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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

共 5313 条记录，以下是911-920 订阅

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Event-Triggered Remote Dynamic control for Network control Systems 16

Event-Triggered Remote Dynamic Control for Network Control S...

引用

16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Li, Haoyun Fan, Yuan Pan, Gaofeng Song, Cheng Anhui Univ Sch Elect Engn & Automat Anhui Engn Lab Human Robot Integrat Syst & Intell Hefei 230601 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

ISBN: (纸本)9781728177090

this work studies the event-triggered control problem for networked control systems. the plant is controlled directly by a dynamic local controller, which receives the reference control signal from the remote controller. the measurement signal of the plant and the reference control signal of the remote controller has their separate event trigger, and thus the remote controller and the local controller can decide when to transmit signals on their own. It is proved that with the proposed control approach and the dual event trigger, the closed loop system is globally asymptotically stable, which has been illustrated by simulation results.

关键词： Event-Triggered control Dynamic controller Network control System Remote control

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A Pedestrian Re-Identification Method Integrating ResNet and Residual Self-Attention Mechanism

A Pedestrian Re-Identification Method Integrating ResNet and...

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robotics, Intelligent control and Artificial Intelligence (RICAI), international conference on

作者： Jianxing Deng Hanzhao Pan Huibin Luo Zhansen Jiao Beijing Institute of Technology Zhuhai China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Person Re-identification is an important task in the field of computer vision, which aims to identify the same person across cameras and scenes. However, due to the influence of perspective changes, illumination differences, occlusion and other factors, person re-identification still faces great challenges. In order to solve these problems, this paper proposes a deep learning method combining ResNet and residual self-attention mechanism to improve the robustness and feature expression ability of the model for complex scenes. In this method, the ResNet-18 network is used as the backbone, and a self-attention module is embedded in each residual block to enhance the network's ability to perceive global context information by learning the relationship between different positions in the feature map. At the same time, the output of the self-attention module is fused by residual connection, which can not only effectively avoid gradient disappearance, but also retain the original feature information and promote the training of the deep network. Experimental results on the public dataset Market-1501 demonstrate that the proposed method achieves mAP and accuracy scores of 95.21% and 93.01%, respectively, outperforming existing methods. these results confirm the effectiveness of the proposed approach in the person reidentification task.

关键词： Training Pedestrians Accuracy Lighting Network architecture Robustness Convolutional neural networks Identification of persons Context modeling Intelligent control

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Configuration Estimation of Continuum Robots Using Piecewise Constant Curvature Generalized Epi-polar Constraint Model 17

Configuration Estimation of Continuum Robots Using Piecewise...

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17th IEEE international conference on automation Science and Engineering (CASE)

作者： Cheng, Hao Liu, Houde Wang, Xueqian Liang, Bin Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781665418737

In recent years, continuum robots have attracted more attention for they can work in more severe environments. However, at present, most of the research focuses on the mechanical structure innovation, and there are few pieces of research on the control of this kind of robot. Since continuum robots are deformable, their shape is a general curve in space. therefore, they are not fully defined by actuator positions, which are different from the traditional rigid robots. To achieve more accurate control, a method of sensing robot configuration in real-time is necessary. However, the existing visual-based approaches all adopt external global cameras, which is difficult to adapt to the demand of unknown unstructured environments. this paper presents a system capable of estimating the configuration of continuum robots under piecewise constant curvature (PCC) assumption from cameras mounted on each constant curvature segment. Specifically, we first proposed the PCC 2R model, which is equivalent to each cc-segment of PCC continuum robots by two joints rigid bodies, thereby reducing the problem complexity and improving the numerical stability of the estimation. then, based on the PCC 2R model, we proposed the PCC generalized epi-polar constraint to completely constrain the four degrees of freedom of each cc-segment in planar, it can be solved through one corresponds, to estimate the configuration of continuum robots under PCC. Finally, the above approach is verified by experiment.

关键词： Technological innovation Shape Motion segmentation Robot vision systems Estimation Cameras Real-time systems

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An Assist-as-Needed Variable Admittance controller for Adaptive Support of Mobile Walking Assistive Robots

An Assist-as-Needed Variable Admittance Controller for Adapt...

