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arXiv 2024年

作者： Zeng, Guangyang Mu, Biqiang Zeng, Qingcheng Song, Yuchen Dai, Chulin Shi, Guodong Wu, Junfeng School of Data Science Chinese University of Hong Kong Shenzhen China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Guangzhou China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney Australia

Camera pose estimation is a fundamental problem in robotics. This paper focuses on two issues of interest: First, point and line features have complementary advantages, and it is of great value to design a uniform algorithm that can fuse them effectively;Second, with the development of modern front-end techniques, a large number of features can exist in a single image, which presents a potential for highly accurate robot pose estimation. With these observations, we propose AOPnP(L), an optimal linear-time camera-robot pose estimation algorithm from points and lines. Specifically, we represent a line with two distinct points on it and unify the noise model for point and line measurements where noises are added to 2D points in the image. By utilizing Plücker coordinates for line parameterization, we formulate a maximum likelihood (ML) problem for combined point and line measurements. To optimally solve the ML problem, AOPnP(L) adopts a two-step estimation scheme. In the first step, a consistent estimate that can converge to the true pose is devised by virtue of bias elimination. In the second step, a single Gauss-Newton iteration is executed to refine the initial estimate. AOPnP(L) features theoretical optimality in the sense that its mean squared error converges to the Cramér-Rao lower bound. Moreover, it owns a linear time complexity. These properties make it well-suited for precision-demanding and real-time robot pose estimation. Extensive experiments are conducted to validate our theoretical developments and demonstrate the superiority of AOPnP(L) in both static localization and dynamic odometry systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Optimal Fuel Feed Strategy for Asymmetric Arranged Multi-tanks Aircraft 3

Optimal Fuel Feed Strategy for Asymmetric Arranged Multi-tan...

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3rd International Conference on Industrial Artificial Intelligence, IAI 2021

作者： Miao, Haoyu Ouyang, Zikai Yang, Shunpeng Yan, Weichao Cao, Mengfan Chen, Shibo Yang, Zaiyue Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China

ISBN: (纸本)9781665435178

Aircraft fuel system, which provides a continuous source of fuel to the engine, is an important component of the aircraft. Although the sequential fuel feed strategy for the symmetric arranged multi-tanks aircraft is widely used in today's aircraft, the fuel feed strategy of the asymmetric arranged multi-tanks is still a challenge. In this article, a two layer offline approach is developed to obtain the fuel feed strategy to minimize the difference between the actual center of gravity (CG) and the desired CG. The performance of the proposed approach is tested in a case study based on the test data of aircraft pitch movement. The result indicates that the proposed approach solves the problem with an offline manner from the optimization perspective. © 2021 IEEE.

关键词： Aircraft

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Image Feature Matching Based on Spatial Constraints and Inertial Assistance for AGV Navigation in Digital Workshops 12

Image Feature Matching Based on Spatial Constraints and Iner...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Sun, Shijie Chen, Li Liang, Wei An, Haibo Zhang, Yinlong School of Artificial Intelligence Shenyang University of Technology Shenyang110020 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of Cetc Jiangsu Nanjing210007 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

VSLAM shows promising potential for AGV navigation in digital workshops, thanks to its merits in low cost, easy configuration, high flexibility, and high accuracy. However, the AGV visual perception system could be easily affected by lighting changes, and body abrupt motions. The state-of-the-art visual feature matching methods may fail to fulfill the navigation tasks in presence of these challenging issues. In order to solve these problems, we propose a feature matching method based on feature space constraints and inertial assistance. In order to reduce the computational time, the spatial constraints are designed to delete the obvious false matches. Besides, the Inertia Assistance Visual Sample Consensus (IAVSAC) is proposed to reduce the computational time in discriminating the wrong feature matches. In order to ensure the reliability of the inertial data, the analysis of IMU noise has been performed. The best IMU configuration with the smallest noises is designed. Our proposed method has been evaluated on the developed AGV navigation platform. The results show that the feature matching accuracy rate and computational time of ours are more favorable than those of RANSAC and MSAC. © 2022 IEEE.

关键词： Navigation

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Fractional-order fast terminal sliding mode control for nanopositioning

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IFAC-PapersOnLine 2023年第2期56卷 4313-4318页

作者： Geng Wang Sumeet S. Aphale Key Laboratory of Testing Technology for Manufacturing Process of Ministry of Education Southwest University of Science and Technology Mianyang China Artificial intelligence Robotics and Mechatronic Systems Group (ARMS) School of Engineering University of Aberdeen UK

A novel fast terminal sliding mode controller is proposed in this work for high-performance trajectory tracking at the nanometer scales. It combines a recursive integer-order non-singular high-order sliding manifold and a fractional-order fast fixed-time reaching law to ensure globally fast convergence, and adopts a time-delay-estimation based disturbance estimator deeming the designed controller robust to parameter uncertainty. The stability of the designed controller is verified through the Lyapunov framework, where the full analyses of convergence region and settling time are also presented. The tracking performance is experimentally verified on a piezo-stack-driven nano-positioning platform.

