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检索条件"机构=Intelligent Robot and Automation Laboratory"

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Prescribed-Time Observer-Based Consensus Control for Nonlinear Leader-Following Multi-Agent Systems

Prescribed-Time Observer-Based Consensus Control for Nonline...

引用

Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： He Kang Na Li Yuan Fan Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Hefei China

This paper studies the prescribed-time consensus control problem of the nonlinear leader-following multi-agent systems(MASs). The convergence time of our protocol can be set according to the needs of the actual task. In the leader-following model, we construct a distributed prescribed-time observer(DPTO) to reconstruct the leader’s state, so that the leader’s information can be accurately obtained by followers. Then we designed a tracking controller to make each follower’s state consistent with the restructureed leader’s state. Finally, the simulation verifies that the control protocol proposed by us is feasible.

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Finite-Region Asynchronous Fault-Tolerant Control of 2-D Markov Jump Systems with Sensor Faults

Finite-Region Asynchronous Fault-Tolerant Control of 2-D Mar...

引用

American Control Conference (ACC)

作者： Chengcheng Ren Zeliang Xia Shuping He Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Hefei China

This paper investigates the finite-region asynchronous fault-tolerant control problem for two-dimensional Markov jump systems subject to sensor faults. The purpose of this paper is to derive a suitable asynchronous fault-tolerant control method to make the closed-loop two-dimensional Markov jump systems be finite region stable and fulfill the defined H ∞ performance index. Then, some sufficient conditions are developed to acquire the finite-region control method by means of the stochastic Lyapunov-Krasovskii functional methods. In addition, to illustrate the feasibility of the methodology, a numerical example is presented.

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A Real-Time VSLAM Based on Deep Features and Object Detection for Dynamic Environments

A Real-Time VSLAM Based on Deep Features and Object Detectio...

引用

Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Yuanhui Jiang Teng Li Yuan Fan Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering Automation Anhui University Hefei China

The current simultaneous localization and mapping (SLAM) relies heavily on the hypothesis of static environments, and the localization accuracy and stability of the algorithm will be extremely affected when facing dynamic and complex environment. In response to this problem, a real-time visual SLAM system for dynamic environments is presented in this work, based on the ORB-SLAM3 algorithm for improvement. After obtaining the RGB images, the features are first extracted by the GCNv2 neural network, while the semantic information about the images is initially obtained using the lightweight target detection network YOLOv5. Then, based on the dynamic characteristics of the object between consecutive frames, the static feature points are obtained by eliminating its dynamic features to complete the pose estimation. The proposed algorithm is validated using the publicly available TUM dataset, and the experimental results show that the root mean square error of absolute trajectory error is reduced to 93.96% and 33.3% compared with ORB-SLAM3 and DS-SLAM, which improves the localization accuracy in dynamic scenes and also ensures the real-time performance of the system.

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Design of a Flexible robot for Inspection in Confined Spaces

Design of a Flexible Robot for Inspection in Confined Spaces

引用

2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Li, Jie Shen, Ji Wang, Chongyang Li, Dingjia Cui, Junlin Liu, Hao School of Mechanical Engineering Shenyang Jianzhu University Shenyang110168 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 Shenyang1100169 China Key Laboratory of Minimally Invasive Surgical Robot Liaoning Province Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

The complex shape of aircraft engine blades, combined with their location in confined spaces, poses significant challenges for inspection tasks. These confined spaces not only limit the operational range of inspection equipment, making the task more difficult, but also hinder traditional rigid robots from flexibly adapting to complex paths. Additionally, manual inspection methods often require disassembling the engine, which is both costly and inefficient. To address these challenges, this paper presents a flexible robot designed for use in confined spaces. The robot features a hinge-joint design, with a continuum flexible arm at the proximal end, allowing it to adapt flexibly to narrow environments, and a jointed flexible arm at the distal end, providing high load-bearing capacity. Furthermore, coordinated movement and pose decoupling of the two-segment continuum are achieved through fixed tendons at both ends. Experimental results demonstrate that this robot exhibits high flexibility and load capacity during blade inspection, significantly improving inspection efficiency and showing great potential for applications in confined-space inspections. © 2024 IEEE.

