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SSRN 2024年

作者： Qin, Biao Liu, Henan Cheng, Jian Tian, Jinchuan Sun, Jiangang Zhou, Zihan Ma, Chuanzhen Chen, Mingjun State Key Laboratory of Robotics and System Harbin Institute of Technology P.O. Box 413 Heilongjiang Harbin150001 China Center for Precision Engineering Harbin Institute of Technology P.O. Box 413 Heilongjiang Harbin150001 China

The hemispherical resonant gyroscope (HRG), distinguished by its simple structure, high reliability, and remarkable precision, stands out as one of the most promising high-performance inertial navigation devices in advanced equipment. However, the core sensitive component of the HRG, known as hemispherical resonator (HSR), with a complex and small curvature structure made of fused silica, experiences subsurface damage (SSD) during the grinding process. This not only leads to brittle fracture, which affects the operational performance of the gyroscope, but also significantly hampers the polishing efficiency of the component. This study was initiated by quantitatively characterizing the SSD of various structural features of the HSR (center rod, lip edge of the spherical shell, and spherical shell). Especially, for the curved-surface of the HSR, the average deviation of SSD detection result is within 6%. Subsequently, based on the characterized SSD, the grinding damage mechanisms of the HSR was explored. The grinding surface integrity is then elucidated from the perspective of maximum undeformed chip thickness. Finally, a predictive model for the SSD depth of the HSR after ultra-precision grinding was established according to the nonlinear relationship between the surface roughness and SSD depth. The maximum deviation between the predictive and experimental values of SSD depth is 9.4%, indicating a high level of predictive accuracy. This work fills the blank in SSD detection of the HSR, laying a theoretical foundation for the high-performance manufacturing of the HSR. © 2024, The Authors. All rights reserved.

关键词： Damage detection

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MAPPG: Multi-Agent Phasic Policy Gradient

MAPPG: Multi-Agent Phasic Policy Gradient

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IEEE Conference on Decision and Control

作者： Qi Zhang Xuetao Zhang Yisha Liu Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin China

We propose a Multi-Agent Phasic Policy Gradient (MAPPG) algorithm, which can assist agents to further alleviate the non-stationarity of the environment. Different from the existing methods, the auxiliary phase is introduced to train the local policy directly by using the environment state, which can be naturally integrated into other algorithms. Specifically, the hidden layer feature sharing is proposed, which ensures feature sharing between the global value network and the local policy network for the first time. Meanwhile, mirror descent is utilized to iteratively update the policy in the auxiliary stage, which makes the policy update more robust. Through a series of evaluations on multi-agent Particle and multi-agent Mujoco benchmark environments, the experimental results show that our method achieves higher rewards than state-of-the-art benchmarks.

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Topology-Guided Perception-Aware Receding Horizon Trajectory Generation for UAVs

Topology-Guided Perception-Aware Receding Horizon Trajectory...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Gang Sun Xuetao Zhang Yisha Liu Hanzhang Wang Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence also School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence the Institute of Robotics and Automatic Information System (lRAIS) Nankai University Tianjin China

The perception-aware motion planning method based on the localization uncertainty has the potential to improve the localization accuracy for robot navigation. How-ever, most of the existing perception-aware methods pre-build a global feature map and can not generate the perception- aware trajectory in real time. This paper proposes a topology- guided perception-aware receding horizon trajectory generation method, which contains a topology-guided position trajectory generation and a perception-aware yaw angle trajectory generation. Specifically, a memorable active map is built by selectively storing the visual landmarks. After that, a library of candidate topological trajectories are generated, which are then evaluated in terms of the perception quality based on the active map, smoothness, collision possibility and feasibility. In addition, the yaw angle trajectory is obtained through a front-end multiple refined path search and a back-end path- guided trajectory optimization. Comparative simulation and real-world experiments are carried out to confirm that the proposed method can keep more visual features in view and reduce the localization error.

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Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse Optimal Control 13

Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse O...

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13th Asian Control Conference, ASCC 2022

作者： Qiu, Tianyu Zhang, Han Wang, Jingchuan Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9788993215236

In this paper, a traditional one v.s. one pursuit and evasion scenario is considered. The evader aims to reach her target and avoid being captured by the pursuer, while the pursuer, without the knowledge of the evader's cost function, intends to capture the evader before she successfully escapes. We formulate this problem as a Model Predictive Control Decoupled Pursuit and Evasion Game (MPC-DPEG) model with incomplete information. We apply Inverse Optimal Control (IOC) technique to estimate the evader's cost function and give a Nash pursuit strategy for MPC-DPEG as the optimal strategy for the pursuer. Numerical examples reveal the improvement of pursuit performance of the Nash pursuit strategy based on the estimated cost function over naive pursuit strategies. © 2022 ACA.

关键词： Simulation Optimal control Games Predictive models Cost function Numerical models Predictive control

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Lower Limb Rehabilitation Motion Angle Measurement Based On Deep Learning YOLOv3 Model 2

Lower Limb Rehabilitation Motion Angle Measurement Based On ...

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2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Yang, Yunlong Wang, Liancheng Yao, Yufeng He, Chenxi Yin, Hesheng Weihai Furui Robotics Co. Ltd Weihai China Tianzhi Institute of Innovation and Technology Weihai Economic and Technological Development Zone Weihai China Weihai China State Key Laboratory of Robotics Harbin Institute of Technology System Department of Mechanical Engineering Harbin China

ISBN: (纸本)9783800757398

The aging of the population and the high incidence of hemiplegia have led to an increasing demand for easy-to-use rehabilitation training. The feedback sensing system which can measure and analyze the lower limb rehabilitation motions is highly significant for improving the rehabilitation outcome. Computer vision-based human motion angle measurement has attracted significant interest. This study aims to measure and analyze the lower limb motion angle in the sagittal plane with a single RGB camera. This paper proposes a method for extracting and monitoring of the lower limb marker points based on YOLOv3 and DarkNet-53 convolutional neural networks, and optimizes the pixel coordinates of the target point based on Kalman. The measurement accuracy of the proposed method is tested by JACO robotic arm, and the test shows that the standard deviation (SD) of the measurement is less than 0.5°. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Neural networks

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Analysis of Kinematic Relationship of Superposition Cooperative Dual-Industrial Robot 2020

Analysis of Kinematic Relationship of Superposition Cooperat...

