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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Yuan, Ting Zhang, Chi Yi, Feng Lv, Pingping Li, Shupei Zhang, Meitong Ningbo University Ningbo Instituteof Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo315201 China University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences School of Electrical Engineering Ningbo315201 China

ISBN: (纸本)9798350312201

In this paper, an adaptive position control method combining proportional-derivative (PD) control, RBF neural network-based sliding mode (SM) control, and feedforward (FF) control, i.e., PD-FF-SM control, is proposed to deal with the dynamic uncertainties, disturbances, and slow response in lower limb exoskeleton robot system. The Lagrange method is utilized to establish dynamic models that ignore the uncertainties and disturbances in the swing phase and supporting phase. Then, the PD feedback controller is designed with a sliding surface function, to keep the system stable. The FF controller is adopted to improve the response by using dynamic models. And the RBF neural network-based SM controller is designed to compensate for the uncertainties and disturbances. The system's stability is verified by Lyapunov's theorem. The simulation results show that the proposed control method can effectively reduce the error of position tracking and response time with inaccurate dynamic modeling. © 2023 IEEE.

关键词： Sliding mode control

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Study of Helicopter Optimal Autorotation Landing Procedure in Tail Rotor Drive Failure

Study of Helicopter Optimal Autorotation Landing Procedure i...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yan, Xufei Chen, Renliang Zhu, Shiqiang Xie, Anhuan Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics College of Aeronautics Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

This paper investigates the optimal landing trajectory and control procedure when a helicopter undergoes autorotation due to tail rotor drive failure (TRDF), in which an optimal control methodology is proposed. First, a helicopter flight dynamics model with TRDF was developed. Then, the autorotation in TRDF was converted to be a nonlinear optimal control problem, solved by direct node collocation method and sequential quadratic programming algorithm. Finally, a model helicopter (Z11) with single main rotor and tail rotor was used to demonstrate the proposed approach. An optimal autorotation landing procedure in TRDF was determined accordingly. Results indicate that the airframe will immediately respond to the excess torque generated by the main rotor via yawing, sideslip and rolling when TRDF occurs. The pilot is recommended to shut down the engine and perform a series of critical operations to stabilize the violent yaw and roll movements. In addition, flight test data were used to validate the numerical simulations of autorotation landing. The proposed optimal control approach provides a useful tool to investigate helicopter TRDF autorotation landing procedure. © 2022 IEEE.

关键词： Helicopters

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Marrying NeRF with Feature Matching for One-step Pose Estimation

arXiv

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arXiv 2024年

作者： Chen, Ronghan Cong, Yang Ren, Yu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China College of Automation Science and Engineering South China University of Technology China

Given the image collection of an object, we aim at building a real-time image-based pose estimation method, which requires neither its CAD model nor hours of object-specific training. Recent NeRF-based methods provide a promising solution by directly optimizing the pose from pixel loss between rendered and target images. However, during inference, they require long converging time, and suffer from local minima, making them impractical for real-time robot applications. We aim at solving this problem by marrying image matching with NeRF. With 2D matches and depth rendered by NeRF, we directly solve the pose in one step by building 2D-3D correspondences between target and initial view, thus allowing for real-time prediction. Moreover, to improve the accuracy of 2D-3D correspondences, we propose a 3D consistent point mining strategy, which effectively discards unfaithful points reconstruted by NeRF. Moreover, current NeRF-based methods naively optimizing pixel loss fail at occluded images. Thus, we further propose a 2D matches based sampling strategy to preclude the occluded area. Experimental results on representative datasets prove that our method outperforms state-of-the-art methods, and improves inference efficiency by 90×, achieving real-time prediction at 6 FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Robot applications

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Optimal Landing of Tilt-rotor Aircraft after Engine Failure Considering Pilot Inherent Limitations

Optimal Landing of Tilt-rotor Aircraft after Engine Failure ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yan, Xufei Chen, Renliang Zhu, Shiqiang Xie, Anhuan Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China College of Aeronautics Nanjing University of Aeronautics and Astronautics National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics Jiangsu Nanjing China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

An augmented longitudinal rigid-body model is developed with a set of algebra equations describing the controls in the cockpit and the differential equations describing the pilot inherent limitations. The landing procedure after one engine failure is formulated into a nonlinear optimal control problem. XV-15 tilt-rotor aircraft is taken as the sample for the demonstration of landing in one engine failure during short takeoff. The results show that the total power required, thrust coefficient, longitudinal flapping angle and optimal controls are more relatively gentle than the solutions without considering the pilot inherent limitations. Compared with the basic longitudinal rigid-body model, the optimal solutions involve more control information. © 2022 IEEE.

关键词： Differential equations

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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Functional Connectivity Analysis Revealed Abnormal Brain Network Pattern in Parkinson's Disease with Freezing of Gait 42

Functional Connectivity Analysis Revealed Abnormal Brain Net...

