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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861-870 订阅

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Multi-Sensor H∞ Filter Design for Networked control Systems with Unknown Communication Delays 16

Multi-Sensor H∞ Filter Design for Networked Control Systems...

引用

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

作者： Nartey, George Zhang, Duanjin Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

ISBN: (纸本)9781728177090

In this paper, the problem of multi-sensor H-infinity filter for networked control systems with unknown communication delays is investigated. A filter for the unknown communication delays system is developed to guarantee the exponentially mean-square stability of the closed-loop system provided its equivalent robust filter is designed. the measured output is prone to sensor saturation which has sector-nonlinearities capability to measure the physical plant. the unknown communication delays are considered in the sensor to the controller link. Given the limitations in communication delays, a filtering scheme is designed via linear matrix inequalities (LMIs) and Lyapunov stability theory. A simulation result is given to prove the effectiveness of the proposed method.

关键词： Networked control systems

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Efficient Asynchronous Vertical Federated Learning via Gradient Prediction and Double-End Sparse Compression 16

Efficient Asynchronous Vertical Federated Learning via Gradi...

引用

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

作者： Li, Ming Chen, Yiwei Wang, Yiqin Pan, Y. Zhejiang Univ Coll Control Sci & Engn Inst Cyber Syst & Control Hangzhou Peoples R China

ISBN: (纸本)9781728177090

Vertical federated learning is a subset of federated learning whose training dataset is vertically distributed among the federations. However, as a natural synchronous algorithm, classical vertical federated learning suffers from "Liebig's Law". In this paper, we propose a novel asynchronous vertical federated learning framework with gradient prediction and double-end sparse compression to accelerate the training process and reduce the intermediate result transmission. Our simulation results show that our framework can achieve 65.41% training acceleration and 86.90% traffic volume reduction at no cost of accuracy compared with the classical framework.

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Gaussian Process Model Predictive control Based Cooperative Motion Planning Structure for UAGVS 16

Gaussian Process Model Predictive Control Based Cooperative ...

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

作者： Sun, Pei Que, Haoyi Zhou, Yiming Shenzhen Polytech Inst Intelligence Sci & Engn 4089 West Shahe Rd Shenzhen Peoples R China

ISBN: (纸本)9781728177090

Gaussian process (GP) models based model predictive control (MPC) structure for cooperative motion planning of Unmanned Aerial and Ground Vehicle System (UAGVS) is proposed in this article. the GP models are firstly trained to describe the dynamics of UAVs and UGVs with their uncertainties. Stochastic optimization problems are designed for motion planning and controlling based on the GP models' probabilistic predictions. And for the necessary interactions among agents, the predicted motion information is also communicated with each other, so that they can obtain others' future planned behaviors. Simulation results show that the proposed method exhibit promising effects of motion planning for UAVGS.

关键词： Motion planning

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Design and development of a shape measurement system for 3D construction printing with a manipulator

Design and development of a shape measurement system for 3D ...

引用

IEEE international conference on automation Science and Engineering (CASE)

作者： Reina Nakanishi Hiroki Ogura Shinya Yamamoto Hiroyuki Abe Ryu Kikuchi Robotics Research Center Institute of Technology Shimizu Corporation Japan Center for Social System Engineering Institute of Technology Shimizu Corporation Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

We describe a 3D construction printing system comprising an industrial robot with a six-degrees-of-freedom manipulator and a pump that supplies mortar. Printing is realized by linking the manipulator to the pump. To measure and evaluate printed object shapes, we attached a time-of-flight camera with ±2-mm measurement accuracy to an end effector mounted on the manipulator, thereby acquiring point-cloud data for printed objects. Using software developed to control the manipulator, and an open-source robotics middleware suite as the robot operating system, we merge the obtained point clouds. After printing mortar objects, we use the developed system to measure their shapes. Experimental results show that point clouds for printed objects have errors of 0 to 10 mm, while data from a conventional stationary 3D laser scanner has measurement accuracy of ±1 mm. Although merged point clouds show increased errors, measurement times decreased to approximately one-sixth those of conventional scanners. the increased error is likely due to factors such as the manipulator’s weight and its installation at the end effector and the sensor. We use a total station to measure deflections to confirm the manipulator’s positioning accuracy.

