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检索条件"任意字段=25th International Conference on Methods and Models in Automation and Robotics"

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Study on the Psychological Acceptance of Level 3 Autonomous Driving

Study on the Psychological Acceptance of Level 3 Autonomous ...

引用

international conference on Control, automation and robotics (ICCAR)

作者： Yilian Li Wenyu Wu Chang Gao Chenhao Li School of Mechanical Engineering Southeast University Nanjing China

ISBN: (数字)9798350373172

ISBN: (纸本)9798350373189

the emergence of autonomous vehicles (AVs) has brought numerous conveniences and development opportunities to human society, but it has also raised safety concerns. In recent years, AVs have been involved in multiple traffic accidents for various reasons, resulting in a continuous decline in user acceptance. In response, we need to propose some hypotheses to investigate the relationship between traffic accidents and the decline in user acceptance. this paper aims to summarize and generalize the objective problems observed during a real vehicle observation experiment and conduct in-depth interviews with participants regarding their usage scenarios, objectives, interactive behaviors, and operational methods related to AVs. By analyzing the factors that influence user acceptance, we will further develop, improve, and integrate the psychological factors affecting drivers' behaviors based on AVs, ultimately creating a model for the influencing factors of user acceptance in AVs.

关键词： Analytical models Correlation Loading Psychology Safety Autonomous vehicles Robots

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Multi-Objective Trapping of UAVs Swarm Based on Gene Regulatory Networks

Multi-Objective Trapping of UAVs Swarm Based on Gene Regulat...

引用

IEEE international conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Peng Xu Ze Shi Zhun Fan International Cooperation Base of Evolution Intelligence and Robotics of Guangdong Province College of Engineering Shantou University Shantou China

Encirclement mode formation in swarm robotic systems is a long-term hot research topic. Most of the encirclement control models designed by traditional methods are based on global information. this paper proposes a distributed unmanned swarm control method. the approach is based on gene regulatory network models and does not rely on any direct communication. the gene regulatory network model will generate a concentration field based on the input target and obstacle information, which will be used by the unmanned swarm to autonomously assign targets and guide the unmanned swarm to move towards the target. In this method, the UAV only needs to obtain the local information within the sensing range through the sensors carried by itself, and can complete the assignment and encirclement tasks of multiple targets by itself, getting rid of the unmanned swarm's dependence on global information. Finally, the validity and reliability of the proposed model are verified by simulating in an obstacle environment.

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A Hybrid Dynamic Modeling Method for External Torque Estimation of Robot Manipulators

A Hybrid Dynamic Modeling Method for External Torque Estimat...

引用

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

作者： Hanqing Yuan Shanrong Ren Jun Yao Jianliang Mao College of Automation Engineering Shanghai University of Electric Power Shanghai China School of Automation Southeast University Nanjing China

this paper introduces a hybrid modeling approach designed to estimate external forces in robot manipulators, with a focus on achieving high-precision force estimation while minimizing reliance on force/torque sensors. Traditional methods used for robot dynamics modeling and identification often grapple with unmodeled dynamics, system measurement noise, filtering delays, and equipment vibrations, all of which contribute to significant errors in the resulting dynamic models. To address these challenges, this paper leverages neural networks to mitigate the discrepancies in the physical model. the process commences with the application of the Newton-Euler method to perform dynamic modeling of the robot manipulators. Subsequent parameter identification is conducted to establish the physical model governing the robot manipulators' dynamics. Building on this physical model, a backpropagation (BP) neural network is employed to create a data-driven model of the model errors, thereby further enhancing model accuracy. To validate the effectiveness of this approach, external torque estimation experiments were carried out on a six-axis robot manipulators platform utilizing a Beckhoff controller. the experimental results affirm the method's capability to significantly improve the accuracy of external torque estimation in scenarios where force/torque sensors are unavailable.

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A Pedestrian Detection Method for Highways Based on Lightweight FCOS and Hybrid Feature Pyramid Networks

A Pedestrian Detection Method for Highways Based on Lightwei...

