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Dynamic bimanual human-to-robot object handovers using motion prediction deep neural networks

Dynamic bimanual human-to-robot object handovers using motio...

引用

2025 ieee/SICE International symposium on System Integration, SII 2025

作者： Mavsar, Matija Uchibe, Eiji Morimoto, Jun Ude, Ales Jožef Stefan Institute Dept. of Automatics Biocybernetics and Robotics Ljubljana Slovenia ATR Computational Neuroscience Laboratories Dept. of Brain-Robot Interface Kyoto Japan Kyoto University Graduate School of Informatics Kyoto Japan

ISBN: (纸本)9798331531614

Facilitating dynamic bimanual handovers between humans and robots is a complex endeavor that requires integrating human pose estimation, motion prediction, and generating appropriate trajectories for receiving robots. This study introduces a method designed to predict required bimanual receiver robot motion during handover tasks, leveraging pose estimation and motion prediction networks. Additionally, we propose a real-time control approach for a dual-arm humanoid robot to dynamically adjust its receiving trajectories. We evaluate the ability of neural networks to accurately predict receiver trajectories and thus improve the handover process. We compare long short-term memory (LSTM) and transformer architectures for motion prediction and also assess prediction accuracy of both absolute and relative receiving trajectories as well trajectories for each separate robot arm, and show that the use of absolute and relative coordinates is beneficial for generating more accurate receiver motions. © 2025 ieee.

关键词： Trajectories

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Offline and Online Energy Simulation using Virtual Commissioning Models with Extended Dynamical Behavior

Offline and Online Energy Simulation using Virtual Commissio...

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2025 ieee/SICE International symposium on System Integration, SII 2025

作者： Deubert, Darius Pfeifer, Denis Scheifele, Christian Fehr, Jorg Verl, Alexander Bosch Rexroth AG Lohr am Main97816 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart70174 Germany ISG Industrielle Steuerungstechnik GmbH Stuttgart70563 Germany Institute of Engineering and Computational Mechanics University of Stuttgart Stuttgart70174 Germany

ISBN: (纸本)9798331531614

Considering energy consumption and dynamical behavior within the simulation of manufacturing systems emerges as an important objective for their development, commissioning, and operation. Currently, these aspects are often neglected in many fields including virtual commissioning. This work presents an approach to extend highly detailed virtual commissioning models of components from industrial automation by dynamics models for the calculation of energy consumption in an offline simulation and for reuse in an online simulation. To validate the presented approach, an exemplary offline virtual commissioning simulation of a pick-and-place robot application is implemented and subsequently transferred to an online simulation. The validation shows a good accuracy of the simulated energy consumption, enabling virtual commissioning engineers to analyze and optimize energy consumption already in the design phase of manufacturing systems. Additionally, the dynamical behavior was well reflected in the online simulation, contributing to the capability of simulation in the operation phase. © 2025 ieee.

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SMILES-ANN for Vapor-liquid Phase Equilibrium Prediction 8

SMILES-ANN for Vapor-liquid Phase Equilibrium Prediction

引用

8th International symposium on Computer Science and Intelligent control, ISCSIC 2024

作者： Xue, Jianfei Zhang, Wei Hebei University of Technology School of Artificial Intelligent Tianjin China Hebei University of Technology School of Artificial Intelligent Control Engineering Technology Innovation Center of Hebei Province Tianjin China

ISBN: (纸本)9798350380286

Both the pretreatment of raw materials (mixtures) and the separation of products and by-products of products require the use of phase equilibrium properties as a theoretical basis. The current thermodynamic equations used for phase equilibrium calculations are often affected by their own system limitations and the complexity of parameter estimation when performing phase equilibrium calculations. In order to weaken the system limitations and computational complexity of the thermodynamic equations, this paper designs the SMILES-ANN model for the prediction of vapor-liquid phase equilibrium, and in order to test the performance of the model, the constant temperature and pressure phase equilibrium dataset of an alcoholic system, which contains about 5,000 data points, is produced, and the bubble point temperature/pressure and dew point temperature/pressure are computed by the model on the dataset. The final predicted mean absolute errors (MAE) for pressure and temperature at equilibrium were 0.0098 and 0.0093, respectively, and the corresponding molar fraction errors for the liquid and gas phases were 0.0480 and 0.0393. © 2024 ieee.