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international conference on Mechatronics and Machine vision in Practice (M2VIP)

作者： Xinxing Chen Yue Zheng Zongming Su Yuquan Leng Jian Huang Chenglong Fu Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Technology Planning Department Huawei Technologies Shenzhen China Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Mobile walking assistive robots have been widely used for monitoring patients’ gait patterns and providing support to prevent falls. Traditional approaches often separate the operational modes of these robots into human-following and supporting modes, which can lead to discontinuities in assistance and potentially compromise safety. this paper proposes a novel assist-as-needed control strategy that incorporates a Balance Index (BI) to dynamically adjust the robot’s support based on the user’s balance capability. the BI is calculated using the interaction force between the user and the robot along the z-axis and the gait symmetry index (GSI). the proposed variable admittance controller modifies its parameters based on the BI, ensuring seamless transitions between compliance and support modes. Experimental validation was conducted with a healthy subject walking both with and without a knee fixation brace. the results demonstrate that the robot provides minimal interference during natural gait and increased support during impaired gait, validating the effectiveness of the proposed control strategy in enhancing user safety and promoting natural walking patterns.

关键词： Legged locomotion Force Stability criteria Interference Assistive robots User experience Safety Admittance Indexes Robots

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MathCLM: Mathematical Cognitive Learning Model Based on the Evolution of Knowledge Graph

MathCLM: Mathematical Cognitive Learning Model Based on the ...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Gongqi Lin Xiuqin Zhong Hongguang Fu University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665476881

In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a challenging mission. However, such integration may lead to a better understanding, practice, and construction of more realistic models, especially for mathematical learning. Unfortunately, existing models are either oversimplified or require much processing time, which is unsuitable for online learning and education. therefore, we propose a novel cognitive learning model, called Mathematical Cognitive Learning Model (MathCLM) based on the evolution of knowledge graph, for online mathematical learning that seeks to effectively represent, learn, and reason in online learning environments. the model's architecture combines cognitive learning with symbolic knowledge representation based on natural language processing (NLP). We introduce the mathematical instance concept to build the strategies by mathematical knowledge, such as theorems, axioms, etc., and infer new custom instances based on the learning knowledge. Furthermore, it can deal with uncertainty and errors from instances recommendation using a graph matching model and displays the inference progressing with different combinations of instances. We build a platform to promote and validate our model. the validation of the model on the real-world platform and the results presented here indicate the promise of the approach when performing online learning and reasoning in real-world scenarios, with possible applications in various areas.

关键词： Visualization Uncertainty automation Education Knowledge representation Mathematical models Natural language processing

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Higher-order Polynomial Signal Tracking control of Unknown Systems Using Off-policy Integral Reinforcement Learning 16

Higher-order Polynomial Signal Tracking Control of Unknown S...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Cheng, Weiran Li, Jinna Liaoning Shihua Univ Sch Informat & Control Engn Fushun 113001 Liaoning Peoples R China

ISBN: (纸本)9781728177090

this paper aims at using an off-policy integral reinforcement learning (IRL) algorithm to solve the linear quadratic tracking (LQT) control problem of completely unknown continuous-time systems, such that an arbitrary higher-order polynomial signal can be followed via an optimal approach. Firstly, a linear continuous-time system with unknown model matrices is introduced with a target of tracking the reference signal with higher-order polynomials. Secondly, based on the knowledge of the on-policy IRL, an off-policy IRL algorithm is used to solve the derived iterative Bellman equation and update the control policy resulting in the optimal control policy. Finally, a simulation example is given to show the efficiency of the proposed approach.

关键词： Reinforcement learning

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Intercellular Movement Tracking for Damage Assessment during Cell Micromanipulation 11

Intercellular Movement Tracking for Damage Assessment during...

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11th IEEE Annual international conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2021

作者： Chen, Yizhe Du, Yue Liu, Yaowei Zhao, Qili Sun, Mingzhu Cui, Maosheng Zhao, Xin Inst. of Robotics and Automat. Info. System and the Tianjin Key Laboratory of Intelligent Robotic Nankai University Tianjin300350 China Institute of Animal Science and Veterinary of Tianjin Tianjin300381 China