关键词： Printing Point cloud compression three-dimensional displays Accuracy Shape Mortar Shape measurement Measurement uncertainty Robot vision systems End effectors

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Predefined Finite-time Output Containment of Nonlinear Multi-Agent Systems with Undirected Topology 16

Predefined Finite-time Output Containment of Nonlinear Multi...

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

作者： Wang, Qing Zhou, Shiyu Zhou, Siquan Dong, Xiwang Yu, Jianglong Ren, Zhang Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China

ISBN: (纸本)9781728177090

this paper focuses on the finite-time output containment problem for a kind of nonlinear multi-agent systems with multiple dynamic leaders. Firstly, considering the topological structure among the followers, a kind of adaptive distributed observer is designed to estimate the whole states of all the leaders. By utilizing common Lyapunov theory, the finite-time convergence of proposed distributed observer is proved. On the basis of this conclusion, a containment control protocol including the desired convex combinations of the leaders is developed for each follower by using the given weights. With the help of the output regulation theory, the finite-time output containment criterion for nonlinear multi-agent systems is derived. Finally, a numerical example is presented to demonstrate the effectiveness of the theoretical results.

关键词： Multi agent systems

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vision-Based Flexible and Precise Automated Assembly with 3D Point Clouds 9

Vision-Based Flexible and Precise Automated Assembly with 3D...

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9th international conference on control, Mechatronics and automation (ICCMA)

作者： Chang, Wen-Chung Lin, Yu-Kai Pham, Van-Toan Natl Taipei Univ Technol Dept Elect Engn Taipei 106 Taiwan Natl Taipei Univ Technol Grad Inst Automat Technol Taipei 106 Taiwan

ISBN: (纸本)9781665410731

Cartesian-based visual servoing with finite-time control employing deep-learning-based partial point cloud registration for automated robotic picking and assembly is proposed in this paper. A partial point cloud registration architecture including deep-learning-based and Iterative Closest Point (ICP) algorithm for rough and precise registration is developed to resolve the time-consuming matching problem. Specifically, the deep-learning-based algorithm employs a descriptor to downsample and extract rough corresponding points that works as input to convolutional neural networks to estimate transformation between two point clouds. then, ICP approach is applied to refine the alignment between them. For visual servoing, two kinds of controllers including Cartesian-based visual servoing and finite-time visual servo control are developed to drive the end-effector of the manipulator to reach desired pose for robotic picking and assembly. In addition, a pure force controller is also synthesized and implemented to resolve robotic insertion based on force/torque sensing to improve flexibility in handling automated robotic manufacturing in the proposed system. Finally, the performance of the proposed approaches has been verified by experiments with an industrial manipulator in velocity servo mode. Based on the analysis and experimental results in this paper, it appears to have demonstrated potential contributions and industrial applications in intelligent and flexible robotics manufacturing based on deep learning.

关键词： 3D partial point cloud registration assembly automated manufacturing convolutional neural networks deep learning eye-in-hand finite-time control force control hand-eye calibration robotic manipulator robotic picking visual servoing

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A collaborative relative localization method for vehicles using vision and LiDAR sensors 16

A collaborative relative localization method for vehicles us...

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

作者： Li, Yanhao Li, Hao Shanghai Jiao Tong Univ SJTU Sch SPEIT Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ SJTU Dept Automat Shanghai 200240 Peoples R China

ISBN: (纸本)9781728177090

Inter-vehicle relative localization is essential to various multi-vehicle collaborative tasks such as environment perception augmentation, long range path planning and communication-assisted driving applications. We propose a real-time collaborative relative localization framework to estimate inter-vehicle poses via vision and LiDAR sensors. the vision sensor is used for inter-vehicle pose initialization, whereas the LiDAR sensor is used for SLAM and pose refinement. A comparative study between our proposed method and a representative baseline method based on ORB-SLAM2-stereo is performed in the CARLA simulator, which demonstrates advantages of the proposed method in terms of accuracy and robustness in real-time operation.

