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Environment Information-based Impedance Control 11

Environment Information-based Impedance Control

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11th IEEE Annual international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2021

作者： Gao, Yinghao Huang, Hailin Xu, Wenfu Li, Bing Xi, Fengfeng School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665425278

The regulation of contact force between manipulators and objects is important in robotics applications such as grinding, assembly, and human-robot interaction, etc. Impedance control is one of the classical methods of force control, but it suffers from lack of prior knowledge of the environment. Many researches combine impedance control with adaptive law to compensate the unknow environment parameter, which work well on continuous surfaces. But the methods always cause large overshoot if the end-effector is not close enough to the object before contact established. We proposed a novel approach which integrates depth camera into impedance control framework. The approach employs depth camera to extract environment information, which then is fed to the impedance controller. The method enables robots to see the shape of objects and perform force tracking on complex surfaces and even incontinuous surfaces. © 2021 IEEE.

关键词： Human robot interaction

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GPR-based Model Reconstruction System for Underground Utilities Using GPRNet

GPR-based Model Reconstruction System for Underground Utilit...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Feng, Jinglun Yang, Liang Hoxha, Ejup Sanakov, Diar Sotnikov, Stanislav Xiao, Jizhong CUNY Elect Engn Dept New York NY 10021 USA

ISBN: (纸本)9781728190778

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) instruments to detect and locate underground objects (i.e. rebars, utility pipes). Many of the previous researches focus on GPR image-based feature detection only, and none can process sparse GPR measurements to successfully reconstruct a very fine and detailed 3D model of underground objects for better visualization. To address this problem, this paper presents a novel robotic system to collect GPR data, localize the underground utilities, and reconstruct the underground objects' dense point cloud model. This system is composed of three modules: 1) visual-inertial-based GPR data collection module which tags the GPR measurements with positioning information provided by an omnidirectional robot;2) a deep neural network (DNN) migration module to interpret the raw GPR B-scan image into a cross-section of object model;3) a DNN-based 3D reconstruction module, i.e., GPRNet, to generate underground utility model with the fine 3D point cloud. In this paper, both the quantitative and qualitative experiment results verify our method that can generate a dense and complete point cloud model of pipe-shaped utilities based on a sparse input, i.e., GPR raw data, with incompleteness and various noise. The experiment results on synthetic data as well as field test data further support the effectiveness of our approach.

关键词： Solid modeling Three-dimensional displays Ground penetrating radar Instruments Position measurement Data models Robustness

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Research on a Game Data Prediction Method Based on an Improved GRU Algorithm

Research on a Game Data Prediction Method Based on an Improv...

引用

Computing, robotics and System Sciences (ICRSS), international conference on

作者： Qiyuan Yang Yiluan Guo International School Nanjing Audit University Nanjing China School of Internal Audit Nanjing Audit University Nanjing China

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

With the rapid development of the gaming industry, massive amounts of player behaviour data provide a wealth of reference for operational decisions. However, traditional statistical methods are limited in their effectiveness when dealing with complex, high-dimensional, non-linear gaming data. To this end, this paper proposes a game data prediction method based on an improved GRU algorithm. By introducing the Attention mechanism, the model can better capture the long-term dependencies in game data and combined with an adaptive parameter adjustment module to improve training efficiency and model stability. This paper discusses data pre-processing in detail, model architecture design, parameter optimisation, etc., and experimentally demonstrates the significant improvement in prediction accuracy and stability of the improved GRU model. The experimental results show that the improved GRU model not only outperforms the traditional GRU on multiple evaluation metrics, but also exhibits greater robustness and practical applicability.

关键词： Training Industries Adaptation models Accuracy Computational modeling Games Predictive models Prediction algorithms Data models Stability analysis

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

Robust Phishing URL Classification Using FastText Character ...

引用

robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

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

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

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.

关键词： Deep learning Uniform resource locators Training Adaptation models Phishing Prevention and mitigation Encoding Tokenization Real-time systems Robots

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PointStack: A Point Cloud Processing Network with Enhanced Global Feature Aggregation

PointStack: A Point Cloud Processing Network with Enhanced G...

