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IEEE international conference on robotics and automation (ICRA)

作者： Al Muzaddid, Md Ahmed Beksi, William J. Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

ISBN: (纸本)9781728196817

In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated point clouds of varying quality. In contrast, our network is capable of explicitly processing point clouds and generating a compressed description at a comprehensive range of bitrates. Furthermore, our approach ensures that there is no loss of information as a result of the voxelization process and the density of the point cloud does not affect the encoder/decoder performance. An extensive experimental evaluation shows that our model obtains state-of-the-art results, it is computationally efficient, and it can work directly with point cloud data thus avoiding an expensive voxelized representation.

关键词： RGB-D Perception Deep Learning for Visual Perception Big Data in robotics and automation

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Detection of Slip from Vision and Touch 39

Detection of Slip from Vision and Touch

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IEEE international conference on robotics and automation (ICRA)

作者： Yan, Gang Schmitz, Alexander Tomo, Tito Pradhono Somlor, Sophon Funabashi, Satoshi Sugano, Shigeki Waseda Univ Fac Sci & Engn Dept Modern Mech Engn Tokyo 1698555 Japan

ISBN: (纸本)9781728196817

Detecting the onset/ongoing of slip, i.e. if a grasped object is slipping or will slip from the gripper while being lifted, is crucial. Conventionally, it is regarded as a tactile sensing related problem. However, recently multi-modal robotic learning has become popular and is expected to boost the performance. In this paper we propose a novel CNN-TCN model to fuse tactile and visual information for detecting the onset/ongoing of slip. In our experiments, two uSkin tactile sensors and one Realsense435i camera are used. Data is collected by randomly grasping and lifting 35 daily objects 1050 times in total. Furthermore, we compare our CNN-TCN model with the widely used CNN-LSTM model. As a result, our proposed model achieves a 88.75% detection accuracy and outperforms the CNN-LSTM model combined with different pretrained vision networks.

关键词： Training Visualization Robot vision systems Tactile sensors Grasping Cameras Data models

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Integrating Visual Reasoning Localization with Reinforcement Learning for 3C Assembly

Integrating Visual Reasoning Localization with Reinforcement...

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2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Huang, Baolin Zhou, Huaidong Zhang, Jiaqi Sun, Fuchun Guo, Jiahui Tsinghua University Department of Computer Science and Technology Beijing100084 China School of Automation Beijing Information Science and Technology University Beijing100192 China School of Automation Northwestern Polytechnical University Xi'an710129 China

ISBN: (纸本)9781665481090

In 3C (Computer, Communication, and Consumer Electronics) flexible assembly tasks, due to the frequent updates and the increasing demand for intelligence, traditional automated assembly methods struggle to meet the requirements for high precision and adaptability. To address these challenges, this paper proposes an assembly method that combines visual reasoning localization with deep reinforcement learning. The method improves the reward function by integrating visual reasoning results with reinforcement learning and trains the model in a virtual environment. Through sim-to-real transfer, the assembly strategies learned in the virtual environment are applied to real-world scenarios, significantly enhancing the assembly accuracy and success rate in real-world settings. Validation on a 3C front camera assembly task demonstrates an assembly accuracy of 0.15 mm and a success rate of 97%. © 2024 IEEE.

关键词： Virtual environments

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Sound Source Localization in a 3D Multi-Surface Environment Using Learning-Based methods 12

Sound Source Localization in a 3D Multi-Surface Environment ...

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12th RSI international conference on robotics and Mechatronics, ICRoM 2024

作者： Esfahanian, Soroush Rayati, Keyhan Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529734

This study presents a three-dimensional localization of a ball drop in a multi-surface environment using cost-effective data acquisition devices, and proposes two learning-based methods to improve the baseline classical localization method based on GCC-PHAT-estimated TDOA values. The first proposed method employs a ball drop surface classification executed by Random Forest and XGBoost models to enhance localization performance, whereas the second method conducts region classification by using both basic and multi-branch neural network models, achieving improved and more efficient localization performance. A dataset of real-world sparse ball drop samples was collected in a controlled multi-surface environment for experimental execution, where results demonstrate that the two proposed methods improve the localization performance across various scenarios, with the region classification method significantly outperforming the baseline method, achieving a 17% reduction in the Mean Euclidean Distance error metric. Furthermore, the surface and region classification methods reduce the localization time compared to the baseline method by 50% and 97%, respectively. The obtained results reveal the potential of combining simple learning-based models with affordable microphone sensors for achieving an accurate localization of sparse audio samples in a multi-surface environment. © 2024 IEEE.

