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

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Real-Time Queue Length Estimation With Trajectory Reconstruction Using Surveillance Data

Real-Time Queue Length Estimation With Trajectory Reconstruc...

引用

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

作者： Zihao Sheng Shibei Xue Yunwen Xu Dewei Li Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

this paper presents a new method to estimate real-time queue lengths at a signalized intersection by utilizing limited data extracted from surveillance videos. this method focuses on reconstructing vehicles' trajectories on an entire road segment. the real-time queue length can be derived from these reconstructed trajectories. In order to improve the accuracy of the trajectory reconstruction, a built-up car-following model is proposed to reconstruct the trajectories of vehicles joining and leaving the queue, respectively, which are further corrected by a fusion algorithm. the proposed method was validated in the Next Generation Simulation dataset, and the queue length for each signal cycle can be estimated with a high accuracy. the results show that the proposed method has a higher precision compared to three baseline models.

关键词： Trajectory Surveillance Videos Real-time systems Image reconstruction Cameras Roads

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Consensus Kalman Filtering for Sensor Networks Based on FDI Attack Detection

Consensus Kalman Filtering for Sensor Networks Based on FDI ...

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

作者： Jiali Hao Ya Zhang School of Automation Southeast University and with Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

this paper studies the consensus Kalman filtering algorithm with distributed attack detection for reducing the effects of false data injection attacks in wireless sensor networks. the FDI attacks are randomly injected into communication channels or sensors with certain probabilities, which undermines the accuracy of the transmission data and the accuracy of measurement data respectively. χ 2 detector is applied, and if the received data is determined to be attacked, it is omitted in consensus Kalman filtering. It is proved that if the FDI attack is randomly injected into communication channels, under the consensus Kalman filtering algorithm with adaptive weighting protocol and attack detection, the estimation errors of the sensor network can be bounded in probability. If the FDI attack is randomly injected into sensors, a probability condition on the attacks is given to guarantee the estimation errors of the sensor network bounded in mean square sense. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms.

关键词： Covariance matrices Kalman filters State estimation Wireless sensor networks Robot sensing systems Distributed databases Detectors

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A Review of Machine vision Applications in Aerospace Manufacturing Quality Inspection

A Review of Machine Vision Applications in Aerospace Manufac...

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Computer, control and robotics (ICCCR), international conference on

作者： Li Deng Gang Liu Yilian Zhang School of Mechanical and Automotive Engineering Shanghai University Of Engineering Science Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Machine vision is a technology that uses image processing and analysis techniques to acquire and understand image information, enabling the recognition, measurement, and detection of objects. Machine vision is widely used in aerospace manufacturing for automated production, quality inspection, and robot guidance. It can improve the efficiency and quality of aerospace manufacturing, reduce labor costs and risks, promote innovation and optimization, adapt to various inspection needs, and realize intelligent, automated, and digital manufacturing processes. this paper introduces the types of vision technologies, overviews the typical applications of machine vision for quality inspection in aerospace manufacturing, (including component surface inspection, drilling quality inspection, assembly quality inspection, and gluing quality inspection), analyzes the advantages, challenges, and development trends of machine vision for quality inspection in aerospace manufacturing, and looks forward to future research directions to support further research and application.

关键词： Drilling Technological innovation Reviews Machine vision Inspection Market research Sensor systems

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A visual AGV-urban car using Fuzzy control

A visual AGV-urban car using Fuzzy control

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international conference on Autonomous Robots and Agents, ICARA

作者： Miguel A. Olivares-Mendez Ignacio Mellado Pascual Campoy Ivan Mondragon Carol Martinez Computer Vision Group Universidad Politénica de Madrid Madrid Spain

the goal of the work described in this paper is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. the only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30Hz. the good performance of the controller has successfully been demonstrated in a real environment at urban velocities. the presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensors.

关键词： Vehicles Wheels Visualization Cameras Humans Robots Software

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Comparison of camera-based and 3D LiDAR-based place recognition across weather conditions

Comparison of camera-based and 3D LiDAR-based place recognit...

引用

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

作者： Kamil Żywanowski Adam Banaszczyk Michał R. Nowicki Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan Poland

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Place recognition based on camera images provides excellent results on benchmarking datasets but might struggle in real-world adverse weather conditions like direct sun, rain, fog, or just darkness at night. In automotive applications, the sensory setups include 3D LiDARs that provide information complementary to cameras. the presented article focuses on the evaluation of camera-based, LiDAR-based, and joint camera-LiDAR-based place recognition. the processing for all data inputs is performed using a similar architecture of a neural network and is evaluated under varying weather conditions using the newly available USyd dataset. the experiments performed on the same trajectories in diverse weather conditions over 50 weeks prove that a 16-line 3D LiDAR can be used to supplement image-based place recognition to increase its performance. this proves that there is a need for more research into place recognition performed with multi-sensory setups.

关键词： Laser radar three-dimensional displays Cameras Meteorology Training Pipelines Image recognition

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A Global Perception Attention-based Network for Point Cloud Completion 20

A Global Perception Attention-based Network for Point Cloud ...