引用

robotics, Control and automation Engineering (RCAE), international conference on

作者： ZhiHeng Cai WeiMing Wang School of Computer Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

With the rapid development of the transportation industry, pedestrian detection algorithms on highway roads have become an important research direction. To solve the problem of a large number of model parameters and a large amount of calculation, this paper proposes a road pedestrian detection method based on lightweight FCOS. In this method, the hybrid feature pyramid networks(HyFpn) is introduced by improving the neck feature pyramid networks, and the lighter mobilenetV3 is used as the backbone network. Experimental results show that the proposed method significantly reduces the size and computational complexity of the model while maintaining high detection accuracy, which is conducive to real-time pedestrian detection on resource-constrained equipment. Compared with the existing methods, the proposed method achieves a better balance between accuracy and speed.

关键词： Training Pedestrians Accuracy Computational modeling Roads Transportation industry Predictive models Feature extraction Mobile handsets Computational complexity

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Fault Detection Robotic System: Controlling and Experimental Validation of Overhead Crane Climbing Robot

Fault Detection Robotic System: Controlling and Experimental...

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international conference on methods and models in automation and robotics (MMAR)

作者： Arun Kumar Yadav Janusz Szpytko Faculty of Mechanical & Robotics Engineering AGH University of Science &Technology Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

the involvement of robots has increased rapidly in the last few years in many applications. A variety of robot performs numerous type of task in a hazardous environment. In order to prevent server damage to the infrastructures of overhead cranes in an industrial environment, a Fault Detection System (FDS) and Structural Health Monitoring (SHM) has a significant role. Mainly enhancing the safety, reliability, and availability of industrial overhead cranes requires timely continuous monitoring. this research proposes a wide approach for inspection that applies to various types of physical machines by mainly focusing on overhead crane bridges. the study of fault detection using a robotic system by NDT & SHM is relatively new, as only a few types of research has been performed. To be more specific, this paper provides a detailed insight into an autonomous robot for crack detection in overhead cranes bridges and girder using an array of GMR (Giant magneto resistive) sensors and magnetic climbing mechanisms. Firstly, we focused on the challenges and issues in the monitoring of the overhead cranes and the possibility of cracks and faults. Concerning these challenges and issues, we have proposed a new approach applicable for overhead cranes bridges crack detection. the intention of conducting this research is to make the inspection process cost-effective, reliable, and safe. this paper provides detailed insight into crack detection robots and sensor systems by considering all attributes.

关键词： Bridges Cranes Service robots Magnetic resonance imaging Inspection Robot sensing systems Structural beams

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Stability investigation and control synthesis of RLC ladder circuits modeled as uncertain spatially interconnected systems*

Stability investigation and control synthesis of RLC ladder ...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Bartłomiej Sulikowski Krzysztof Gałkowski Dongdong Zhao Li Xu Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland School of Information Science and Engineering Lanzhou University Lanzhou China Department of Intelligent Mechatronics Akita Prefectural University Akita Japan

ISBN: (纸本)9781665476881

this paper deals with the problem of stability analysis and control scheme synthesis for RLC ladder circuits modeled as two-dimensional $(2D)$ systems, to which class spatially interconnected systems can be associated. this is because in the state-space model more than one indeterminates appear (time - continuous and the coordinates of the node in the structure - discrete). Hence analysis and control synthesis of such systems require developing new results from the systems theory area. As discussed later on due to the finite number of nodes in real systems under consideration, one option (exploited in this paper) for the analysis and synthesis of such systems is embedding temporal and spatial (node to node) dynamics into one 1D, i.e. $n=1$ , model. In this paper, RLC circuits, displayed as a series, i.e. along one spatial (node) indeterminate, constructed with conductances (resistors - R), inductances (L) and capacitors (C), considered are built of electronic elements with values given with some tolerance. this motivates the application of uncertain state-space model with the polytopic type of uncertainty. then, for such a model, the Linear Matrix Inequalities (LMI) based methodology towards stability analysis and controller design methodology are developed. It actually leads to interesting results from the robust control area. the case study proves the correctness of the proposed methods.

关键词： Analytical models Uncertainty Two dimensional displays RLC circuits Interconnected systems Stability analysis State-space methods

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CausalAgents: A Robustness Benchmark for Motion Forecasting

CausalAgents: A Robustness Benchmark for Motion Forecasting

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IEEE international conference on robotics and automation (ICRA)

作者： Liting Sun Rebecca Roelofs Ben Caine Khaled S. Refaat Ben Sapp Scott Ettinger Wei Chai Waymo Google Research

