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2024 10th International Conference on Applied Materials and Manufacturing Technology, ICAMMT 2024

作者： Wu, Junjie Ji, Yulei Zhong, Lei Wang, Yuhan State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Meta Robotics Institute Shanghai Jiao Tong University Shanghai200240 China Shanghai TOP Numerical Control Technology Co. Ltd Shanghai201100 China

The increasing demand for highly efficient and precise machining in the aerospace industry is leading to increasingly stringent requirements for machine tool accuracy measurement and compensation technologies. However, existing methods can only measure the accuracy of machine tools after they have been shut down, using instruments that do not measure the kinematic errors during the machining process. To address this issue, a method for real-time prediction of kinematic errors in a five-axis machine tool has been proposed to enhance machining accuracy and efficiency. The method employs a neural network-based predictive model, which has been trained and validated through experimental testing and data collection using internal sensors of the machine tool. The study reveals that this predictive model can effectively forecast the kinematic errors, through real-time compensation, and can reduce errors by 60-86%, significantly enhancing machine performance. © Published under licence by IOP Publishing Ltd.

关键词： Aerospace industry

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Virtual chain: A storage model supporting cross-blockchain transaction

Virtual chain: A storage model supporting cross-blockchain t...

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作者： Wang, Yan Yang, Bingqing Liu, Jingxin Zeng, Hui Xia, Changqing College of Information Liaoning University Shenyang China State key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

At present, blockchain is not only applied in the financial field, but also extended to the medical, insurance, internet of things, and many other fields. Its independence leads to more and more significant problems of network isolation. To address the problem of the cooperative operation between different blockchains, this article proposes virtual chain, a storage model supporting cross-blockchain transaction. It not only ensures that cross-chain transactions are tamper-proof and traceable but also realizes completely decentralized storage. First, to tackle the multidimensional heterogeneous problem existing in cross-chain trading, a data structure based on prefix tree is proposed to manage cross-chain trading data on top of source chain and target chain. Second, a signature verification mechanism based on elliptic curve is proposed to meet the operational atomicity requirements of both parties of cross-chain transaction. Third, the data fusion technology is used to expand the block capacity indirectly to achieve the high throughput of cross-chain transactions. Finally, by using the credibility-based access control and block chain technology to ensure the security of the data storage model, and conduct security analysis from three aspects of tamper-proof, data protection and anti-conspiracy node attack. The experiment shows that the proposed cross-chain transaction storage model can realize the efficient storage of the heterogeneous data, and satisfy the high requirement of the cross-chain transaction. Moreover, the virtual chain storage model establishes a safe and reliable working environment. © 2020 John Wiley & Sons Ltd.

关键词： Blockchain

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Automated Suckering of Vines with a Mobile Robot and a Torque-controlled Suckering Tool 5

Automated Suckering of Vines with a Mobile Robot and a Torqu...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Stuhne, Dario Vatavuk, Ivo Hrabar, Ivan Vasiljević, Goran Kovačić, Zdenko University of Zagreb Faculty of EE&C Zagreb Croatia University of Zagreb LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

The research presented in this paper focuses on the development of fully automated and autonomous vine suckering, a seasonal viticulture activity required to remove all unwanted shoots from the vine trunks. This requires human workers to do a lot of strenuous work, often using only their hands or tools such as clippers or shears. The suckering is done during the early growing season (usually April-May) to provide more energy, water, and nutrients to the main shoots. Commercially available suckering machines appear to be a large-scale solution. However, their performance varies between 10% and 85% and their purely mechanical nature can easily cause damage to the vines. In this paper, we present a concept for autonomous suckering using a mobile robot equipped with a special suckering tool driven by a torque-controlled DC motor that protects the vine from damage. The proposed suckering system was designed, analysed and experimentally validated in a real vineyard. The results showed that the system was able to remove about two thirds of the unwanted shoots, reaching a suckering rate of more than 86 vines per hour. © 2022 IEEE.

关键词： Mobile robots

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An analysis method of industrial data relationship based on business context 3

An analysis method of industrial data relationship based on ...

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3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Liu, Yang Zhang, Tianshi Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Industrial big data processing is an important support for improving industrial production efficiency. Because industrial big data involves complex business logic in production scenarios, there is a widespread problem that the relationship between data is difficult to understand. Therefore, this paper proposes a relational analysis method of industrial data based on business context. Firstly, a semantic data dictionary is established according to the production business process, and then a data object recognition model based on deep learning is trained;then a context-based automatic association method of industrial data relations is proposed;finally, the experimental verification is carried out on the actual production data, and the results show that The method in this paper is effective in improving the relationship analysis of industrial data. © 2022 IEEE.

关键词： Big data

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A Relational Retrieval Model for Industrial Production Systems 2

A Relational Retrieval Model for Industrial Production Syste...

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2nd International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2022

作者： Liu, Yang Zhang, Tianshi Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781665459280

ISBN: (纸本)9781665459280

Industrial big data analysis is an important approach to explore the essence of industrial production processes. Traditional keyword retrieval and semantic retrieval are difficult to adapt to the industrial manufacturing systems with the characteristics of strong coupling of business associations. Therefore, according to the characteristics of the association in the industrial production system, an association retrieval model for the industrial production system is proposed in this paper. Firstly, the industrial Internet of things system is analyzed and abstracted, and the key factors of correlation retrieval are derived;then through the analysis of the relationship between key factors, the invalid relationship is screened out, the correlation relationship with practical significance is obtained, and the establishment of the relevance retrieval model is completed. Finally, the feasibility and effectiveness of the association retrieval model are verified by actual industrial scene data. © 2022 IEEE.

