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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801-810 订阅

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Trust Management Framework for Misbehavior Detection in Collective Perception Services

Trust Management Framework for Misbehavior Detection in Coll...

引用

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

作者： Jiahao Zhang Ines Ben Jemaa Fawzi Nashashibi IRT SystemX Paris France INRIA - Team ASTRA Paris France

ISBN: (纸本)9781665476881

Collective Perception Messages (CPM) enable vehicles to share their perceived objects with their neighbors in V2X network. these perception data extend local vehicles' perception and consequently improve road safety awareness. However, attacks on perception data are challenging and require advanced and efficient misbehavior detection mechanism especially in specific road scenarios where contradictory information need to be analysed. In this work, we introduce a trust management framework to detect misbehaving nodes through transmitted CPM messages. Our framework is based on trust assessment built through several processing steps. It addresses conflict situation when contradictory data are received using the Subjective Logic mechanism. the results show that our solution is effective in detecting misbehaving nodes based on their attributed trust scores. In addition, we show the impact of our solution and some CPM configuration parameters on safety services and especially on risk anticipation in intersection scenarios.

关键词： Uncertainty automation Simulation Scalability Robot sensing systems Road safety Safety

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Distributed State Estimation for State-Saturated Power Systems under Denial-of-Service Attacks 16

Distributed State Estimation for State-Saturated Power Syste...

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

作者： Chen, Wei Wang, Xueli Mao, Jingyang Ding, Derui Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China

ISBN: (纸本)9781728177090

In this paper, the distributed state estimation problem is studied for a class of state-saturated power systems subject to Denial-of-Service (DoS) attacks. the randomly occurring DoS attacks is modeled by a series of Bernoulli distributed stochastic variables with known probability distributions. the aim of this paper is to design a distributed estimator ensure, in the presence of both cyber-attacks and state saturations, the desired estimation performance is satisfied. By virtue of some typical matrix inequalities, a tight upper bound of estimation error covariance is derived. the estimation parameters are obtained with the help of the solution of a set of Riccatilike difference equations. the developed recursive algorithm is independent of the global information and thus satisfies the requirements of scalability and online application. Finally, a practical example is developed to verify the validity of the designed estimator.

关键词： Power systems distributed estimator state saturation DoS attack recursive algorithm

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Improved LMI-based conditions for designing of PD-type ILC laws for linear batch processes over two-dimensional setting

Improved LMI-based conditions for designing of PD-type ILC l...

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

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Shoulin Hao Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9781665476881

this paper considers the problem of designing of iterative learning control (ILC) laws for linear batch processes. Unlike the majority of existing results about ILC law design for linear batch processes over repetitive/two-dimensional setting where Lyapunov theory is applied, this study is focused on formulating the ILC law design procedures by transforming it into an equivalent problem of (structural) stability analysis for a linear Roesser model for two-dimensional (2D) systems. then, based on a non-conservative version of stability and stabilization conditions for linear 2D systems, suitable PD-type ILC laws are derived by the application of the linear matrix inequality (LMI) approach. Finally, a numerical example is given to show the validity of the proposed design procedure and some advantages are emphasized when compared to the existing alternatives.

关键词： State feedback Uncertainty Two dimensional displays Batch production systems Stability analysis Structural engineering Numerical models

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Data Transmission Resilience to Cyber-attacks on Heterogeneous Multi-agent Deep Reinforcement Learning Systems

Data Transmission Resilience to Cyber-attacks on Heterogeneo...

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

作者： Neshat Elhami Fard Rastko R. Selmic Department of Electrical and Computer Engineering Concordia University Montreal QC Canada

ISBN: (纸本)9781665476881

this paper investigates the data transmission resilience between agents of a cluster-based, heterogeneous, multi-agent deep reinforcement learning (MADRL) system under gradient-based adversarial attacks. We propose an algorithm using a deep Q-network (DQN) approach and a proportional feedback controller to defend against the fast gradient sign method (FGSM) attack and improve the DQN agent performance. the feedback control system is an auxiliary tool that helps the DQN algorithm reduce system deficiencies. In accordance with the achieved results and under FGSM adversarial attack, the resilience of the developed system is evaluated in three different ways termed robust, semi-robust, and non-robust based on average reward and DQN loss. the data transfer is carried out between agents of a MADRL system in timely and time-delayed manners, for both leaderless and leader-follower scenarios. Simulation results are included to verify the presented results.