引用

Software Engineering and Computer Science (CSECS), international conference on

作者： Yongliang Tao Jintao Wang Pengchao Li Mingmin Liu Zhenjun Du Yang Liu Chengpeng Li Faculty of Robot Science and Engineering Northeastern university Shenyang China Central Research Academy ShanDong SIASUN Industrial Software Research Institute Co. Ltd Shandong China Central Research Academy SIASUN Robot & Automation Co. Ltd Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Point cloud data, characterized by its rich geometric information, dense data representation, and flexible topological structure, finds extensive application in fields such as computer graphics, computer vision, and robotics perception. The rapid advancement and upgrade of three-dimensional point cloud acquisition devices have made the acquisition of point cloud data increasingly convenient. This, in turn, has spurred an urgent need for research on point cloud data processing. The irregularity and disorderliness of point clouds necessitate the design of network models that differ from those used for image processing. Presently, most network models for point cloud processing focus primarily on the design for local feature extraction, even though the effectiveness of these improvements has become somewhat marginal. In response to this, a stacked diffusion network model designed to capture global context has been proposed. This model excels at capturing global information and can effectively integrate it with local information. Extensive experiments conducted on standard benchmark datasets such as ModelNet40 and ShapeNetPart indicate that this model, in comparison to DGCNN, improves point cloud classification by 1.2%, achieving an Overall Accuracy (OA) of 94.1%. Furthermore, it enhances point cloud part segmentation by 1.3%, reaching an mIOU evaluation metric of 86.5%. When compared to other advanced methods, this model also exhibits noticeable improvements in point cloud classification and part segmentation.

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Joint Distribution and Class-based Data Augmentation for Wildlife Detection

Joint Distribution and Class-based Data Augmentation for Wil...

引用

automation, robotics and Computer Engineering (ICARCE), international conference on

作者： Yunhao Pan Chenhong Sui Fuhao Jiang Guobin Yang Ankang Zang Shengwen Zhou Physics and Electronic Information College Yantai University Yantai China

ISBN: (纸本)9781665475495

Data augmentation is of great importance to alleviate the insufficiency of training samples, and further improve wildlife detection accuracy. However, current data augmentation methods tend to augment all kinds of samples equally, ignoring the problem of uneven distribution of the number and size of all kinds of samples in wildlife detection datasets, resulting in poor generalization of the model. To address this problem, this paper proposes a joint distribution and class-based data augmentation method for wildlife detection. In this method, diverse rather than universal data augmentation methods are introduced for different classes with a small proportion. This makes the distributions of different classes more balanced. Therefore, each class even with a small number of samples gets good training as well. To evaluate the effectiveness of the proposed method, extensive comparative experiments are conducted. Experimental results show the superiority of our proposed method. Specifically, the detection accuracy of Faster RCNN with Swin Transformer as the backbone network is improved by 0.8% to 96.2% after data augmentation with our method.

关键词： Training Deep learning Computer vision Analytical models automation Computational modeling Wildlife

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Research on Trajectory Planning of Orchard Robot

Research on Trajectory Planning of Orchard Robot

引用

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

作者： Zhonghua Miao Hao Lu Nan Li Dong Wang School of Mechatronics Engineering and Automation Shanghai University Shanghai China

In contrast to typical scenarios, orchards present a more intricate environment characterized by numerous irregular obstructions. This complexity often leads to the generation of zigzag paths when employing conventional path planning algorithms. Moreover, the challenge is compounded by the orchard's slippery terrain and the fact that traditional path planning approaches do not consider robot kinematics, necessitating abrupt changes in acceleration. Such conditions make it arduous for orchard robots to follow these paths effectively. To mitigate these issues, this study introduces the minimum Jerk principle, which focuses on minimizing the rate of change in robot acceleration, as the cost function for optimizing the paths devised by standard path planning methods. The paper culminates in an empirical evaluation using a robot in an orchard setting. Experimental results demonstrate a substantial reduction in the robot's angular velocity fluctuations and a 12% decrease in tracking time, compared to paths generated by conventional methods.

