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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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Prior Knowledge Informed Neural Network for Subsurface Flow Prediction

Prior Knowledge Informed Neural Network for Subsurface Flow ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Data-driven deep learning has been widely applied to solve subsurface flow problems. However, it is difficult to achieve accurate prediction with pure data-driven methods, especially when there is only a small amount of valid data. Prior knowledge is pre-existent and comes from an independent source. The neural network can be informed by prior knowledge to improve algorithm performance even with limited and sparse data. In this study, prior knowledge informed neural network (PKINN) is proposed to predict subsurface flow. The prior knowledge, on the one hand, is embedded in the neural networks as integrated loss function to inform the training process of deep learning methods. On the other hand, it is used as part of input term of neural network to improve calculation accuracy. Subsurface flow models are built to verify the proposed PKINN model. The results show that the PKINN method can solve subsurface flow problems effectively.

关键词： Knowledge engineering Deep learning Training Industrial electronics Solid modeling Accuracy Neural networks

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Neural network equivalent model for highly efficient massive data classification

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Science China(Information Sciences) 2023年第3期66卷 287-288页

作者： Siquan YU Zhi HAN Yandong TANG Chengdong WU School of Information Science and Engineering Northeastern University Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Faculty of Robot Science and Engineering Northeastern University

Dear editor, Recently, neural networks(NNs) have been successfully applied to massive data classification tasks [1, 2]. However,there is an inconsistent problem in neural network structure and convergence [3]. On one hand, under the overparameterize assumption, it requires to design a complex network to guarantee the convergence [4].

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Bayesian mentalizing model for the Prisoner's Dilemma 3

Bayesian mentalizing model for the Prisoner's Dilemma

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3rd International Conference on Biomedical and Intelligent systems, IC-BIS 2024

作者： Zhu, Changbo Tang, Yandong Tang, Fengzhen Si, Bailu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China School of Systems Science Beijing Normal University Beijing100875 China Chinese Institute for Brain Research Beijing Beijing102206 China

ISBN: (纸本)9781510681279

This study proposes a Bayesian agent based on a hierarchical Bayesian model and Bayesian decision theory as a mentalizing model of players involved in the prisoner's dilemma, aiming to simulate the decision-making process of humans in the game. By mimicking the cognitive reasoning process of humans, this model seeks to enhance the understanding of cooperative and betrayal behaviors of biological agents in the Prisoner's Dilemma, thus providing a robust computational model framework for social cognition research. Moreover, we demonstrate through simulations that the model effectively tracks its own state and that of its partner, as well as makes effective gaming decisions. © 2024 SPIE.

关键词： Bayesian networks

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Robust-Tube Model Predictive Control for Piezoelectrically Actuated Fast Mechanical Switches for Hybrid Circuit Breakers 2

Robust-Tube Model Predictive Control for Piezoelectrically A...

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2nd International Conference on Artificial Intelligence and Automation Control, AIAC 2024

作者： Chai, Yu Yang, Chen Dong, Huijuan Zhao, Jie Shardt, Yuri A. W. Ju, Bingfeng ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China Technical University of Ilmenau Department of Automation Engineering Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331529147

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%. © 2024 IEEE.

关键词： Predictive control systems

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Analysis of normal force of wheel-sand interaction for planetary rovers with active suspension*

Analysis of normal force of wheel-sand interaction for plane...

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IEEE International Conference on robotics and Biomimetics

作者： Zhengyin Wang Huaiguang Yang Liang Ding Qingning Lan Haibo Gao Zdravko Terze Zongquan Deng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Faculty of Mechanical Engineering and Naval Architecture University of Zagreb Zagreb Croatia

The planetary rover with active suspension can climb steeper slopes and cross bigger obstacles in a wheel-step motion mode, which enables the planetary rover to perform more complex exploration tasks. However, wheel-step motion requires coordinated movement of the suspension and the wheels: the suspension can determine the wheel's sinkage depth and translation speed. Matching up with the wheel rotation speed can actively construct the slip ratio. The forces exerted on the wheels by sand during this process are a key index for motion control. This poses new challenges to existing wheel-sand interaction models. In this paper, dynamic normal force tests are carried out. The dynamic variation of wheel normal force with slip ratio is investigated at different sinkage depths. It is found that the normal force exerted on the wheel increases firstly and then decreases with the slip ratio, and reaches the maximum value near s=0. The distributed stresses of the wheel were analysed by discrete element method (DEM) simulation, and the phenomenon of the distribution stress reconstruction of the wheel is found. Further, by analyzing the local particles in contact with the wheel, it is found that the particle movement direction and morphology vary with slip ratio. The analysis of the wheel’s normal force and the granular material variation with slip ratio provides the basis for establishing a physics-based wheel-sand interaction model.

