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Hand-to-eye calibration of a robotic arm using point cloud registration method

Hand-to-eye calibration of a robotic arm using point cloud r...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Zhuoran Miao Yunbo Zuo Huan Hu Xiaoliang Ge Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing People's Republic of China

ISBN: (数字)9781837241910

The objective of this paper is to investigate a hand-eye calibration approach employing an eye-to-hand configuration, focusing on point cloud registration. In the initial segments of the paper, the paper delineate the core principles that underpin hand-eye calibration, as well as the various techniques that are predominantly utilized in this field. Furthermore, the article meticulously analyzes the limitations of the traditional methods and identifies potential areas where improvements can be made. Subsequently, it delves into the hand-eye calibration methodology grounded in 3D point cloud registration. This exploration includes an in-depth analysis of an enhanced version of the Super 4PCS Algorithm-based point cloud registration algorithm, which is a novel and effective method for point cloud registration. Additionally, the article discusses the intricacies of coordinate system transformation and provides a comprehensive explanation of how to solve the hand-eye calibration equation AX=XB using the navy method. Furthermore, the paper empirically validates the efficacy and precision of this method. This validation process involves a comparative analysis with other existing approaches. Lastly, it summarizes the research findings and discusses the strengths and limitations of the proposed method.

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Application of wavelet kurtosis convolutional neural network model in rolling bearing fault diagnosis

Application of wavelet kurtosis convolutional neural network...

引用

10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Dachao Men Zhanglei Jiang Xiuli Liu Wei Zheng Zihan Li Zhendong Zha Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing People's Republic of China

ISBN: (数字)9781837241910

Aiming at the problem of substantial noise in the actual industrial environment, where the intelligent diagnostic model exhibits weak anti-noise performance and the interpretability of the extracted features is not strong, the wavelet kurtosis convolutional neural network model integrating signal processing technology and deep learning neural networks has been proposed. Firstly, the standard CNN convolution kernel has been replaced with a wavelet basis function to form a wavelet convolution layer, and the input signal undergoes decomposition into various frequency components via the wavelet filter in this layer; Secondly, a kurtosis loss layer has been used to filter the high kurtosis value components that contain more fault features, so as to achieve the filtration of the noise and the accurate localisation of the fault signals; Lastly, the efficacy of the proposed model has been verified in the rolling bearing dataset and diagnostic model. The results showed that the model can reach 90% diagnostic accuracy under -4db Gaussian white noise interference, which was higher than other diagnostic models, and had good generalisation and noise resistance.

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Dense point cloud map construction based on ORB-SLAM2 and cross-attention network

Dense point cloud map construction based on ORB-SLAM2 and cr...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Yizheng Liu Qinghua Su Yue Song Pengbo Zhang Zhihao Xie Liyong Wang Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science & Technology University Beijing People's Republic of China

ISBN: (数字)9781837241910

In the field of 3D vision technology, binocular vision technology, as a representative of low-cost and high-efficiency methods, has gained widespread attention and holds superior application prospects in the industrial sector. The current state-of-the-art binocular sparse visual SLAM systems can accurately and reliably estimate camera trajectories and landmark positions. However, these sparse maps are solely used for localization and cannot be employed for other tasks such as obstacle avoidance or scene understanding. To address this, this paper proposes a framework for constructing dense point cloud maps in binocular sparse visual SLAM systems. This framework enhances the traditional ORB-SLAM2 algorithm by incorporating depth estimation using a cross-attention network to complete depth information and build dense point cloud maps based on its localization capabilities. For the first-frame image, the proportion of incorrectly predicted pixels in the KITTI dataset is only 1.77%. The proposed method is effective and robust on common datasets, suitable for constructing dense point cloud maps in outdoor large-scale scenes. The output model is overall comprehensive and detailed, holding significant practical value in various outdoor scenarios.