关键词： Deep neural networks

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Approaching Problems with Different Kinds of Petri Net models 23

Approaching Problems with Different Kinds of Petri Net Model...

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23rd IEEE international conference on automation, Quality and Testing, robotics (AQTR)

作者： Cuibus, Octavian Letia, Tiberiu Tech Univ Cluj Napoca Automat Dept Cluj Napoca Romania

ISBN: (纸本)9781665479332

In the last years, various kinds of Petri Nets were conceived for solving all types of software problems, each Petri Net kind having its own features and limitations. Some of the most outstanding types are: Petri Nets (PN), Enhanced Time Petri Nets (ETPN), Fuzzy Logic Enhanced Time Petri Nets (FLETPN), Unified Enhanced Time Petri Nets (UETPN), Object Enhanced Time Petri Nets (OETPN), Stochastic OETPN, Quantum Petri Nets (QPN). The current research goals are to analyze and compare their power for solving practical application problems. As example, we approach the problem of a moving robot in a warehouse (moving entity problem). A feature analysis of various PN model types is performed based on this example.

关键词： PN ETPN FLETPN UETPN OETPN SOETPN QPN moving entity

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Hybrid models for Forecasting Allocative Localization Error in Wireless Sensor Networks

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international Journal of Cognitive Computing in Engineering 2025年 6卷 333-350页

作者： Li, Guo Sheng, Hongyu State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China College of Robotics Beijing Union University Beijing 100101 China

This study presents a machine learning-based approach to forecast Allocative Localization Error (ALE) in Wireless Sensor Networks (WSNs), addressing challenges such as dynamic network topologies and resource constraints. The approach utilizes Radial Basis Function (RBF) models enhanced with advanced optimization algorithms, including Coot Optimization Algorithm (COA), Smell Agent Optimization (SAO), and Northern Goshawk Optimization (NGO) to improve ALE prediction accuracy. Hybrid models (RBCO, RBSO, and RFNG) are developed by integrating these optimization techniques, which refine critical RBF parameters, such as spread and center selection, through iterative optimization. Furthermore, an ensemble framework (RSNC) combines all three optimizers with RBF to achieve superior performance. The proposed methods are validated using R2 and RMSE metrics, demonstrating their ability to minimize ALE, optimize resource allocation, and extend network lifespans. The study highlights the practical applicability of these models in real-world scenarios, such as environmental monitoring and industrial automation, offering enhanced efficiency and economic benefits. The RFNG model, in particular, achieved the lowest Mean Absolute Relative Error (MARE) of 0.049, demonstrating superior performance compared to other approaches in the test section. Moreover, RBNG obtained 0.069 and 0.978 values for the RMSE and R2, respectively, which were the most suitable values compared to other models, namely RBGO, RBSO, RSNC, and RBF. The results indicate that the proposed hybrid models significantly improve the prediction of ALE, leading to more efficient node deployment and better network management. This research provides valuable insights into leveraging machine learning for WSN optimization, benefiting researchers, network engineers, and industries relying on sensor networks for applications such as environmental monitoring, smart cities, and asset tracking. © 2025

关键词： Radial basis function networks

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Improving the Feasibility of DS-based Collision Avoidance Using Non-Linear Model Predictive Control 39

Improving the Feasibility of DS-based Collision Avoidance Us...

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IEEE international conference on robotics and automation (ICRA)

作者： Farsoni, Saverio Sozzi, Alessio Minelli, Marco Secchi, Cristian Bonfe, Marcello Univ Ferrara Dept Engn Ferrara Italy Univ Modena & Reggio Emilia Dept Sci & Methods Engn Modena Italy

ISBN: (纸本)9781728196817

In this paper we present a novel strategy for reactive collision-free feasible motion planning for robotic manipulators operating inside an environment populated by moving obstacles. The proposed strategy embeds the Dynamical System (DS) based obstacle avoidance algorithm into a constrained non-linear optimization problem following the Model Predictive Control (MPC) approach. The solution of the problem allows the robot to avoid undesired collision with moving obstacles ensuring at the same time that its motion is feasible and does not overcome the designed constraints on velocity and acceleration. Simulations demonstrate that the introduction of the MPC prediction horizon helps the optimization solver in finding the solution leading to obstacle avoidance in situations where a non predictive implementation of the DS-based method would fail. Finally, the proposed strategy has been validated in an experimental work-cell using a Franka-Emika Panda robot.