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K2MUSE: A human lower limb multimodal dataset under diverse conditions for facilitating rehabilitation robotics

arXiv

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arXiv 2025年

作者： Li, Jiwei Zhang, Bi Tan, Xiaowei Chen, Wanxin Liu, Zhaoyuan Zhang, Juanjuan Huo, Weiguang Huang, Jian Liu, Lianqing Zhao, Xingang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences No.114 Nanta street Liaoning Shenyang110016 China

The natural interaction and control performance of lower limb rehabilitation robots are closely linked to biomechanical information from various human locomotion activities. Multidimensional human motion data significantly deepen the understanding of the complex mechanisms governing neuromuscular alterations, thereby facilitating the development and application of rehabilitation robots in multifaceted real-world environments. However, currently available lower limb datasets are inadequate for supplying the essential multimodal data and large-scale gait samples necessary for effective data-driven approaches, and they neglect the significant effects of acquisition interference in real applications. To fill this gap, we present the K2MUSE dataset, which includes a comprehensive collection of multimodal data, comprising kinematic, kinetic, amplitude-mode ultrasound (AUS), and surface electromyography (sEMG) measurements. The proposed dataset includes lower limb multimodal data from 30 able-bodied participants walking under different inclines (0◦, ±5◦, and ±10◦), various speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s), and different nonideal acquisition conditions (muscle fatigue, electrode shifts, and inter-day differences). The kinematic and ground reaction force data were collected via a Vicon motion capture system and an instrumented treadmill with embedded force plates, whereas the sEMG and AUS data were synchronously recorded for thirteen muscles on the bilateral lower limbs. This dataset offers a new resource for designing control frameworks for rehabilitation robots and conducting biomechanical analyses of lower limb locomotion. The dataset is available at https://***/. © 2025, CC BY-NC-SA.

关键词： Gait analysis

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter 8

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Lin, Qiguang Yan, Chaojie Li, Qiang Ling, Yonggen Zheng, Yu Lee, Wangwei Wan, Zhaoliang Huang, Bidan Liu, Xiaofeng College of IoT Engineering Hohai University Jiangsu Key Laboratory of Special Robotic Technology Jiangsu Changzhou213022 China Zhejiang University Institute of Cyber-System and Control State Key Laboratory of Industrial Control and Technology Hangzhou China Bielefeld33619 Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350300178

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks. © 2023 IEEE.

关键词： Extended Kalman filters

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Adaptive RISE Control for Dual-Arm Unmanned Aerial Manipulator systems with Deep Neural Networks

arXiv

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arXiv 2025年

作者： Wang, Yang Yu, Hai Wu, Shizhen Yang, Zhichao Han, Jianda Fang, Yongchun Liang, Xiao Institute of Robotics and Automatic Information System College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin300350 China

The unmanned aerial manipulator system, consisting of a multirotor UAV (unmanned aerial vehicle) and a manipulator, has attracted considerable interest from researchers. Nevertheless, the operation of a dual-arm manipulator poses a dynamic challenge, as the CoM (center of mass) of the system changes with manipulator movement, potentially impacting the multirotor UAV. Additionally, unmodeled effects, parameter uncertainties, and external disturbances can significantly degrade control performance, leading to unforeseen dangers. To tackle these issues, this paper proposes a nonlinear adaptive RISE (robust integral of the sign of the error) controller based on DNN (deep neural network). The first step involves establishing the kinematic and dynamic model of the dual-arm aerial manipulator. Subsequently, the adaptive RISE controller is proposed with a DNN feedforward term to effectively address both internal and external challenges. By employing Lyapunov techniques, the asymptotic convergence of the tracking error signals are guaranteed rigorously. Notably, this paper marks a pioneering effort by presenting the first DNN-based adaptive RISE controller design accompanied by a comprehensive stability analysis. To validate the practicality and robustness of the proposed control approach, several groups of actual hardware experiments are conducted. The results confirm the efficacy of the developed methodology in handling real-world scenarios, thereby offering valuable insights into the performance of the dual-arm aerial manipulator system. Copyright © 2025, The Authors. All rights reserved.

关键词： Lyapunov functions

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Research on Collision Detection Algorithm for Human-SRL Collaborative Motion Planning 4

Research on Collision Detection Algorithm for Human-SRL Coll...

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2022 4th International Symposium on robotics and Intelligent Manufacturing Technology, ISRIMT 2022

作者： Zhang, Xiaobo Liu, Jinguo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China China University of Chinese Academy of Sciences Beijing100049 China

The existing collision detection algorithms are commonly based on triangle patch models, which leads to complex detection logic. Aiming at this problem, this paper proposes a new collision detection algorithm. It has simple detection logic and fast detection speed, and it is mainly used for human-SRL cooperative motion planning. The algorithm is based on a human-SRL skeleton model and a multi-sphere envelope method and includes four sub-algorithms, namely, a multi-sphere envelope sub-algorithm, an SRL collision detection sub-algorithm, a collision detection sub-algorithm between SRL and human, and a collision detection sub-algorithm between SRL and environment objects. This paper first introduces the pseudo-code of the collision detection algorithm. Secondly, the implementation principles of its four sub-algorithms are expounded. Finally, the correctness of the collision detection algorithm is verified by simulation. © Published under licence by IOP Publishing Ltd.

关键词： Motion planning

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