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FWAF-VID: A Flapping-Wing Aggressive Flight Benchmark Dataset for Visual-Inertial Localization

IEEE robotics and Automation Letters 2025年第6期10卷 6328-6335页

作者： Jiang, Jizhou Pan, Erzhen Xu, Wenfu Sun, Wei Ye, Jingyang Harbin Institute of Technology School of Robotics and Advanced Manufacture Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China Guangdong Biomimetic Intelligent Unmanned System Engineering Technology Research Center Shenzhen518052 China

Accurate state estimation of micro aerial vehicles (MAVs) in high-speed and dynamic environments poses a significant challenge for visual-inertial odometry (VIO) algorithms. Flapping-wing aerial vehicles (FWAVs), as an emerging flight platform, have attracted significant attention for stealth capabilities and efficient flight characteristics. Despite the availability of numerous MAV visual-inertial datasets for six-degree-of-freedom (6-DoF) state estimation, these datasets are not applicable for FWAVs with pronounced vibrations and agile maneuverability. To address this gap, we propose a large-scale flapping-wing aggressive flight visual-inertial dataset FWAF-VID. It contains diverse integrations of synchronized onboard cameras and IMU sensors. A total of 28 sequences include static flapping, calibration, and real-world flights collected in outdoor and indoor environments. The 17 flight sequences feature variations in illuminations, speeds, trajectories, camera perspectives, and flight modalities. We provide precisely aligned ground truth RTK and UWB measurements for algorithm evaluation. Furthermore, a comprehensive benchmark comparison is conducted, qualitatively and quantitatively evaluating the positioning accuracy of state-of-the-art VIO algorithms. The experimental results indicate that despite the degradation in localization accuracy in high-dynamic environments, FWAF-VID can effectively support the further development of VIO algorithms for FWAVs. To the best of our knowledge, this is the first onboard benchmark dataset tailored for aggressive flight in flapping-wing airborne platforms. © 2025 IEEE.

关键词： Micro air vehicle (MAV)

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Flexible servo riveting system control strategy based on the RBF network and self-pierce riveting process

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Advances in Manufacturing 2023年第1期11卷 39-55页

作者： Yan Liu Qiu Tang Xin-Cheng Tian Long Cui Center for Robotics School of Control Science and EngineeringShandong UniversityJinan250061People’s Republic of China Engineering Research Center of Intelligent Unmanned System Ministry of EducationJinan250061People’s Republic of China

As more and more composite materials are used in lightweight vehicle white bodies,self-pierce riveting(SPR)technology has attracted great ***,the existing riveting tools still have the disadvantages of low efficiency and *** improve these disadvantages and the riveting qualification rate,this paper improves the control scheme of the existing riveting tools,and proposes a novel controller design approach of the flexible servo riveting system based on the RBF network and SPR ***,this paper briefly introduces the working principle and SPR procedure of the servo riveting *** a moving component force analysis is performed,which lays the foundation for the motion ***,the riveting quality inspection rules of traditional riveting tools are used for reference to plan the force-displacement curve *** control this process,the riveting force is fed back into the closed-loop control of the riveting tool and the riveting speed is computed based on the admittance control ***,this paper adopts the permanent magnet synchronous motor(PMSM)as the power of riveting tool,and proposes an integral sliding mode control approach based on the improved reaching law and the radial basis function(RBF)network friction compensation for the PMSM speed ***,the proposed control approach is simulated by Matlab,and is applied to the servo riveting system designed by our *** simulation and riveting results show the feasibility of the designed controller.

关键词： Lightweight vehicle body connection Self-pierce riveting(SPR)process Flexible servo riveting tool Riveting force planning and control

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Design and Experimental Validation of a Worm-Like Tensegrity Robot for In-Pipe Locomotion

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Journal of Bionic Engineering 2023年第2期20卷 515-529页

作者： Xiaolin Dai Yixiang Liu Wei Wang Rui Song Yibin Li Jie Zhao School of Control Science and Engineering Shandong UniversityJinan250061China Engineering Research Center of Intelligent Unmanned System of Ministry of Education Jinan250061China Shandong Research Institute of Industrial Technology Jinan250061China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin150080China

Traditional rigid-body in-pipe robots usually have complex and heavy structures with limited flexibility and *** soft in-pipe robots have great improvements in flexibility,they still have manufacturing difficulties due to their reliance on high-performance soft *** structure is a kind of self-stressed spatial structure consisting discrete rigid struts connected by a continuous net of tensional flexible strings,which combines the advantages of both rigid structures and soft *** applying tensegrity structures into robotics,this paper proposes a novel worm-like tensegrity robot for moving inside ***,a robot module capable of body deformation is designed based on the concept of tensegrity and its deformation performance is ***,the optimal parameters of the module are obtained based on the tensegrity *** deformation ability of the tensegrity module is tested ***,the worm-like tensegrity robot that can crawl inside pipes is developed by connecting three modules in *** performance and load capacity are tested on the prototype of the worm-like tensegrity robot by experiments of moving in horizontal pipe,vertical pipe,and elbow *** results demonstrate the effectiveness of the proposed design and suggest that the robot has high compliance,mobility,and adaptability although with simple structure and low cost.