关键词： Temperature

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Model Predictive Velocity control of Electrical Drives on an Industrial-PC

Model Predictive Velocity Control of Electrical Drives on an...

引用

2022 International symposium on Power Electronics, Electrical Drives, automation and Motion, SPEEDAM 2022

作者： Karau, Fabian Leuer, Michael Bielefeld University of Applied Science Faculty of Engineering and Mathematics Bielefeld Germany

ISBN: (数字)9781665484596

ISBN: (纸本)9781665484596

Accurate position and velocity control of electrical drives is required in many industrial applications such as milling machines and robots. A cascade control based on PI controllers is widespread. However, problems arise with complex nonlinear drive systems. A powerful solution approach is the model predictive control (MPC). But the high computational effort required makes implementation difficult, especially with older servo drives. This paper shows that MPC can also be implemented on an Industrial-PC (IPC), which is available in many industrial plants anyway. To solve dead time problems the augmented state space representation is used. © 2022 ieee.

关键词： Model predictive control

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Improved Adaptive DBSCAN for Data Cleaning in Molten Iron Weighing Process 13

Improved Adaptive DBSCAN for Data Cleaning in Molten Iron We...

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13th ieee Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Feng, Yu Li, Xiaoli Yu, Xiaowei Wang, Kang Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9798350361674

In the intricate process of steel manufacturing, the precise measurement of molten iron is a pivotal procedure, which directly impacts the quality of steel production. Rail weighbridges are commonly deployed in the steel industry for molten iron measurement. Therefore, the accuracy of rail weighbridges weighing data is extremely important. However, due to process intricacies and equipment nuances, weighbridge weight data often exhibits outliers. This problem can potentially hinder data-centric modeling and predictive tasks. To solve this problem, this paper introduces a data cleaning methodology based on an Improved Adaptive Density-Based Spatial Clustering of Applications with Noise (IA-DBSCAN). The proposed data cleaning method is rooted in an improved adaptive DBSCAN approach, which is particularly effective in identifying clusters of arbitrary shapes and can differentiate noise from actual clusters. The application of the IA-DBSCAN algorithm facilitates the identification and cleaning of concentrated outliers. The algorithm dynamically seeks eps and MinPts parameters to achieve optimal clustering outcomes. Constraints have been incorporated into the improved algorithm, enabling it to achieve data cleaning more rapidly and accurately. After data cleaning, based on the characteristics of the selected dataset in this paper, data imputation using the linear regression method can align with the original data characteristics. Experimental results indicate that the proposed method can effectively identify and clean outliers data in the dataset, supplement missing values based on the original data characteristics, and construct a complete and anomaly-free dataset, establish a robust foundation for modeling and prediction. © 2024 ieee.

关键词： Steelmaking

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Load Comparison Between Two controlling Strategies for Wind Energy Conversion System Power-Boosting 16

Load Comparison Between Two Controlling Strategies for Wind ...

引用

16th ieee International symposium on Applied computational intelligence and Informatics, SACI 2022

作者： Bordeasu, Dorin Prostean, Octavian Vasar, Cristian Debes, Ali Department of Automation and Applied Informatics Timişoara Romania Control Engineering Siemens Gamesa Brande Denmark

