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arXiv 2023年

作者： Xia, Ruican Pei, Hailong The Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou510640 China

Relative state estimation using exteroceptive sensors suffers from limitations of the field of view (FOV) and false detection, that the proprioceptive sensor (IMU) data are usually engaged to compensate. Recently ego-motion constraint obtained by Inertial measurement unit (IMU) preintegration has been extensively used in simultaneous localization and mapping (SLAM) to alleviate the computation burden. This paper introduces an extended preintegration incorporating the IMU preintegration of two platforms to formulate the motion constraint of relative state. One merit of this analytic constraint is that it can be seamlessly integrated into the unified graph optimization framework to implement the relative state estimation in a high-performance real-time tracking thread, another point is a full smoother design with this precise constraint to optimize the 3D coordinate and refine the state for the refinement thread. We compare extensively in simulations the proposed algorithms with two existing approaches to confirm our outperformance. In the real virtual reality (VR) application design with the proposed estimator, we properly realize the visual tracking of the six degrees of freedom (6DoF) controller suitable for almost all scenarios, including the challenging environment with missing features, light mutation, dynamic scenes, etc. The demo video is at https://***/watch?v=0idb9Ls2iAM. For the benefit of the community, we make the source code public. Copyright © 2023, The Authors. All rights reserved.

关键词： Virtual reality

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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A Manta Ray-Inspired Biosyncretic Robot with Stable controllability by Dynamic Electric Stimulation

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Cyborg and Bionic systems 2022年第1期2022卷 314-326页

作者： Chuang Zhang Yiwei Zhang Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Inst itute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang 110159China Emerging Technologies Institute Department of Industrial Manufacturing Systems Engineering University of Hong KongPokfulamChina

Biosyncretic robots,which are new nature-based robots in addition to bionic robots,that utilize biological materials to realize their core function,have been supposed to further promote the progress in *** as the main operation mechanism relates to the robotic overall ***,biosyncretic robots actuated by living biological actuators have attracted increasing ***,innovative propelling modes and control methods are still necessary for the further development of controllable motion performance of biosyncretic *** this work,a muscle tissue-based biosyncretic swimmer with a manta *** propelling mode has been *** is more,to improve the stable controllability of the biosyncretic swimmer,a dynamic control method based on circularly distributed multiple electrodes(CDME)has been *** this method,the direction of the electric field generated by the CDME could be real-time controlled to be parallel with the adtuation tissue of the dynamic ***,the instability of the tissue actuation induced by the dynamic included angle between the tissue axis and electric field direction could be ***,the biosyncretic robot has demonstrated stable,controllable,and efective swimming,by adjusting the electric stimulation pulse direction,amplitude,and *** work may be beneficial for not only the development of biosyncretic robots but also other related studies including bionic design of soft robots and muscle tissue engineering.

关键词： Robot robot Dynamic

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A DDOS Attack Traffic Classification Model for Industrial Internet Based on CNN-LSTM

A DDOS Attack Traffic Classification Model for Industrial In...

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2022 Chinese Automation Congress, CAC 2022

作者： Wei, Weixuan Liu, Xianda Sheng, Chuan Feng, Ansong Shenyang University of Chemical Technology College of Information Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

With the continuous development and application of industrial Internet, the weakness of the security defense capability of industrial Internet has become increasingly prominent. This paper presents an end-to-end classification model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for DDoS attacks over industrial Internet. By self-learning the spatial and temporal characteristics of the original attack traffic, the model can avoid subjective errors that may occur due to manual selection of features. This method is a feasible means, with an accuracy rate of 99%. © 2022 IEEE.

关键词： Long short-term memory

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TASF: Terrestrial-Aerial Synergistic Framework for a New Generation of Intelligent Transportation

TASF: Terrestrial-Aerial Synergistic Framework for a New Gen...

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International Conference on Intelligent Transportation

作者： Cong Wang Dujun Nie Ruijun Zhang Siyu Teng Long Chen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Hong Kong Baptist University Kowloon Hong Kong China WAYTOUS Inc. Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Terrestrial-Aerial Synergistic Framework (TASF) represents a novel approach to intelligent transportation, integrating terrestrial and aerial vehicles to enhance urban transportation efficiency and capacity. In this paper, we offer an in-depth analysis of TASF, emphasizing its innovative applications in urban transportation, parcel delivery, and emergency response services. TASF incorporates a five-layer architecture comprising perception, communication, planning, control, and interaction. With the five-layer architecture, TASF can achieve effective collaboration among terrestrial and aerial vehicles, encompassing two principal features: terrestrial-aerial synergistic parallel perception and multi-agent parallel management. Parallel perception results from fusing Bird's Eye View (BEV) representations from Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), whereas decentralized multi-agent management employs foundational models to enhance decision-making capabilities. Additionally, we consider challenges and potential solutions related to TASF, including safety, reliability, energy efficiency, management and integration. TASF marks a paradigm-shifting leap in the realm of intelligent transportation systems, providing a scalable and efficacious solution tailored to the dynamic requirements of urban mobility and emergency services.