关键词： Bio-inspired robot Soft robot application Tensegrity structure Pipe crawling

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Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 722-736页

作者： Da Yu Mingyi Zhang Mantian Li Fusheng Zha Junge Zhang Lining Sun Kaiqi Huang the State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin 150080China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190the School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100049 the CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China IEEE

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter *** this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new ***,prior knowledge is unavailable during training and *** works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory *** better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous ***,besides the previous raw samples,we store the corresponding logits and features in the ***,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the *** the same number of samples for replay,our method can use more past knowledge to prevent *** conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic *** further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

关键词： Catastrophic forgetting class-incremental learning continual learning(CL) experience replay

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C2FNet: Coarse-to-Fine Keypoint Localization Network for Monocular 6D Object Pose Estimation

C2FNet: Coarse-to-Fine Keypoint Localization Network for Mon...

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2023 China Automation Congress, CAC 2023

作者： Sun, Jiahao Ma, Xin Li, Yibin Center for Robotics School of Control Science and Engineering Shandong University China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan China

ISBN: (纸本)9798350303759

Estimating the 6D object pose from a singular RGB image is a fundamental task in the field of computer vision. Recent studies have demonstrated that keypoint-based methods exhibit remarkable efficacy. Such methodologies initially identify keypoints and subsequently deduce the object's pose by addressing a Perspective-n-Point (PnP) problem. Nonetheless., the prevalent approach of directly regressing the 2D coordinates of keypoints renders these methods sensitive to occlusions. Instead., we inaugurate a coarse-to-fine keypoint localization network (C2FNet)., where we utilize the predicted keypoint offsets from the first stage as input to deformable convolutional networks (DCN) in the second stage., to further capture geometric features and enhance spatial relationships between keypoints., thus outputting accurate and robust keypoint coordinates. Experiments indicate that the proposed approach surpasses existing advanced methods in terms of performance on the LINEMOD and Occlusion LINEMOD datasets. © 2023 IEEE.

关键词： Computer vision

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A Study on Trajectory Tracking of an ROV for Underwater Glider Recovery Based on Adaptive Backstepping Control Using a Command Filter

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Journal of Institute of Control, robotics and systems 2024年第11期30卷 1266-1276页

作者： Lee, Mun Haeng Kim, Sung Jae Lee, Jung Woo Suh, Jin Ho Department of Intelligent Robot Engineering Pukyong National University Korea Republic of The Industrial Science Technology Research Center Pukyong National University Korea Republic of Korea Institute of Robotics & Technology Convergence Korea Republic of Department of Mechanical System Engineering Pukyong National University Korea Republic of

This paper presents a path for a remotely operated vehicle (ROV) to retrieve an underwater glider and proposes a backstepping-based control method that utilizes a command filter and an adaptive term for trajectory tracking. The ROV path, which consists of diving, approaching, and ascending to reach the underwater glider while minimizing wave disturbances, is generated using a fifth-order polynomial trajectory generation method. To address the complexity of the backstepping method, the virtual control signal is replaced with a command filter signal. Additionally, a compensation mechanism is configured to suppress filter errors. An adaptive term based on Lyapunov stability is incorporated to compensate for lumped disturbances to enhance system control performance. The stability of the entire control system is proven through Lyapunov stability theory. To verify the performance of the proposed controller, simulations are conducted to track the configured path in a marine environment. This environment includes waves and ocean currents, with wave disturbances modeled based on the movement of water particles in waves. The simulation results demonstrate that the proposed controller achieves superior tracking performance and resilience against disturbances compared with the proportional-integral-differential controller. © 2024, Institute of Control, robotics and systems. All rights reserved.

关键词： Remotely operated vehicles

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Multi-Vision Sensor Fusion Localization and Navigation of Robot in Unstructured Environment

Multi-Vision Sensor Fusion Localization and Navigation of Ro...

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2023 China Automation Congress, CAC 2023

作者： Shang, Fuhao Zhou, Lelai Li, Yibin Zhang, Chen School of Control Science and Engineering Shandong University Center for Robotics Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China

ISBN: (纸本)9798350303759

Robots that have the ability to work in complex natural environments have broad application prospects. Focusing on the lack of autonomous navigation ability of the robot in the wild environment, this paper respectively proposes the improved localization and trajectory tracking methods. The multi-vision sensor fusion localization method is based on dual-factor graph optimization, which realizes the rapid processing of asynchronous sensor information. The dual-factor graph optimization can improve the localization accuracy and fault tolerance of the system under outdoor environments. Meanwhile, the multi-fine granularity MPC-based trajectory tracking method for unstructured terrain. Under the large-scale global path, a refined map is built around the robot. And the elevation difference is introduced to impose new constraints at the omni-directional velocity. The proposed methods in this study are implemented in actual environment. Experiments and simulations in representative terrain environments have verified the effectiveness of the improved methods. © 2023 IEEE.