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Periodic statistical analysis of gait phase segmentation

Periodic statistical analysis of gait phase segmentation

引用

10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Mingkai Fu Shaohong Wang Chao Ma Key Laboratory of Modern Measurement and Control Technology Ministry of Edu. Beijing Information Science and Technology Beijing People's Republic of China

ISBN: (数字)9781837241910

In order to achieve accurate segmentation of gait phases for precise real-time control of lower limb exoskeleton robots, the periodic statistical analysis of human motion posture by using the inertial measurement unit is conducted. In current research, the data of forward and turning gait types were acquired and every gait type data were devised into eight for different gait phase segmentation. During the process, the phase division values for different types of subjects were calculated and the phase division of different subjects with the same gait type was calculated. The standard deviations for different gaits and phases for the same experimenter are all below 10, primarily oscillated around 4. For gait data from different subjects, the percentages at the same phase are all below 3. The results showed that the results from the feasible method could better accord with the characteristics of each stage of actual *** those results could provide reference for real-time judgment of gait phase and technical support for intelligent control of lower limb exoskeletons.

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Hybrid-driven model for temperature prediction in wet clutches

Hybrid-driven model for temperature prediction in wet clutch...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Yuanbin Huang Le Li Qian Wang Liyong Wang Key Laboratory of Modern Measurement and Control Technology in Ministry of Education Beijing Information Science and Technology University Beijing People's Republic of China

ISBN: (数字)9781837241910

A hybrid-driven model for predicting the temperature at the friction interface of wet clutch friction components was developed, employing a radial basis function neural network algorithm. This model uses temperature data obtained from wet clutch test bench experiments to compensate for the results from a thermo-mechanical coupled numerical simulation model of the friction components. The hybrid model inputs include load pressure, relative speed, radial position of the friction components, and numerically simulated temperatures at the friction interface, to output predicted temperatures under specific operating conditions. The results indicate that the predicted temperatures of the friction components under various conditions align well with experimental values, with a maximum mean relative error of 0.033 and all prediction intervals having a coefficient of determination higher than 0.97. Thus, the hybrid-driven model demonstrates high accuracy and stability in predictions, and possesses strong adaptability and predictive capabilities for different temperature distributions at the friction interface.

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Optimization of eye-in-hand calibration methods

Optimization of eye-in-hand calibration methods

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Xiaoliang Ge Yunbo Zuo Yuhai Gu Zhuoran Miao Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing People's Republic of China

ISBN: (数字)9781837241910

Hand-eye calibration is a vital part in the field of robot vision and motion control, which ensures that the robot system can accurately perceive and operate the surrounding environment. To enhance the accuracy of the hand-eye calibration matrix obtained in the current experimental environment, this paper provides an optimal scheme for the selection of calibration points and hand-eye calibration algorithms on the checkerboard calibration board. The geometric errors, rotation-translation errors and other parameters of Tsai algorithm and Navy algorithm were calculated based on multiple sets of experimental data, and the performance differences between the two algorithms were evaluated. The experimental results show that in the calibration environment of this experiment, the optimal selection number of marker points is about 12, and the optimal calibration algorithm is Tsai algorithm. This study provides a reference for the optimization of robotic hand-eye calibration technology.

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Deep CNN fusing attention and LGBM classifier for high-accuracy hand gesture recognition

Deep CNN fusing attention and LGBM classifier for high-accur...

引用

10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Zhicong You Wangdong Lu Chunmei Zhu Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing People's Republic of China

ISBN: (数字)9781837241910

Gesture recognition has been widely used in automation and medical and smart-home. The adaptability and accuracy of traditional machine learning algorithms are limited by feature extraction and classifier selection, and the recognition accuracy is low as well as the robustness is poor. In our work, an improved CNN (Convolutional Neural Networks) combined with an LGBM (Lightweight Gradient Boosting Machine) hand gesture recognition method is proposed to improve the performance of hand gesture recognition. The method designs a 4-layer CNN structure and integrates a CBAM (Convolutional Block Attention Module) in the network to enhance the model's ability to learn and extract features. Furthermore, the extracted features are fed into an LGBM classifier for gesture classification, which improves the accuracy of gesture classification and reduces the risk of model overfitting. The accuracy of the 16 types of static gesture recognition reaches 97.89% in our dataset. An improvement of more than 10% is obtained in comparison with traditional machine learning classification algorithms. It demonstrates promising prospects for gesture recognition.

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Edge Node Cooperation and Resource Sharing in Mobile Edge Computing 15

Edge Node Cooperation and Resource Sharing in Mobile Edge Co...