关键词： Vehicles

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Hybrid Multi-Robot System for Drilling and Blasting automation 16

Hybrid Multi-Robot System for Drilling and Blasting Automati...

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

作者： Dac Dang Khoa Nguyen Lai, Yujun Sutjipto, Sheila Paul, Gavin Univ Technol Sydney UTS Fac Engn & Informat Technol Ctr Autonomous Syst CAS Sydney NSW Australia

ISBN: (纸本)9781728177090

Multi-robot systems possess the potential of becoming the next generation of robots in the mining industry due to their robustness and scalability. However, they present challenges for the system to efficiently allocate tasks to each robot and allow them to navigate toward their targets safely. this paper introduces a hybrid approach method for a multi-robot system, alongside with a case study in drilling and blasting automation. A Centralized control Unit delegates tasks and information among the robots in the system, each equipped with a decentralized motion planner that supports cooperative inter-robot collision avoidance. the proposed system inherits the advantage of a centralized multi-robot system in providing a time-wise optimal solution;while also possessing the computational benefit and scalability of a decentralized system. Simulations were conducted to validate the proposed method and discuss insights into the efficacy and performance of the proposed method.

关键词： Multipurpose robots

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Dynamic event-triggered control for discrete-time Markov jump systems 16

Dynamic event-triggered control for discrete-time Markov jum...

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

作者： Zhang, Yueyuan Chen, Yiyang Soochow Univ Sch Mech & Elect Engn Suzhou 215137 Peoples R China

ISBN: (纸本)9781728177090

this paper concentrates on the dynamic event trigger based controller design for discrete-time Markov jump systems. the rich data transmission within the network channel may cause the collision and the waste of resources, which leads to some kinds of poor system performance. Firstly, a discrete time domain dynamic event trigger mechanism is proposed for controller design. Secondly, for discrete time Markov jump systems, a mode-dependent Lyapunov function is designed to ensure the system state to be exponentially mean-square ultimately bounded by linear matrix inequalities (LMIs). Finally, a numerical simulation example is conducted to illustrate the effectiveness of the provided method.

关键词： Linear matrix inequalities

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RGB-D-based Human Detection and Segmentation for Mobile Robot Navigation in Industrial Environments 16

RGB-D-based Human Detection and Segmentation for Mobile Robo...

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16th international Joint conference on Computer vision, Imaging and Computer Graphics theory and Applications (VISIGRAPP) / 16th international conference on Computer vision theory and Applications (VISAPP)

作者： Kedilioglu, Oguz Lieret, Markus Schottenhamml, Julia Wurfl, Tobias Blank, Andreas Maier, Andreas Franke, Joerg Friedrich Alexander Univ Erlangen Nurnberg FAU Inst Factory Automat & Prod Syst D-91058 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Pattern Recognit Lab D-91058 Erlangen Germany

ISBN: (纸本)9789897584886

Automated guided vehicles (AGV) are nowadays a common option for the efficient and automated in-house transportation of various cargo and materials. By the additional application of unmanned aerial vehicles (UAV) in the delivery and intralogistics sector this flow of materials is expected to be extended by the third dimension within the next decade. To ensure a collision-free movement for those vehicles optical, ultrasonic or capacitive distance sensors are commonly employed. While such systems allow a collision-free navigation, they are not able to distinguish humans from static objects and therefore require the robot to move at a human-safe speed at any time. To overcome these limitations and allow an environment sensitive collision avoidance for UAVs and AGVs we provide a solution for the depth camera based real-time semantic segmentation of workers in industrial environments. the semantic segmentation is based on an adapted version of the deep convolutional neural network (CNN) architecture FuseNet. After explaining the underlying methodology we present an automated approach for the generation of weakly annotated training data and evaluate the performance of the trained model compared to other well-known approaches.

关键词： Image Segmentation Object Recognition Neural Networks Deep Learning robotics Autonomous Mobile Robots Flexible automation Warehouse automation

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