关键词： Inspection equipment

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DGN-MEF:A DenseNet-Based Multi-Exposure Image Fusion Framework using Adaptive Gabor convolution

DGN-MEF:A DenseNet-Based Multi-Exposure Image Fusion Framewo...

引用

第43届中国控制会议

作者： Mei Zhang Ye Jin Jinhui Zhu School of Automation Science and Engineering South China University of Technology School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method.

关键词： Multi-exposure Image Fusion Adaptive Gabor Convolution Cross-domain Aggregation Attention

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A Visual SLAM Image Mismatching Filter Algorithm Based on Progressive Aample Consensus 11

A Visual SLAM Image Mismatching Filter Algorithm Based on Pr...

引用

11th International Conference on Information Science and Technology, ICIST 2021

作者： Guo, Yuchao Fan, Yuan Pan, Gaofeng Song, Cheng Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipmen Intelligent Equipment School of Electrical Engineering and Automation Hefei China Nanjing University of Science and Technology School of Automation Nanjing China

ISBN: (纸本)9781665412667

Visual SLAM based on ORB features will increase the computational pressure of SLAM system due to the large amount of feature extraction and matching computation and the need to screen a large number of mismatched point pairs. It cannot completely eliminate the mismatched point pairs, which will affect the camera positioning accuracy of visual SLAM system to a certain extent. To solve the two questions, the PROSAC algorithm is used, screening point of mismatch on, all matching points by calculation of evaluation function, selection to match point to build the model with the highest quality, through continuous to join interior point, build the final model, screening point pairs of mismatch. Provide high quality data for camera pose estimation and back end optimization of SLAM system. Through the comparison of RANSAC algorithm and PROSAC algorithm false match screening time, as well as tracking error. PROSAC algorithm effectively reduced the time of mismatching screening, with a maximum improvement of 100 times. The tracking error has also improved significantly. © 2021 IEEE.

关键词： Cameras

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Long-Term LiDAR Place Recognition Based on Phase Congruency in Changing Environments 42

Long-Term LiDAR Place Recognition Based on Phase Congruency ...

引用

42nd Chinese Control Conference, CCC 2023

作者： Cao, Fengkui Li, Chi Wang, Ting 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 College of Information Engineering Dalian University Dalian China Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9789887581543

Robust Place recognition is an open challenging problem under various perceptual conditions, e.g., all weather, times-of-day and seasons shifts. Although LiDAR sensors do not suffer from illumination problem of visual appearance, they are dramatically affected by structural changes across seasons. In this paper, a simple yet effective place recognition method is proposed based on phase congruency to achieve long-term robust localization under severe seasonal changes. To improve the efficiency while retaining geometric information of scene layouts in 3D space, a compact cylindrical description is designed to convert 3D point clouds to 2D images, in which the pixel values are computed based on geometric information rather than heights or numbers of points. Then the histogram of orientated phase congruency are encoded as global descriptor for place retrieval. Extensive real experiments on across-season Oxford robotCar dataset show that the proposed system outperforms baseline methods, verifying its robust performance in challenging scenarios. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Image enhancement

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A method for estimating the contribution of an object to scene semantics 4

A method for estimating the contribution of an object to sce...

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4th International Conference on Computer Vision and Information Technology, CVIT 2023

作者： Lin, Zhiyuan Zhu, Feng Wang, Jianyu Wang, Qun Zhao, Pengfei Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China 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 Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China

ISBN: (纸本)9781510672864

The amount of semantic contribution of each object in an image to understand the scene is different. Understanding the relative importance of objects in a scene for scene semantics is critical for various computer vision applications, such as scene recognition and image captioning. In this paper, we refer to the contribution of an object to scene semantics as the degree of its gist and propose a method for Estimating the degree of the Gist of an Instance (EGoI). In the EGoI method, an object gist degree is estimated by the semantic features comparison strategy and the semantic distance comparison strategy. In the first strategy, the image is represented as a scene graph first, then the aggregation features and the node features of different graph node combinations are calculated to estimate the instance gist that is not in the combination of the nodes. In the second strategy, the captions of the complete and incomplete images are generated, then the semantic distance of these captions is used to estimate the instance gist deleted in the scene. Different strategies for estimating the gist degree of instances are tested in the experiments. The results show that the proposed method can effectively quantify the contribution of an instance to scene semantics. Among these strategies, the method that compares semantic features has better discriminative power for the gist degree of various instances in the scene. © 2024 SPIE.