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

This paper explains in detail the development, validation and simulation of a standard PI controller that can boost the WECS (Wind energy conversion system) power through two strategies. In the first strategy, the power is boosted by increasing the generator angular velocity through the direct increase of the angular velocity of the wind turbine rotor by slightly pitching the blades. In the second strategy, the power is boosted by increasing the load torque of the generator, through the direct increase of the load torque of the wind turbine rotor. For achieving this goal, the following tasks have been accomplished: a wind energy conversion system nonlinear mathematical model has been developed, a gain scheduled proportional-integral (PI) controller as the baseline controller, and two gain scheduled proportional-integral (PI) power-boosting controllers for the two power-boosting strategies. For the power-boosting controllers, it is also presented a logic that switches between baseline and power booster controllers when certain wind speeds conditions are met. The response, stability, performance and the increase in the shaft torsion, tower and blades bending moments of the gain scheduled PI power-boosting controllers have been compared with the baseline controller. By comparing the output power response of the controllers, it can be seen that the power booster controllers boost the power when the defined conditions are met. Finally, for boosting the power of a WECS in certain conditions the most efficient way is by increasing the generator load torque, because this strategy increases the least the loading of the main components of a WECS. This power boosting strategy implementation it might require the change (or reinforcement) of the drive train shaft, if it cannot take the extra loading created by power boosting controller. © 2022 ieee.

关键词： controllers

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Neural-Dynamics-Based Active Steering control for Autonomous Vehicles with Noises

Neural-Dynamics-Based Active Steering Control for Autonomous...

引用

ieee symposium on Intelligent Vehicle

作者： Ying Liufu Long Jin Fei-Yue Wang School of Information Science and Engineering Lanzhou University Lanzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This paper introduces a neural-dynamics-based active steering control (NDASC) scheme developed under artificial systems, computational experiments, and parallel execution (ACP) framework, aimed at enhancing the stability and reliability of autonomous vehicles in noisy environments. Based on the Taylor expansion theorem, noises can be represented in the form of polynomials for the desired accuracy, and therefore polynomial noises can be viewed as a more generalized representation of noises. Then, the proposed NDASC scheme includes a model predictive active steering control (MPASC) strategy solved by a polynomial noise resilience neural dynamics (PNRND) model. computational experiments parallelly implemented upon the CarSim-Simulink platform substantiate the effectiveness and robustness of the proposed NDASC scheme, providing significant theoretical and practical insights for control strategies of autonomous vehicles under various noisy environments.

关键词： Noise Predictive models Polynomials Stability analysis Robustness Noise measurement Vehicle dynamics

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

引用

2023 ieee/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Peng, Juewen Pan, Zhiyu Liu, Chengxin Luo, Xianrui Sun, Huiqiang Shen, Liao Xian, Ke Cao, Zhiguo Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control China Nanyang Technological University S-Lab Singapore

ISBN: (纸本)9798350302493

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge. © 2023 ieee.

关键词： Color photography

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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM 5

An Approach for Upper Limb Movement Intention Recognition Us...

引用

5th World Robot Conference symposium on Advanced Robotics and automation, WRC SARA 2023

作者： Zhang, Weiming Zhang, Xiaodong Xu, Cheng Zhou, Guchuan Zhang, Teng Wang, Yu Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Shaanxi Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

ISBN: (纸本)9798350307320

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively. © 2023 ieee.

关键词： Electroencephalography

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Gaussian Process-Based Adaptive Sliding Mode Tracking control for Robotic Manipulators 4

Gaussian Process-Based Adaptive Sliding Mode Tracking Contro...

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4th World Robot Conference (WRC) / symposium on Advanced Robotics and automation (SARA)

作者： Li, Tong Sun, Liang Jiang, Jingjing Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Artificial Intelligence Beijing 100083 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England

ISBN: (数字)9781665463690

ISBN: (纸本)9781665463690

A Gaussian process (GP) based adaptive sliding mode control algorithm is proposed for tracking control of robotic manipulators with dynamical uncertainties. Gaussian process is used to obtain a data-driven model which can compensate for the unknown dynamics of the *** sliding mode controller with parameter adaptive law is designed to bring the trajectory to the normalized tracking and to force the trajectory close to the sliding surface. Stability analysis based on the Lyapunov theorem is given to guarantee the tracking control performance. Numerical simulations on a 2-DOF robotic manipulator verify the effectiveness of the proposed controller.

关键词： data-driven model adaptive sliding mode tracking control robotic manipulators

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