关键词： Manufacture

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Procedural Generation of Synthetic Dataset for Robotic Applications in Sweet Pepper Cultivation 5

Procedural Generation of Synthetic Dataset for Robotic Appli...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Vuletić, Jelena Polić, Marsela Orsag, Matko University of Zagreb Faculty of EE&C Zagreb Croatia University of Zagreb LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

In this paper, a deep learning based object detection model is developed for robotic applications in structured agricultural cultivation of sweet peppers (Capsicum annuum). We propose a realistic simulation based method for procedural generation of a labeled sweet pepper dataset. Realistic 3D models of fruit, leaves, and other relevant plant body parts are generated through photogrammetry, enabling generation of a large and relevant dataset. The dataset is labeled for both object detection and semantic segmentation by scene rendering in multiple layers and using artificial depth information. Smart augmentation techniques are deployed in procedural generation, as well as label filtering techniques for more realistic dataset labels. A MobileNet-SSD based deep learning model is trained through transfer learning on the generated dataset. The trained model is tested both in simulated environment, as well as on a relevant real-world setup. © 2022 IEEE.

关键词： Object detection

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Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction

arXiv

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arXiv 2025年

作者： He, Yushen Zhao, Lei Deng, Tianchen Fang, Zipeng Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Service mobile robots are often required to avoid dynamic objects while performing their tasks, but they usually have only limited computational resources. So we present a lightweight multi-modal framework for 3D object detection and trajectory prediction. Our system synergistically integrates LiDAR and camera inputs to achieve real-time perception of pedestrians, vehicles, and riders in 3D space. The framework proposes two novel modules: 1) a Cross-Modal Deformable Transformer (CMDT) for object detection with high accuracy and acceptable amount of computation, and 2) a Reference Trajectory-based Multi-Class Transformer (RTMCT) for efficient and diverse trajectory prediction of mult-class objects with flexible trajectory lengths. Evaluations on the CODa benchmark demonstrate superior performance over existing methods across detection (+2.03% in mAP) and trajectory prediction (-0.408m in minADE5 of pedestrians) metrics. Remarkably, the system exhibits exceptional deployability - when implemented on a wheelchair robot with an entry-level NVIDIA 3060 GPU, it achieves realtime inference at 13.2 fps. To facilitate reproducibility and practical deployment, we release the related code of the method at https://***/TossherO/3D Perception and its ROS inference version at https://***/TossherO/ros packages. Copyright © 2025, The Authors. All rights reserved.

关键词： Mobile robots

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Multi-aspect Optimal Sliding Mode controller for a Mecanum Wheeled Robot

Multi-aspect Optimal Sliding Mode Controller for a Mecanum W...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Sohrab Allahyari Hamid Rahmanei S. Ali A. Moosavian Dept. of Mechanical Engineering Center of Excellence in Robotics & Control Advanced Robotics & Automated Systems (ARAS) Laboratory K. N. Toosi University of Technology Tehran

Four-wheel drive Mecanum robots have gained attention due to their ability to move in all directions, which allows them to work in tight and complex environments. For this reason, ensuring precise control of these robots along predefined trajectories is crucial. This work presents a comparative study on the effects of parametric uncertainties, external disturbances, measurement noises, and initial deviations on the trajectory-tracking performance of a 4-wheel drive Mecanum robot. First, the kinematic and kinetic models of the Mecanum robot are introduced. Then, the integral sliding mode control is designed, and proof of stability is provided. In addition, we explore other control strategies, including PID, feedback linearization, sliding mode, and adaptive sliding mode control, that are designed for this robot. Furthermore, the particle swarm optimization algorithm is hired for the controllers with a cost function as the total sum of average error, maximum error, and final error. Optimization has been done on all controllers to compare the results fairly. To appraise the effectiveness of the proposed control optimization methods, simulations are carried out in MATLAB/Simulink. The results reveal that the proposed ISMC method performs best in both defined scenarios compared to other controllers. However, it must be noted that the ISMC controller exhibits limitations when facing significant uncertainties in the dynamic model. This study underscores the effectiveness of our proposed control method and provides valuable insights into the practical implementation controller for the Mecanum robot in real-world applications.

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Trajectory Tracking of Robotic Arms Using Learning Modified Transpose Jacobian

Trajectory Tracking of Robotic Arms Using Learning Modified ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Danial Ansari S Ali A Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics & Automated Systems (ARAS) Laboratory K. N. Toosi University of Technology Tehran Iran

In this research, the issue of trajectory tracking in task space of a robotic arm with rotational joints has been solved. For this purpose, a non-model based controller the modified jacobian-transpose is used which it’s performance has been improved by reinforcement learning. Traditionally, the settings of the error thresholds for this controller will require trial and error, but in this algorithm, reinforcement learning is used to adjust these parameters online and according to the conditions. The simulations for the desired trajectory indicate that the improvement of the trajectory tracking achieved by using proposed algorithm. Also, the superiority of this controller in noisy conditions compared to the reference Jacobian transpose controller has been shown.

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Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 146-159页

作者： Yinlong Zhang Wei Liang Mingze Yuan Hongsheng He Jindong Tan Zhibo Pang Key Laboratory of Networked Control Systems Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Computing Wichita State UniversityWichita 67260 USA Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeTN 37996 USA Department of Automation Technology ABB Corporate Research SwedenVasteras 72178Sweden Department of Intelligent Systems Royal Institute of Technology(KTH)Stockholm 11428Sweden

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment ***,traditional calibrations suffer inaccuracy and *** address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying *** our method,the spatial relationship is geometrically correlated between the sensing units and robotic *** decoupled estimations on rotation and translation could reduce the coupled errors during the ***,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and *** calibration has been evaluated on our developed *** the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,*** comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

关键词： Calibration inertial measurement unit(IMU) monocular camera robotic arm spiral moving trajectory

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