关键词： Semantics

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A Hollow FBG-Based 3-Axis Force Sensor for Surgical robots

A Hollow FBG-Based 3-Axis Force Sensor for Surgical Robots

引用

2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Li, Jie Deng, Weiquan Wang, Chongyang Cheng, Haoning Cui, Junlin Liang, Ming Liu, Hao Shenyang Jianzhu University School of Mechanical Engineering Shenyang110168 China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang1100169 China Key Laboratory of Minimally Invasive Surgical Robot Shenyang110016 China The General Hospital of Northern Theater Command of the Chinese People's Liberation Army Department of Cardiology China

ISBN: (纸本)9798350372601

The ability to perceive forces is crucial for surgical robots to perform operations safely and achieve effective treatment. This paper presents the design of a hollow force sensor based on fiber Bragg gratings (FBGs) to detect 3-Axis forces between the instrument and tissue. The sensor, consisting of an elastic body and four optical fibers with FBGs, is designed for convenient integration into instruments. Unlike most previous force sensors proposed by researchers, which fail to balance sensitivity across axes and have a hollow structure, the sensor designed in this paper positions the optical fibers away from the center to reserve sufficient space for the instrument channel. Additionally, the proposed elastic body comprises both an outer tube of flexible deformable beams and inner support pillars that enhance radial stiffness. This design ensures sensitivity along the axis while increasing stiffness against bending deformation radially. Furthermore, the force sensor is manufactured using 3D printing, making assembly simple and enabling easy interchangeability of instrument tips for various surgical instruments. Finite element simulations and experiments were conducted to verify the sensor's excellent linearity. The measurement range for each axis extends from 0 to 4 N. The axial resolution is 6 mN, while the radial resolution is 3.1 mN. At last, temperature compensation was implemented for the sensor environment to mitigate temperature-induced interference, ensuring precise measurements of three-Axis forces. The sensor is expected to be integrated into surgical robots to ensure surgical safety and enhance surgical quality. © 2024 IEEE.

关键词： Fiber Bragg gratings

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Detection of Face Mask Wearing for COVID-19 Protection based on Transfer Learning and Classic CNN Model 19

Detection of Face Mask Wearing for COVID-19 Protection based...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Han, Yingzhu Dai, Chuyi Liu, Ding School of Mechano-Electronic Engineering Xidian University Xi'an710071 China Hangzhou Institute of Technology Xidian University Intelligent Robot Laboratory Hangzhou311231 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781665472432

In 2020, COVID-19 swept the world. To prevent the spread of the outbreak, it is crucial to ensure that everyone wears a mask during daily travel and in public places. However, relying on human inspection alone is inevitably negligent and there is a potential risk of cross-contamination between people. Automated detection by means of cameras and artificial intelligence becomes a technical solution. By training convolutional neural networks, image recognition can be implemented and image classification can be performed as a solution to the target mask-wearing detection problem. To this end, in this thesis, three typical convolutional neural network architectures, VGG-16, Inception V3, and DenseNet-121, are used as models based on deep learning to investigate the mask-wearing detection problem by using transfer learning ideas. By building six different models and comparing the performance of different typical network architectures on the same dataset using two transfer learning methods, feature extraction and fine-tuning, we can conclude that DenseNet-121 is the typical architecture with the best performance among the three networks, and fine-tuning has better transfer ability than feature extraction in solving the target mask wearing detection problem. © 2022 IEEE.

关键词： Convolutional neural networks

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