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20th IEEE international conference on Mechatronics and automation, ICMA 2023

作者： Wu, Fengyong Dong, Enzeng Tong, Jigang Yang, Sen Li, Wenyu Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Nankai University College of Artificial Intelligence 94 Weijin Road Tianjin China

ISBN: (纸本)9798350320831

the purpose of point cloud completion is designed to high-precision complete the complete shape from local observations. However, the previous approaches have tended to focus on a good perception of local details and lack a better overall representation of the point cloud. To solve this problem, we propose a global attention point cloud reconstruction method based on SnowflakeNet. We designed a global focus module to integrate the global structure information of the original point cloud on the basis of the local shape context features, so as to enhance the perception ability of the network to the local fine structure and the overall shape of the point cloud. At the same time, we improved the point cloud generation part of the network and added SPD that can re-optimize the position of the completion point cloud, so as to achieve more accurate completion results. We conducted a series of experiments on the Completion3D dataset and confirmed that our network outperformed the original algorithm. © 2023 IEEE.

关键词： Computer vision

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Model Predictive Path Integral control Framework for Partially Observable Navigation: A Quadrotor Case Study

Model Predictive Path Integral Control Framework for Partial...

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

作者： Ihab S. Mohamed Guillaume Allibert Philippe Martinet Université Côte d’Azur Inria France Université Côte d’Azur CNRS I3S France

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Recently, Model Predictive Path Integral (MPPI) control algorithm has been extensively applied to autonomous navigation tasks, where the cost map is mostly assumed to be known and the 2D navigation tasks are only performed. In this paper, we propose a generic MPPI control framework that can be used for 2D or 3D autonomous navigation tasks in either fully or partially observable environments, which are the most prevalent in robotics applications. this framework exploits directly the 3D-voxel grid acquired from an on-board sensing system for performing collision-free navigation. We test the framework, in realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks in a cluttered environment, for both fully and partially observable scenarios. Preliminary results demonstrate that the proposed framework works perfectly, under partial observability, in 2D and 3D cluttered environments.

关键词： Navigation Rotors Vehicle dynamics Task analysis Trajectory Two dimensional displays Real-time systems

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A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems

A Neuro-Inspired Approach to Solve a Simultaneous Location a...

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international conference on control, automation, robotics and vision

作者： Matheus Chaves Menezes Edison Pignaton de Freitas Sen Cheng Alexandre Cesar Muniz de Oliveira Paulo Rogerio de Almeida Ribeiro Graduate Program on Computer Science Federal University of Maranhão São Luís Brazil Graduate Program on Computer Science Federal University of Rio Grande do Sul Porto Alegre Brazil Computational Neuroscience Ruhr University Bochum Bochum Germany Department of Informatics Federal University of Maranhão São Luís Maranhão Department of Computer Engineering Federal University of Maranhão São Luís Maranhão

this paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. this proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. the work here reported extends this approach by merging partial information acquired by multiple robots. the results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots.

关键词： Navigation Simultaneous localization and mapping Feature extraction Robot vision systems Task analysis Hippocampus

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Swarm robotics Pattern Formation Algorithms

Swarm Robotics Pattern Formation Algorithms

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international conference on control, Mechatronics and automation (ICCMA)

作者： Mohamed H. Wagdy Hady A. Khalil Shady A. Maged Ain shams University Cairo Egypt

ISBN: (数字)9781728192109

ISBN: (纸本)9781728192116

Pattern formation is an important challenge in autonomous swarm robotics, it can drastically increase the efficiency of a group of robots and can be used in applications such as exploration and area coverage. Many pattern formation algorithms have been suggested with different constraints and assumptions of the robot's capabilities typically leaning towards robots with limited sensors meanwhile the proposed work relies on the current advancements in autonomous robots algorithms using robots with a wide range of sensors to tackle the pattern formation *** will present four main algorithms to solve the pattern formation problem mainly: peer detection, leader selection, pattern formation, and narrow path *** algorithms will be explained in this paper and are tested using ROS/Gazebo under different scenarios.

关键词： Robots Pattern formation Navigation Swarm robotics Sensors Robot vision systems Cameras

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A new k-NN based Open-Set Recognition method

A new k-NN based Open-Set Recognition method

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

作者： Xue-meng Hui Zhun-ga Liu Key Laboratory of Information Fusion Technology of Ministry of Education Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665476881

Traditional pattern classification methods can handle the objects whose categories are contained in the given (known) categories of training data. In open-set scenarios, objects to classify may belong to the ignorant (unknown) classes that is not included in training data set. Open-Set Recognition (OSR) tries to detect these unknown class objects and classify known class objects. In this paper, we propose a simple OSR method based on k-Nearest Neighbors (k-NNs). the test data (objects to classify) is put together with the labeled training data, so that the labeled training instances and the unlabeled objects to classify can appear in the k-NNs of other objects. then, the probability of object lying in the given classes can be determined according to the k-NNs of this object. If the labeled training data is the majority of k-NNs, this object most likely belongs to one of the given classes, and the distances between the object and its neighbors are taken into account here. then the objects with high probability are marked with the estimated probability. However, if most of k-NNs are the unlabeled test objects, the class of object cannot be classified in this step because the k-NNs of the object is uncertain. In this paper, the probability of the other uncertain objects belonging to known classes is re-calculated based on the labeled training instances and the objects marked with the estimated probability. Such iteration will not stop until all the probabilities of objects belonging to known classes are not changed. then, the Otsu's method is employed to obtain the optimal threshold for the final recognition. If the probability of object belonging to known classes is smaller than this threshold, it will be assigned to the unknown class. the other objects will be considered as known classes and then committed to a specific class by a pre-trained classifier. the effectiveness of the proposed method has been validated using some experiments.

关键词： Training automation Sensitivity analysis Training data Pattern classification Robot sensing systems

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