关键词： Robot kinematics Robot control Predictive models Prediction algorithms Planning Collision avoidance Robots

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A feature extraction framework with entropy on graphs for cross-dataset building fault detection 20

A feature extraction framework with entropy on graphs for cr...

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IEEE 20th international conference on automation Science and Engineering (CASE)

作者： Huang, Jiajing Patharkar, Abhidnya Wu, Teresa Wen, Jin O'Neill, Zheng Candan, K. Selcuk Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Drexel Univ Dept Civil Architectural & Environm Engn Philadelphia PA 19104 USA Texas A&M Univ J Mike Walker 66 Dept Mech Engn College Stn TX 77843 USA

ISBN: (纸本)9798350358513;9798350358520

Simulation is commonly adopted in developing building automated fault detection and diagnosis (AFDD) strategies. However, simulations often fall short in accurately representing real-world scenarios, which hinders the efficacy of models trained on such data for identifying faults in actual buildings. To tackle this challenge, we present a new approach for feature extraction that leverages entropy obtained from graph structures. These structures are constructed based on features that can distinguish between normal and faulty conditions. This method includes acquiring graph structures from simulated data, extracting their entropies as features to train AFDD models. Then, the process of obtaining entropies from graphs is replicated for real building data, and the trained AFDD model is applied to conduct tests on them. Empirical findings illustrate that our suggested approach enables fault detection in real-world scenarios, even when the model is trained with simulated data. The features extracted by our proposed approach surpass the baseline, which consists of GNN embedded features, in terms of fault detection performance. Therefore, we infer that our method has the potential to take advantage of simulation for real building fault detection.

关键词： Failure Detection and Recovery Big-Data and Data Mining AI-Based methods

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning 3

Robust Phishing URL Classification Using FastText Character ...

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3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Al-Sakib, Abdullah Limon, Zishad Hossain Sakib, Anamul Pranto, Md Nuruzzaman Islam, Md Ariful Sultana, Shayma Ahmed, Md. Redwan Rahman, Shafiur Westcliff University Dept. of Information Technology CA92614 United States Westcliff University Dept. of Computer Science CA92614 United States International American University Dept. of Business Administration CA90010 United States International American University Dept. of Management Information System CA90010 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims. © 2024 IEEE.

关键词： Phishing

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Research on methods of Expressway Vehicle Detection under Abnormal Weather Conditions Based on Deep Learning 7

Research on Methods of Expressway Vehicle Detection under Ab...

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7th international conference on robotics, Control and automation (ICRCA)

作者： Cao, Rong Ma, Xiaogang Chen, Xuehui Ma, Xinyi Hua, Liru Zhao, Chihang Ma, Teng Wang, Xinliang Shandong Hispeed Co Ltd Jinan Peoples R China Southeast Univ Sch Transportat Nanjing Peoples R China Shandong Hispeed Xinwei Informat Technol Co Ltd Jinan Peoples R China

ISBN: (纸本)9798350345780

Aiming at the issue that vehicle detection accuracy is easily influenced by abnormal weather conditions such as rain, snow, and frog, et al. This paper studies the methods of expressway vehicle detection based on deep learning. First, methods for detecting expressway vehicles based on FasterRCNN, YOLOV3, and SSD are compared and analyzed. Then, based on SEU expressway vehicle detection dataset under abnormal weather conditions, the training of the vehicle detection model and the research of compare experiments on Faster-RCNN, YOLOv3, and SSD are carried out by manually labeling and collecting specific regions of vehicles. Theoretical analysis and experimental results show that the YOLOv3-based detection model of expressway vehicle detection under abnormal weather conditions outperforms the other two methods, with an average accuracy of 99.2%.

关键词： Intelligent Transportation Deep Learning Vehicle Detection Abnormal Weather Conditions

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