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

As machine learning models become increasingly prevalent in motion forecasting for autonomous vehicles (AVs), it is critical to ensure that model predictions are safe and reliable. In this paper, we examine the robustness of motion forecasting to non-causal perturbations. We construct a new benchmark for evaluating and improving model robustness by applying perturbations to existing data. Specifically, we conduct an extensive labeling effort to identify causal agents, or agents whose presence influences human drivers’ behavior, in the Waymo Open Motion Dataset (WOMD), and we use these labels to perturb the data by deleting non-causal agents from the scene. We evaluate a diverse set of state-of-the-art deep-learning models on our proposed benchmark and find that all evaluated models exhibit large shifts under non-causal perturbation: we observe a surprising 25-38% relative change in minADE as compared to the original. In addition, we investigate techniques to improve model robustness, including increasing the training dataset size and using targeted data augmentations that randomly drop non-causal agents throughout training. Finally, we release the causal agent labels as an extension to WOMD and the robustness benchmarks to aid the community in building more reliable and safe deep-learning models for motion forecasting 1 .

关键词： Training Sensitivity Perturbation methods Machine learning Predictive models Benchmark testing Robustness

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Deep learning methods for Fake News detection

Deep learning methods for Fake News detection

引用

international Symposium on Computational Intelligence and Informatics

作者： Viera Maslej Krešňáková Martin Sarnovský Peter Butka Technical university of Košice Košice Slovakia

ISBN: (数字)9781728156255

ISBN: (纸本)9781728156262

Spreading of misinformation on the web nowadays represents a serious issue, as their influence on peoples opinions may be significant. Fake news represents a specific type of misinformation. While its detection was mostly being performed manually in the past, automated methods using machine learning and related fields became more critical. On the other hand, deep learning methods became very popular and frequently used methods in the field of data analysis in recent years. the study presented in this paper deals with the detection of fake news from the textual data using deep learning techniques. Our main idea was to train different types of neural network models using both entire texts from the articles and to use just the title text. the models were trained and evaluated on the Fake News dataset obtained from the Kaggle competition.

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Integration of INS and GPS using radial basis function neural networks for vehicular navigation

Integration of INS and GPS using radial basis function neura...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： M Malleswaran S Angel Deborah S Manjula V. Vaidehi Dept of Electrical Engineering Embedded System Anna University Tirunelveli India Dept of Information Technology MIT Campus Anna University Chennai Chennai India

Navigation systems used in recent days rely mainly on Kalman filter to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In common, INS/GPS data fusion provides reliable navigation solution by overcoming drawbacks such as signal blockage for GPS and increase in position errors with time for INS. Kalman filtering INS/GPS integration techniques used in present days have some inadequacies related to the stochastic error models of inertial sensors, immunity to noise, and observability. this paper aims to introduce a new system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN). A multi-layer perceptron ANN has been recently suggested to fuse data from INS and differential GPS (DGPS). though the integrated system using multi-layer perceptron scheme improves the positioning accuracy, it has shortcomings like complexity with respect to the architecture of multi-layer perceptron networks and limitation of online training algorithm to provide real-time capabilities. this paper, therefore, proposes the use of an alternative ANN architecture. the proposed architecture is based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks. the RBF-ANN module is trained to predict the INS position error and provide accurate positioning of the moving vehicle.

关键词： Global Positioning System Artificial neural networks Training Mean square error methods Stochastic processes Vehicle dynamics

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Full Resolution Repetition Counting 16th

Full Resolution Repetition Counting

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16th international conference on Intelligent robotics and Applications, ICIRA 2023

作者： Li, Jianing Chen, Bowen Wang, Zhiyong Liu, Honghai School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819964826

Given an untrimmed video, repetitive actions counting aims to estimate the number of repetitions of class-agnostic actions. To handle the various length of videos and repetitive actions, also optimization challenges in end-to-end video model training, down-sampling is commonly utilized in recent state-of-the-art methods, leading to ignorance of several repetitive samples. In this paper, we attempt to understand repetitive actions from a full temporal resolution view, by combining offline feature extraction and temporal convolution networks. the former step enables us to train repetition counting network without down-sampling while preserving all repetitions regardless of the video length and action frequency, and the later network models all frames in a flexible and dynamically expanding temporal receptive field to retrieve all repetitions with a global aspect. Besides, temporal self-similarity matrix is used in our model to represent the correlation of action, which contains much cycle information in time series. We experimentally demonstrate that our method achieves better or comparable performance in three public datasets, i.e., TransRAC, UCFRep and QUVA. We expect this work will encourage our community to think about the importance of full temporal resolution. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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