关键词： Navigation

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Hierarchical Trajectory Optimization for Humanoid Robot Jumping Motion 16th

Hierarchical Trajectory Optimization for Humanoid Robot Jump...

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

作者： Sun, Junbao Liu, Haopeng Li, Xu Feng, Haibo Fu, Yili Zhang, Songyuan Intelligent Robot Research Center Zhejiang Laboratory Hangzhou311100 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819964796

In order to make the robot have high dynamic motion ability, this paper uses the trajectory optimization method to plan the jumping motion trajectory of the humanoid robot quickly and effectively, and carries out the jumping motion experiment on the HIT-HU humanoid robot platform. In order to improve the solving speed of trajectory optimization problem, simplified centroid dynamics model and single rigid body model are established. Differential dynamic programming (DDP) based on the combination of the centroid dynamic model and the whole body joint kinematic model is used to solve the hopping trajectory optimization problem with high accuracy, and the external penalty function method is used to deal with the relevant constraints. In order to overcome the problem that the solver is sensitive to the initial point selection, a hierarchical trajectory optimization framework is proposed. The framework uses the direct collocation method based on the single rigid body model. The trajectory of the simplified model is used as the initial trajectory guess, and the differential dynamic programming method is introduced to solve the optimal trajectory of the whole joint. The differential dynamic programming algorithm using the direct method of warm start is about twice as fast as the method of manually given initial trajectory. The simulation and experimental verification of the vertical jump trajectories generated by the algorithm on the humanoid robot HIT-HU show that the jump trajectories generated by the algorithm can be effectively transformed into the motion trajectories of humanoid robots, and can be effectively executed on the physical prototype. This further verifies the effectiveness of the algorithm and plays an important role in practical application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Anthropomorphic robots

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research on Multi-sensor Fusion Localization of Wheel-Legged Robots in Dynamic Scenarios 7

Research on Multi-sensor Fusion Localization of Wheel-Legged...

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Li, Xu Fu, Yili Cheng, Zuolun Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin150001 China Zhejiang Laboratory Intelligent Robot Research Center Hangzhou311100 China

ISBN: (纸本)9798350302882

With the rapid development of human beings in motion control and localization perception, intelligent mobile robots are widely used in various scenarios, such as industrial production, medical care, logistics and distribution. Among them, wheel-legged robots have both the high mobility of wheeled robots and the high adaptability of legged robots, which can not only move fast on platform terrain, but also adapt to rugged and complex ground with the multiple degrees of freedom of their legs. Wheel-legged robots face the following main problems in high-precision robust localization in multiple scenarios: there are a lot of dynamic factors in daily indoor and outdoor scenarios, such as vehicles, pedestrians, etc., which require specific processing of dynamic environment in the visual front-end part to improve the effect of visual factor constraints;as ground mobile robots, the localization framework of visual inertial fusion (VIO) will show scale degeneration phenomenon, resulting in long-distance localization accuracy decline. In addition, as a two wheeled inverted pendulum robot, the ground constraint form will undergo corresponding changes. Aiming at the above problems, this paper takes the hydraulic-driven wheel-legged robot as the object and proposes a multi-sensor fusion localization method in dynamic scenarios. © 2023 IEEE.

关键词： Degrees of freedom (mechanics)

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Towards Disturbance Rejection in Feature Pyramid Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 946-958页

作者： Hu, Xiaobo Xu, Wei Gan, Yi Su, Jianbo Zhang, Jun Shanghai Jiao Tong University Joint Institute of UM-SJTU Shanghai200240 China Shanghai Jiao Tong University Research Center of Intelligent Robotics Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai200240 China

The multiscale problem is one of the challenging topics in the object detection field. Among the current studies toward this problem, feature pyramid network (FPN) has been shown a superior performance. The core principle of FPN is a mapping for the detectable objects into hierarchy features. These features are designed with different size of receptive fields, which brings good scale invariance in the classification and localization subtasks. However, a disturbance of information dilution is generated by the assignment for the detectable objects into different features. This dilution disturbance is associated with the convolution or pooling operation during the hierarchy feature extraction process. Both of the imprecise localization information in the top features and the fuzzy classification information in the bottom ones limit the performance of FPN. Aiming at the disturbance rejection problem of information dilution, this article presents a feature compensation method. A cross compensation between the semantic information and the localization information in hierarchy features is used to enhance the vanilla FPN. Then, the evaluation metrics in an object detection task are examined. Not only the overall performance, but also the respective metrics for multiscale objects validate a considerable improvement of the FPN with a disturbance compensation mechanism. The experimental results demonstrate an effectiveness of the proposed method in this article. © 2020 IEEE.

关键词： Object detection

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