关键词： high-order sliding mode fractional calculus fast fixed-time convergence time delay estimation nanopositioning

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Estimating the Lateral Motion States of an Underwater Robot by Propeller Wake Sensing Using an Artificial Lateral Line

arXiv

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arXiv 2024年

作者： Wang, Jun Zhao, Dexin Zhao, Youxi Zhang, Feitian Shen, Tongsheng The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China The National Innovation Institute of Defense Technology Beijing China The Department of Mechanics and Engineering Science College of Engineering Peking University Beijing China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

The artificial lateral line (ALL), comprising distributed flow sensors, has been successful in sensing motion states of bioinspired underwater robots like robotic fish. However, its application to robots driven by rotating propellers remains unexplored due to the complexity of propeller wake flow. This paper investigates the feasibility of using ALL to sense propeller wake for underwater robot leader-follower formation. To estimate the lateral motion states of a leader propeller, this paper designs a multi-output deep learning network that extracts temporal and spatial features from distributed pressure measurements of propeller wake. Extensive experiments are conducted on a designed testbed, the results of which validate the effectiveness of the proposed propeller wake sensing method. Copyright © 2024, The Authors. All rights reserved.

关键词： Wakes

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EEG-Based Hand Movement Recognition: Feature Domain and Level of Decomposition 1

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13th National Conference on Industrial Problems on Machines and Mechanism, IPRoMM 2020

作者： Phukan, Nabasmita M. Kakoty, Nayan Gupta, Nipun Baruah, Neelanjana Jorhat Engineering College Jorhat India Embedded Systems and Robotics Laboratory Tezpur University Tezpur India Madhav Institute of Technology and Science Gwalior India

ISBN: (数字)9789811617690

ISBN: (纸本)9789811617683

This manuscript reports feature domains for the recognition of right and left hand movements using Electroencephalogram (EEG). A 21-channel EEG dataset of seven subjects during right and left hand fist open and close movements was collected from PhysioNet of the BCI2000 Instrumentation system. Features in time, frequency, and time-frequency domains have been explored. Support vector machine with radial basis function kernel was used for the recognition of right and left hand fist open and close movements. The recognition with time, frequency, and time-frequency domain features resulted in an accuracy of 90%, 92%, 97.5%, respectively. Time-frequency domain features obtained through discrete wavelet transform (DWT) at four decomposition levels have resulted in maximum recognition rate. The highest recognition rate of 98.6 ± 0.6 % has resulted in DWT features at level 2. This was substantiated by the fact that DWT features at level 2 establish maximum correlation with pre-processed EEG. Experimental result shows that time-frequency is the best performing feature domain among the three. Further, correlation measure of time-frequency domain features with EEG are established as a benchmark for selecting DWT decomposition level. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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A Track-based Colon Endoscopic Robot with Depth Perception Stereo Cameras for Haustral Fold Detection during Colonic Navigation

A Track-based Colon Endoscopic Robot with Depth Perception S...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shujing He Yujie Zhang Baoyi Huang Jie Lin Chaoyang Shi Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Guangzhou University of Chinese Medicine Shenzhen China Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin University Tianjin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Colon endoscopic robots represent a promising screening modality for the visualization of colon cancers with high sensitivity. However, current colonoscopy robots are often characterized by intricate and bulky mechanical structures, which pose practical challenges when moving through the complex and narrow environment of the colon. Moreover, these robots are typically equipped with a single camera, limiting their ability to accurately estimate the depth of haustral folds in the colon, which is of great importance for the active colonic navigation of the robots. To address these challenges, we develop a track-based stereoscopic endoscopic robot (TSER) which is equipped with four tracks positioned at the corners of its body. This innovative design maximizes the contact between the tracks and the colon wall, enhancing maneuverability. The tracks are constructed from de-molded polydimethylsiloxane (PDMS) and incorporate micro-patterns on their outer surfaces. We have proposed a straightforward strategy for detecting haustral folds using TSER’s stereo camera, which allows for precise identification of their position and depth. The TSER achieves an average motion speed of 9.8 mm/s in a bellows tube that contains silicone oil and a speed of 5.2 mm/s in an exvivo porcine intestinal segment. Impressively, the TSER boasts an 88.11% accuracy rate in haustral fold depth estimation, surpassing the performance of existing geometric shape fitting methods. These results demonstrate that the TSER holds great potential for effective and efficient movement and inspection within the colon, offering a promising solution for improved colon cancer screening.

关键词： Visualization Sensitivity Tracking Navigation Shape Stereo image processing Oils

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A Robust Surveillance System for AGV Using Forward-Downward Visual Measurements

A Robust Surveillance System for AGV Using Forward-Downward ...

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第35届中国控制与决策会议

作者： Sichao Zhang Wei Liang Xudong Yuan Haibo An Meng Zheng Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Science and Technology on Information Systems Engineering Laboratory the 28th Research Institute of CETC

Automatic Guided Vehicle(AGV) has been widely used in the warehouse for transporting the bulky and heavy ***,the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to,e.g.,cyber attacks,unexpected blocks of the wireless *** order to ensure AGV running safely,this paper presents a visual surveillance system by making full use of the measurements from the forward and downward ***,the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image ***,the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference *** from that,the AGV poses from the downward camera could correct scale and poses estimated the forward *** proposed method has been extensively performed on the developed *** results proves the effectiveness in using the complementary forwarddownward visual measurements for AGV security surveillance.

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration

Design of Capsule Robot for Magnetic Gastric Biopsy and Drug...

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IEEE International Conference on Mechatronics and Automation

作者： Zhihui Xu Zongze Li Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion.

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