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42nd Chinese Control Conference, CCC 2023

作者： Shu, Zhilin Liu, Jinrui Song, Ting Han, Jianda Yu, Ningbo College of Artificial Intelligence Nankai University Tianjin300350 China Nankai University Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Tianjin300350 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

ISBN: (纸本)9789887581543

Patients with Parkinson's disease (PD) commonly have deficits in lower-limb movements. Freezing of gait (FOG), a type of gait disturbance of PD, is a complex motor symptom exhibited as inability to generate an effective gait. The mechanism of lower-limb dysfunction of PD, especially FOG, remains to be explored. To investigate the neural mechanisms related to FOG in PD, we evaluated 39 subjects (13 PD patients with FOG, 13 PD patients without FOG, and 13 healthy subjects) based on electroencephalogram (EEG) signals while they performed the pedaling task. We conducted functional connectivity analysis using Magnitude Squared Coherence (MSC) and calculated the metrics to investigate the characteristics of the brain network among the three groups. The results indicated that PD patients were associated with reduced functional integration and segregation of brain network, subserving both motor and cognitive abilities. The reduced functional integration and segregation of cognitive processes were correlated with the severity of FOG. This study revealed the differences in brain networks between PD patients and healthy controls and provided insights into the neural mechanism of FOG. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Electroencephalography

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Detection of Positional Accuracy Degradation of Industrial Robots Based on Kinematic Analysis

Detection of Positional Accuracy Degradation of Industrial R...

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Electrical, Automation and Computer engineering (ICEACE), International Conference on

作者： Kongping Yang Yuming Qi Bing Xie Sanpeng Deng Kaidi Mo Tianjin Key Laboratory of Intelligent Robot Technology and Application Institute of Robotics and Intelligent Equipment Tianjin University of Technology and Education Tianjin China College of Mechanical Engineering Tianjin University of Science and Technology Tianjin China School of Technology Beijing Forestry University Beijing China

Aiming at the problem of positional accuracy degradation of industrial robots in long-term operation, a positional accuracy degradation detection method of industrial robots based on kinematic analysis is proposed. Firstly, the accuracy of the joint angles collected from the robot control cabinet and external high-precision sensors is compared and analyzed based on the MD- H method; secondly, the positional degradation detection model of the industrial robot is established based on the principle of differential kinematics; and finally, an experimental platform is built based on the KUKA KR 3 R540 industrial robot and experimental validation is carried out. The experimental results show that the root-mean-square error between the detected position error and the actual position error is 0.049 mm, and the method can effectively detect the position accuracy degradation of the robot.

关键词：

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A Novel Surface Electromyography Signals Denoising Method for Gait Phase Classification 42

A Novel Surface Electromyography Signals Denoising Method fo...

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42nd Chinese Control Conference, CCC 2023

作者： Sun, Yubo Guo, Zhenhui Feng, Yuxin Yu, Ningbo College of Artificial Intelligence Nankai University Tianjin300350 China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin300350 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute Nankai University Shenzhen518083 China

ISBN: (纸本)9789887581543

Surface electromyography (sEMG) is a non-invasive technique for recording electrical activity and is extensively employed in gait phase classification. However, the measurement process of the sEMG signal inevitably introduces noise. Consequently, denoising is required for gait analysis from the collected sEMG signal. A novel denoising method ICEEMDAN-DFA-WD has been proposed in this paper. The sEMG signals were decomposed into several intrinsic mode functions (IMF) through an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The Hurst exponents were simulated by detrended fluctuation analysis (DFA) to distinguish noisy IMF and desired IMF. During the walking process, the lower limbs' sEMG exhibits strong periodicity, and the desired IMF yields higher Hurst exponents than the noisy IMF. For the noisy IMF, noise removal was performed through further decomposition by wavelet transform, followed by a hard threshold. The processed IMFs were reconstructed to denoise sEMG. In this study, experiments were conducted to compare with the existing denoising methods WD, EMD-IIT, EEMD-IWT, VMD-SIT. The proposed method can obtain better denoising effect and gait phase classification accuracy. The proposed method provides a more effective way for sEMG denoising. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Empirical mode decomposition

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Dual-loop joint torque control based on lower limb exoskeleton robots 18

Dual-loop joint torque control based on lower limb exoskelet...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Meitong Zhang, Chi Yi, Feng Shu, Xindong Yuan, Ting Li, Zheng Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences School of Electrical Engineering Ningbo315201 China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo315201 China University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science and Technology School of Electrical Engineering Shijiazhuang050000 China

ISBN: (纸本)9798350312201

In the framework of impedance control, the torque trajectory of the joint is unpredictable due to nonlinear factors during the movement of the exoskeleton. To precisely predict command torque trajectory and compensate for the disturbances, we propose a dual-loop joint torque control system including predictive feedforward and a disturbance observer. A predictive feedforward module is adopted as the control outer loop based on the feedback control, and a general compensation module is used to compensate for the prediction error. The inner loop adopts a disturbance observer to estimate and compensate for low-frequency external disturbances. The simulation, which utilizes the actual torque of hip and knee joints as a reference, is conducted to prove the effectiveness of the proposed method. The comparison results demonstrate that the proposed control method can realize high bandwidth for exoskeleton joint control. © 2023 IEEE.

关键词： Torque control

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

引用

arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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