关键词： Deep learning Simulation System performance Clustering algorithms Process control Reinforcement learning Propagation losses

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Center Keypoint for Parking Slot Detection with Self-Calibrated Convolutions Network

Center Keypoint for Parking Slot Detection with Self-Calibra...

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

作者： Ruitao Zheng Shikang Lian Weihao Liang Yaze Tang Wei Meng

ISBN: (纸本)9781665476881

Available parking slot detection is the first step for autonomous parking systems. In this paper, we propose a novel parking slot detection method that uses the center information regression and the occupancy classification of the parking slot. We design a self-calibrated convolutions network (SCCN) to obtain the position, length, occupancy and direction, which can also infer the parking slot type according to the prediction results. the method divides an around view monitor (AVM) image into the 16 × 16 grid cells and performs a SCCN detector for feature extraction. Subsequently, the whole parking slot can be easily inferred via prior geometric information and detection results. We adopt the heatmap, MultiBins, and midline to detect the center keypoint, direction and occupancy, respectively. And we quantitatively evaluate the performance of the proposed method on the public PS2.0 datasets. the experimental results show the outperformance by a precision rate of 99.35%, a recall rate of 99.17%, an occupancy classification accuracy of 99.12% and all correctly inferred types of parking slots on the datasets.

关键词： Heating systems automation Detectors Feature extraction Reliability Data mining Robots

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Where to look for tiny objects? ROI prediction for tiny object detection in high resolution images

Where to look for tiny objects? ROI prediction for tiny obje...

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

作者： Aleksandra Kos Karol Majek Dominik Belter Cufix 05–825 Grodzisk Mazowiecki Poland Institute of Robotics and Machine Intelligence Poznan University of Technology Poznań Poland

ISBN: (纸本)9781665476881

In this paper, we focus on the detection of tiny objects. the goal is to improve the performance of tiny object detection while preserving the average precision and recall metrics compared to a brute-force, sliding-window approach. We extend the object categories in COCO detection metrics from small, medium, and large by defining tiny, very tiny, and micro-objects. We propose an evaluation protocol for all six object sizes. To detect tiny objects, we offer a novel ROI proposal method based on a two-level nested U-structure architecture U 2 -Net. For this purpose, we experiment with multiple dilation techniques as well as Region of Interest (ROI) aggregation methods. We evaluate our method using the Mapillary Traffic Sign Dataset. the obtained detection strategy outperforms the single-step prediction approach and is comparable to the quality obtained with the use of the sliding-window, being nearly 7 times faster.

关键词： Measurement Protocols Image resolution automation Object detection Proposals Robots

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Multimodal Image Matching using Phase Congruency-based Self-Similarity Structural Features

Multimodal Image Matching using Phase Congruency-based Self-...

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

作者： Jianwei Fan Qing Xiong Jian Li Guichi Liu Wanying Song School of Computer and Information Technology Xinyang Normal University Xinyang China Xi'an Key Laboratory of Heterogeneous Network Convergence Communication Xi'an University of Science and Technology Xian China

ISBN: (纸本)9781665476881

Due to the significant differences in geometric and nonlinear intensity, multimodal image matching is still a challenging problem. To address this issue, this paper proposes a novel matching method using phase congruency (PC)-based self-similarity structural features for multimodal images. Firstly, the feature points are extracted from the PC maps of the original images by the Harris detector. then, combined with the theory of the self-similarity, a PC-based self-similarity structural (PCSS) descriptor is designed for multimodal images. Finally, the Euclidean distance is used as the matching measure for the corresponding point recognition. Experimental results conducted on various real multimodal image pairs demonstrate that the proposed method can achieve better matching performance in terms of the number of correct matches and the registration precision in comparison with the traditional methods.