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Real-Time Human Lower Limbs Motion Estimation and Feedback for Potential Applications in Robotic Gait Aid and Training

Real-Time Human Lower Limbs Motion Estimation and Feedback f...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Wang, Lei Li, Qingguo Yi, Jingang Zhang, Jinyuan Liu, Tao Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Queens Univ Dept Mech & Mat Engn Kingston ON K7L 3N6 Canada Rutgers State Univ Dept Mech & Aerosp Engn Piscataway NJ 08854 USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781728190778

Real-time lower limbs motion or gait measurement is an important part in human-robotic interaction for the control of robotic walkers and rehabilitation devices. Laser range finder or infrared sensor that is mounted on the device has been widely used in applications. Although these sensors can provide accurate horizontal motion information of lower limbs during human walking, it is still difficult to measure the angular motion of lower limbs due to their functional principles. Using inertial measurement units (IMU) can measure the angular motion of lower limbs, but it requires a large amount of IMU units for measurements of all lower limb segments. In this study, a novel method is developed for real-time monitoring lower limbs (shanks and thighs) motion in human walking using just two shank-mounted IMUs. A pose prediction model based on multiple linear regression and Kalman filter is proposed. The root-mean-square error (RMSE) of the thigh orientation and the knee joint angle estimation in sagittal plane are 6.1 +/- 1.3 and 6.8 +/- 1.4 degs, respectively. The RMSE of the ankle, knee, and hip position estimation are 4.2 +/- 1.3, 4.2 +/- 1.1 and 3.5 +/- 0.9 cm, respectively.

关键词： Legged locomotion Knee Training Measurement units Thigh Estimation Predictive models

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Trust Recognition in Human-Robot Cooperation Using EEG

Trust Recognition in Human-Robot Cooperation Using EEG

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IEEE international conference on robotics and automation (ICRA)

作者： Caiyue Xu Changming Zhang Yanmin Zhou Zhipeng Wang Ping Lu Bin He Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Collaboration between humans and robots is becoming increasingly crucial in our daily life. In order to accomplish efficient cooperation, trust recognition is vital, empowering robots to predict human behaviors and make trust-aware decisions. Consequently, there is an urgent need for a generalized approach to recognize human-robot trust. This study addresses this need by introducing an EEG-based method for trust recognition during human-robot cooperation. A human-robot cooperation game scenario is used to stimulate various human trust levels when working with robots. To enhance recognition performance, the study proposes an EEG Vision Transformer model coupled with a 3-D spatial representation to capture the spatial information of EEG, taking into account the topological relationship among electrodes. To validate this approach, a public EEG-based human trust dataset called EEGTrust is constructed. Experimental results indicate the effectiveness of the proposed approach, achieving an accuracy of 74.99% in slice-wise cross-validation and 62.00% in trial-wise cross-validation. This outperforms baseline models in both recognition accuracy and generalization. Furthermore, an ablation study demonstrates a significant improvement in trust recognition performance of the spatial representation. The source code and EEGTrust dataset are available at https://***/CaiyueXu/EEGTrust.

关键词： Adaptation models Computer vision Solid modeling Accuracy Source coding Brain modeling Transformers

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Motion Planning of Warehouse automation Robot Based on Recurrent Neural Network

Motion Planning of Warehouse Automation Robot Based on Recur...

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Mechatronics, IoT and Industrial Informatics (ICMIII), international conference on

作者： Xiangmei Li Yantai Sci. & Tech. Innovation Promotion Centre Yantai Shandong China

The global economy is developing rapidly and the promotion of economic globalization is also steadily advancing, which makes the market competition at home and abroad more and more cruel. In order to be in an advantageous position in the fierce economic market competition, enterprises have no choice but to fully realize industrial automation. With the vigorous development of e-commerce and logistics industry, warehouse automation has become a hot research direction in the field of robotics. Mobile robotics is a comprehensive system that integrates sensing, dynamic decision-making and planning, behavior control and execution, etc. Its research content involves sensor technology, computer engineering, automation control, mechanics and other multidisciplinary theories and applications. It reflects the latest achievements of information science and artificial intelligence technology. It is one of the most active fields of scientific and technological development. This paper uses a cyclic neural network, which introduces traditional signal analysis methods into the cyclic neural network. By replacing the state vector of the hidden layer of the traditional cyclic neural network with a state-frequency matrix, the time-domain characteristic pattern and frequency domain of the time series are realized. Joint modeling of feature patterns.

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