引用

15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Meng, Anqi Gao, Xiaozheng Huang, Yuting Xu, Zhan Shang, Jiadong Ministry of Education Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Beijing China Ministry of Information Industry Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Beijing China Beijing Institute of Technology School of Information and Electronics Beijing China Beijing Institute of Remote Sensing Equipment Beijing China

ISBN: (纸本)9798350324662

As edge computing undergoes ongoing development propelled by the advancements in Internet of Things and mobile communication technologies, the issue of edge node cooperation and resource sharing needs to be investigated. In this paper, the issue of edge node cooperation and resource sharing is modeled as a two-layer framework. In the lower layer, a heuristic matching algorithm between users and edge nodes is developed, and a resource sharing algorithm among edge nodes in the same coalition is proposed. In the upper layer, a coalition formation algorithm is designed based on the coalition game. Simulation results show that the proposed algorithms reduce the network cost effectively compared with non-cooperative scheme. © 2023 IEEE.

关键词： coalition game mobile edge computing resource sharing

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Interference Utilization with Bandwidth Allocation in DFRC Systems

Interference Utilization with Bandwidth Allocation in DFRC S...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Zhan Xu Shengfeng Kang Xiaolong Yang Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Dual-functional Radar and Communication (DFRC) has become one of the key technologies of advanced wire-less systems. This is because of the continuous development of mobile communication, the scarcity of spectrum and the potential network applications. In this paper, we propose a symbol-level precoding optimization scheme for Multi-Input-Multi-Output (MIMO) radar and multi-user Multiple-Input-Single-Output (MU-MISO) communication systems. More specif-ically, the weight sum of communication rate and radar detection probability are maximized by jointly optimizing the precoding vector and bandwidth allocation. Meanwhile, it includes the condition of ensuring the receiving signal-to-interference-plus-noise ratio (SINR) at receivers, the interference to the radar and the transmission power budget at the base station (BS). Com-pared with the traditional beamforming based on Semidefinite Relaxation (SDR), the feasible area of the optimization problem is extended. Based on the concept of constructive interference, the harmful multi-user interference (MUI) is converted into a beneficial signal. So the communication rate is ensured and the system performance is improved. Numerical simulations show that our proposed method achieves a better result of the object function compared to benchmark methods, while obtaining a favorable bandwidth allocation.

关键词： Base stations Precoding System performance Radar detection Interference Radar Channel allocation

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Cross-border Data Security Risk Analysis and Development Suggestions of the Protection Technologies for Intelligent Connected Vehicles

Cross-border Data Security Risk Analysis and Development Sug...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Xiaolong Huang Chongshi Xin Haojie Ji Wangdong Lu Peng He Yupeng Chen State Key Laboratory of Intelligent Vehicle Safety Technology Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science& Technology University Beijing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science& Technology University Beijing China China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

In recent years, intelligent connected vehicles (ICVs) have developed quickly and have been progressively introduced into the international market. Nevertheless, the operation of ICVs produces a large amount of important data coming from driving vehicle and necessitating cross-border transit. As the imperfections of the regulatory framework and the infancy of protective solutions for the ICV data, these data are vulnerable to the threats like eavesdropping and tampering during cross-border transmission. The private information, especially the data barred from leaving the country, will be probably leaked to other nations, endangering individual privacy and national security. Thus, the purpose of this manuscript is to analyze the security risks of cross-border data in ICVs and provide risk mitigation strategies. The legal requirements and pertinent cross-border materials need to be sorted out firstly to establish a thorough technical process for cross-border compliance of ICV data. Additionally, the ICV data risk analyses will be performed at every stage of the cross-border process. Furthermore, some efficient risk mitigation methods need to be offered to improve the security and dependability of the across-border data of ICVs. To address the problems associated with cross-border data pertaining to ICVs, a classification method for the cross-border data of ICVs will be presented according to current rules and data categorization schemes. Finally, this manuscript provides theoretical underpinnings and useful references for building a cross-border data security protection system, as well as a thorough analysis of the development patterns of cutting-edge technologies for cross-border data.

关键词： Technological innovation Connected vehicles Law Data security Solids Risk analysis Protection

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