refines the imitation policy using them. Experiments on the CALVIN benchmark show that DTP outperforms state-of-the-art baselines by 25% in success rate, starting from scratch without external pretraining. Moreover, DTP significantly improves real-world robot performance. All source code and experiment videos will be released upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Visible-Infrared Cross-Modal Person Re-Identification Based on Modality Alignment and Fine-Grained Mining

Visible-Infrared Cross-Modal Person Re-Identification Based ...

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IEEE International Conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Yingchang Zhao Hang Yu Zhuxuan Cheng Huijie Fan Sen Lin School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Information Engineering Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In the current field of computer vision, visible-infrared cross-modal person re-identification has become a research topic of great interest. This task aims to identify and match images of the same pedestrian from different modalities and is widely applied in scenarios like night-time surveillance and multi-camera tracking. However, due to significant visual differences between modalities, this task faces many challenges. To overcome these challenges, in this paper we propose a cross-modal person re-identification method based on modality alignment and fine-grained local feature mining. First, we introduce the Modality Alignment Module, which aims to reduce visual differences between modalities, thereby providing a more consistent foundation for feature extraction and matching. Then, through the Fine-grained Mining Module, we delve into the local details in pedestrian images to enhance the distinctiveness of features and the accuracy of identification. Moreover, by incorporating advanced deep learning architectures and optimization algorithms, our method shows exceptional results on both SYSU-MM01 and RegDB datasets.

关键词： Visualization Pedestrians Surveillance Deep architecture Feature extraction Robustness Intelligent systems Optimization Faces Identification of persons

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UAV Forest Fire Detection based on RepVGG-YOLOv5

UAV Forest Fire Detection based on RepVGG-YOLOv5

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Song, Kang Zhang, Yueyuan Lu, Bo Chi, Wenzheng Sun, Lining Department of Automation Engineering School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Ningbo Institute of Materi-als Technology & Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Suzhou215137 China

ISBN: (纸本)9781665481090

The safety of forest resources is of great im-portance to natural and public safety, and the efficient and accurate detection of forest fires is an issue of close concern. Considering the limitations of traditional forest fire detection methods, such as the limited range of view from watchtowers and insufficient resolution of satellite images, a deep learning-based Unmanned Aerial Vehicle (UAV) fire detection system has been proposed. With its high mobility, images of forest fires can be captured by camera-equipped UAV and transmitted to the ground station in real time. The ground station uses a deep learning object detection algorithm to achieve the recognition of fire. In this paper, we choose the state-of-the-art object detection algorithm YOLOv5 for the UAV forest fire detection scenario, and to further improve the detection performance of YOLOv5, we propose RepVGG-YOLOv5 by drawing on the RepVGG network, a training-time multi-branch topology with an inference-time plain architecture. Then we test the models on a self-built dataset, and the experimental results show that the improved RepVGG-YOLOv5 model outperforms the YOLOv5 model in terms of detection performance and detection speed, which proves the feasibility of this paper's model in the UAV forest fire detection systems and the effectiveness of the model improvement. Furthermore, the great potential of this paper's object detection model for real-time applications of accurate forest fire detection has also been demonstrated. © 2022 IEEE.

关键词： Object detection

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