ISBN: (纸本)9781665425278

Robotic cell micromanipulation that is designed for delicate operation has brought new prospects for enhancing cell developmental potential. However, mechanical damage of cells during robotic micromanipulation cannot be ignored such that it compromises the effort of the system in improving cell viability. therefore, cell damage assessment is particularly important, but also a great challenge due to the complexity of the composition of a cell and its functions. In this paper, a novel cell damage assessment method was proposed, relying on the sensing of mitochondrial micron-scale displacement and intracellular strain. Mitochondria in living cells were stained and used as physical markers to avoid the shortcomings of introducing physical markers into cell body. We designed a quantitative marker to characterize cell mechanical damage during the penetration process. the experimental result shows that the high-strain damage is intensified as the penetration proceeds. the maximum displacement and mean displacement were respectively 3.87 pixels (2.42\mu m) and 0.78 pixels (0.49\mu m). the maximum strain and mean strain were respectively 0.107 and 0.0143 between two adjacent frames with the micropipette speed of 10\mu m/s. the maximum displacement measurement error was from 0.294 before optimization to 0.065 after optimization. the quantitative damage marker was sufficiently sensitive to sense the different levels of damage at different time sequences. At the penetration proceeds, the value of such marker at the high-strain region was several times higher than that of the low-strain region. the proposed method has implication for improving the biological efficacy of the robotic micromanipulation. © 2021 IEEE.

关键词： robotics

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Hierarchical decomposition and prescribed performance bound based adaptive control for leader-follower formation of uncertain nonholonomic mobile robots 16

Hierarchical decomposition and prescribed performance bound ...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Shen, Jiajun Wang, Wei Lu, Jinhu Deng, Chao Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781728177090

In this paper, the cooperative formation control problem is investigated for multiple two-wheeled mobile robots with unknown parameters. By combining the hierarchical decomposition and prescribed performance bound (PPB) technique, distributed adaptive formation controllers are designed based on dynamic surface control. It is proved in the Lyapunov sense that all the closed-loop signals are semi-globally bounded and the tracking error converges to a compact set, which can be made arbitrarily small by adjusting the design parameters appropriately. Furthermore, formation maintenance, connectivity preservation and collision avoidance can be achieved with the proposed control scheme. Simulations are also given to verify the effectiveness of the theoretical results.

关键词： Mobile robots

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Novel Position Falsification Attacks Detection in the Internet of Vehicles using Machine Learning

Novel Position Falsification Attacks Detection in the Intern...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Harun Surej Ilango Maode Ma Rong Su School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Engineering Qatar University Qatar

ISBN: (纸本)9781665476881

In an Internet of Vehicles (IoV) network, vehicles periodically broadcast Basic Safety Messages (BSMs) that contain the vehicle's current position, speed, and acceleration. Safety-critical applications like blind-spot warning and lane change warning systems use these BSMs to ensure the safety of road users. However, an attacker can affect the efficacy of such applications by injecting false information into the messages. One such attack is the position falsification attack, where the attacker inserts incorrect information regarding the vehicle's position in the BSMs. the literature has explored the use of Misbehavior Detection Systems (MDSs) to detect position falsification attacks. But the limitation of the existing MDSs is that they are signature-based and require prior knowledge about the attacks for effective detection. To overcome this shortcoming, we propose a Novel Position Falsification Attack Detection System for the Internet of Vehicles (NPFADS for the IoV)that learns and detects new position falsification attacks emerging in IoV networks. the performance of NPFADS is quantitatively measured using the metrics precision, recall, F1 score, and ROC. the Vehicular Reference Misbehavior (VeReMi) dataset is used as the benchmark to analyze the performance of NPFADS. the performance of NPFADS is compared to existing MDSs in the literature, and the analysis shows that NPFADS performs on par with the existing signature-based detection models even when initialized with zero initial knowledge.

关键词： Measurement Analytical models automation Roads Benchmark testing Alarm systems Safety

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Intuitive Virtual Reality based control of a Real-world Mobile Manipulator 16

Intuitive Virtual Reality based Control of a Real-world Mobi...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Dinh Tung Le Sutjipto, Sheila Lai, Yujun Paul, Gavin

ISBN: (纸本)9781728177090

this paper presents an integration of Virtual Reality (VR) interfaces with the control system of a real-world mobile manipulator, ultimately facilitating a natural and intuitive method for human-robot interaction. VR's ability to track movements in 3D space and translate performed motions provide an intuitive platform for users to explore and interact with the virtual environment. Coupled with intuitive controls, such as grabbing and pointing, the VR platform provides a compelling advantage that can be used to solve limitations of traditional remote robot teleoperation methods. this paper summarises the system implemented, which includes a simulation of the robot in Unity3d, as well as analyses critical results of accuracy and performance, from experiments with users of various experience levels. the method used for measuring accuracy with a simulated robot presented a utilitarian validation for contrasting the difference between 2D and VR 3D interfaces. Users' performance and experience under various levels of control latency, which is a crucial factor in remote online robot control, were also measured.

关键词： Virtual reality

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