关键词： Matched filters automation Image matching Euclidean distance Detectors Feature extraction Filtering theory

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Simulated Framework for Physical Human-Robot Collaboration to Co-Manipulate Objects

Simulated Framework for Physical Human-Robot Collaboration t...

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

作者： Martin Mujica Mourad Benoussaad Jean-Yves Fourquet LAAS-CNRS University of Toulouse Toulouse France LGP-ENIT University of Toulouse Tarbes France

ISBN: (纸本)9781665476881

Physical Human-Robot Interaction (pHRI) is an important field that has grown considerably over the last years. Nowadays, new possible applications are being evaluated. In order to tackle the new issues that might arise, novel methods for robot control have to be used. To ease the comparison and evaluation of those methods, this work proposes a simulated frame-work to study the collaboration between a robot manipulator and a person while moving payloads. the way of conceiving the simulated framework done in Matlab is presented, detailing how the robot, the object, and the person can be described. Experiments are done on a real robot KUKA LBR iiwa 14 R820 to compare and validate the interaction forces predicted by the proposed framework. Results showed that this method allows to simulate the co-manipulation of objects, even with unknown load, showing forces that are close to the real ones applied by the person. the Matlab code is publicly available at https://***/MMujica/simulated_framework_phrc.

关键词： Codes Robot control Collaboration Human-robot interaction Manipulators Behavioral sciences Task analysis

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Design of a Tendon-Actuated Foldable Wheeled-Legged Hybrid Mobile Robot with high load-bearing capacity

Design of a Tendon-Actuated Foldable Wheeled-Legged Hybrid M...

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

作者： Yinan Yang Chongyang Zhang Wenfu Xu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen P.R. China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665476881

Mobile robots that can carry heavy loads play an important role in transporting and exploring in unstructured environment. However, there are some problems existing in conventional motor direct-drive robots such as excessive joint torque and unfoldable legs when they are under heavy loads. this paper proposes a tendon-actuated foldable wheeled-legged robot with high load-bearing capacity. A spreader is attached to the knee joint so that the output torque of the motor can be amplified by pulling the end of the spreader with a cable. the single and double fixed pulleys are used to reduce the required cable tension and avoid the interference of different cables, which can better achieve the folding of legs. In addition, the statics and kinematics of the tendon-actuated leg and the formulas for calculating the required tension and motor torque, as well as the relationship between foot velocity, joint speed, and motor speed are given. Finally, Matlab is used for simulation analysis, and it is found that the joint torques required for the tendon actuation are lower than for the direct actuation at the same joint angles.

关键词： Legged locomotion Knee Torque Pulleys Kinematics Mechanical cables Static analysis

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Feature engineering techniques for skeleton-based two-person interaction classification in video

Feature engineering techniques for skeleton-based two-person...

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

作者： Sebastian Puchała Włodzimierz Kasprzak Paweł Piwowarski Warsaw University of Technology Institute of Control and Computation Engineering Warszawa Poland

ISBN: (纸本)9781665476881

An LSTM-based approach to two-person interaction classification in image sequences, based on human skeleton detection in single frames, is proposed. the main contribution is the design of different preliminary feature extraction techniques from many skeleton data sets. the approach consists of four main stages. First, we employ the OpenPose engine in order to estimate silhouettes of persons in single video frames. Next, the skeleton data sets are tracked over time and particular joints are corrected whenever needed. then, three different versions of features are defined, based on the stream of refined skeleton joints. In the final stage, a classifier network, based on LSTM, is developed and experimentally studied. the models are trained and evaluated on the interaction subset of the NTU RGB+D data set. the most efficient model achieved a classification accuracy of 94.5% (in the CV mode). this is an excellent performance, comparable with the best reported Graph CNN-based classifiers for this subset.

关键词： Legged locomotion Learning systems Process control Feature extraction Skeleton Data models Image sequences

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