关键词： Foundation models Urban areas Decision making Transportation Emergency services Land vehicles Energy efficiency Planning Reliability Vehicle dynamics

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User-Centric Bandwidth Allocation in Network Slicing via Dueling DRL

User-Centric Bandwidth Allocation in Network Slicing via Due...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Yitian Wang Kangxu Tian Hui Wang Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Information Engineering Shenyang University of Chemical Technology Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

Network slicing plays a pivotal role in 5 G and next-generation communication systems to support diversified service requirements. This paper investigates bandwidth allocation in a multi-base station, multi-user network where users probabilistically request network slicing services. We formulate a dynamic resource optimization problem to maximize user satisfaction, accounting for stochastic service demands and heterogeneous quality-of-service requirements. An Adaptive Hierarchical Dueling Deep Q-Network (AH-Dueling DQN) algorithm is proposed to address the NP-hard mixed-integer programming nature of this problem, leveraging deep reinforcement learning to handle high-dimensional state-action spaces. Simulation results demonstrate the algorithm’s effectiveness in reducing users’ latency while improving the throughput.

关键词： Network slicing Heuristic algorithms Simulation Stochastic processes Quality of service Channel allocation Deep reinforcement learning Throughput Resource management Optimization

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control method of outlet moisture content of tobacco primary processing based on TVA-TCN-MPC 10

Control method of outlet moisture content of tobacco primary...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Zhu, Xuejing Peng, Hui Shu, Kuiwu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Hongta Liaoning Tobacco Industry Limited Liability Company Yingkou China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

The stability of outlet moisture content of primary tobacco processing is an indicator that the tobacco industry is very concerned about. In view of the problems that most of the existing control methods need to rely on artificial experience and complex mechanism modeling, a control method of outlet moisture content of tobacco primary processing based on TVA- TCN-MPC is proposed. Firstly, the TVA- TCN outlet moisture prediction model is established with the input moisture, water addition coefficient, and outlet moisture as input. Then, its output is used as the input of the objective optimization function, which is constrained by the control error and the adjustment range of the water addition coefficient, so that the control problem is transformed into the problem of solving the objective function, and the local optimal control sequence is obtained. And then continuously rolling optimization to achieve the target outlet moisture. Experiments are carried out on the actual data of cigarette factory, and the outlet moisture control algorithm based on TVA- TCN-MPC can effectively improve the stability of outlet moisture. © 2022 IEEE.

关键词： Water supply

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Aspect-Ratio and Scale Aware Rotated Defect Detection for Fiber-reinforced Composites

Aspect-Ratio and Scale Aware Rotated Defect Detection for Fi...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Yuanye Xu Yuanhao Liu Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection often lead to overlapping boxes and inaccurate results. This paper proposes an Aspect-Ratio and Scale Aware Detector network (ARSADet) specifically designed for rotated defect detection in FRCs. The network incorporates a Gabor convolutional network for feature extraction, an aspect-ratio and scale-aware label assignment method, and a newly established FRCs Oriented Defect Detection dataset (FODD). Evaluation on the FODD dataset demonstrates superior performance compared to existing methods.

关键词： Deep learning Stacking Detectors Optical fiber networks Feature extraction Product design Quality assessment

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Model-Based Shape Estimation and Closed-Loop control of Magnetic Continuum Robot Based on Monocular Vision

Model-Based Shape Estimation and Closed-Loop Control of Magn...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhu, Junfeng Huang, Yuanrui Chen, Jian Liu, Hongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100049 China Hong Kong Institute of Science Innovation Chinese Academy of Sciences Centre of AI and Robotics China School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom

ISBN: (纸本)9781665481090

Magnetic continuum robots (MCRs) show promising potential for applications in narrow lumens, such as vascular interventions. Accurate shape sensing of these robots is crucial for the success of interventional surgeries. However, the submillimeter dimensions of MCRs poses significant challenges for integrating additional sensors. An alternative approach is shape estimation from images, yet the lack of precise MCR kinematics can make this process time-consuming. In this work, we propose a novel MCR design featuring a rectangular tip capable of specific planar bending, along with a corresponding kinematic model and a mechanical model. Additionally, we introduce a model-based shape estimation method that enables the estimation of the MCR's shape from a single-view image without the need for external markers. Experimental results demonstrate that our method can estimate an MCR's shape with a mean absolute error (MAE) of 0.58 ± 0.41 mm within 85ms, facilitating real-time application. Furthermore, closed-loop control based on this method has achieved an end position error below 11% of the tip's total length, highlighting the accuracy and practicality of our approach for surgical interventions. © 2024 IEEE.

关键词： Closed loop control systems

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Simulation Analysis and Model Modification of the LWR Traffic Flow Theory

Simulation Analysis and Model Modification of the LWR Traffi...

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Yang Chen Jun Fu Anton Selivanov State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Department of Automatic Control and Systems Engineering University of Sheffield UK

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The surge in urbanization and ensuing traffic congestion underscores the imperative for contemporary traffic flow control models. This paper explores the Lighthill-Whitham-Richards (LWR) theory in the macroscopic domain of traffic flow control. It evaluates the accuracy and mathematical logic of simulation performance of related models, namely Greenshields, Newell-Daganzo, Greenberg, Underwood, and California. This paper employs thorough simulations that utilize a holistic three-dimensional(3D) analysis based on time, location, and density. It provides a comprehensive comparison and discusses specific features of each model. Additionally, a modification to the macroscopic model is proposed. The modified model overcomes the shortcomings of performing well only in some specific short ranges and achieves a full range of application effects. The study offers valuable guidance for real-life scenario selections and applications in the traffic domain.

关键词： Analytical models Accuracy Urban areas Transportation Predictive models Mathematical models Numerical models

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