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2020 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2020

作者： Kong, Minxiu Wang, Rundong Cheng, Yanchun Yang, Jubo School of Mechatronics Engineering Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin China

ISBN: (纸本)9781450372022

With the increasing complexity of manufacturing process, the ability of a single robot is not enough for the current diversified production environment and tasks, so the research on multi-robot technology is increasingly important. This article selects the most widely used superposition cooperative dual robot as the object, studies a base-based calibration algorithm based on the space common target coordinate system, and then establishes the trajectory constraint relationship of the dual robot on this basis, which provides theoretical basis for teaching work. Finally, the correctness of the algorithm is verified by simulation software. © 2020 ACM.

关键词： Industrial robots

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Robotic dual-arm patch clamp for neuron excitability research under mechanical stimulation

Robotic dual-arm patch clamp for neuron excitability researc...

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第43届中国控制会议

作者： Biting Ma Chaoyu Cui Ruimin Li Minghui Li Jinyu Qiu Ke Li Mingzhu Sun Xin Zhao Qili Zhao National Key Laboratory of Intelligent Tracking and Forecasting fo Infectious Diseases Engineering Research Center of Trusted BehaviorIntelligence Ministry of Education Tianjin Key Laboratory of intelligentRobotic (tikLIR) Institute of Robotics and Automatic Information System(IRAlS) Nankai University Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The dual-arm patch clamp technique, using one micropipette electrode to patch the target cell to record its electric signals with the other micropipette mechanically stimulating the target cell, is a significant technique in neuroscience for studying neuron excitability under mechanical stimulation. A key step for dual-arm patch clamp operation is to coordinate two micropipettes to reach the target cells in the narrow brain tissue environment. The traditional manual operations face issues such as inaccurate workspace estimation, clumsy movements, and collision risks of the two micropipettes, resulting in low operational efficiency. Addressing the above issues, a robotic dual-arm patch clamp method was developed for neuron excitability research under mechanical stimulation in this paper. First, the reachable workspace of the dual-arm was calculated based on a kinematics model. Then, a robotic dual arm patch clamp operation procedure is proposed to approach, whole-cell patch, and mechanically stimulate the target cell. The experiment results on neurons in mouse brain slices show that the success rate of dual micropipette contacting cell surface reaches up to 90%(18/20) at an average speed of 6 mins/cell, which are both significantly improved in comparison to manual operation. An increase in neuron excitability in the primary visual cortex(V1) was found under the mechanical stimulation, proving the feasibility of the proposed system for studying the impact of mechanical stimulation on ion channel activities.

关键词： Robotic Patch Clamp Robotic Cell Measurement Workspace Calculation Whole-cell Recording Mechanical Stimulation

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A cooperative positioning approach of unmanned aerial vehicles with accuracy and robustness

A cooperative positioning approach of unmanned aerial vehicl...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Zhou, Ziwei Lin, Ziying Ni, Yi Dong, Wei Zhu, Xiangyang Shanghai Jiao Tong University Robotics Institute School of Mechanical Engineering Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781665436786

Efficient localization is crucial for the autonomous navigation of Unmanned Aerial Vehicles (UAVs) in GPS-denied environments. Current onboard visual positioning approach may easily deteriorate with varying illumination and unpredicted obstacles. To tackle this issue, a novel cooperative positioning approach is proposed in this paper. This approach is comprised of a marker-based position estimation and an end-to-end UAV detection. For the marker-based method, a prior known pattern is attached on UAV and the Perspective-n-point (PnP)algorithm is implemented to provide an accurate estimation of the UAV's position. Meanwhile, to enhance the localization robustness, an end-to-end detection method is designed based on FCOS neural network and ultra-wideband (UWB) range measurement. Then, with an additional data fusion, the aforementioned two methods are integrated eventually. In this way, the accuracy and robustness of the localization can be enhanced accordingly. Extensive experiments under different environments are carried out to verify the effectiveness of the proposed method. Results demonstrate that the proposed method can estimate the flight trajectory of UAV with high accuracy and robustness in cluttered environments with varying illumination. © 2021 IEEE.

关键词： Data fusion

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Terminal Orbital Adaptive Dynamic Programming-based Control for Satellite Pursuit Evasion Game with Input Saturation

Terminal Orbital Adaptive Dynamic Programming-based Control ...

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Shuoheng Ma Zhiqiang Ma Bo Zhang Panfeng Huang Research Center for Intelligent Robotics School of Astronautics Northwestern Polytechnical University Xi’an China National Key Laboratory of Aerospace Flight Dynamics Northwestern Polytechnical University Xi’an China Aerospace System Engineering Shanghai Shanghai China

A continuous space pursuit-evasion game algorithm based on zero-sum game is proposed to solve the problem of the terminal pursuit-evasion control of spacecraft with input saturation. A novel single-network adaptive dynamic programming method is proposed to obtain the solution of zero-sum game. Firstly, it is assumed that the two satellites are close such that the nonlinear dynamics could be reduced to the CW equations. Considering the input saturation, a non-quadratic cost function is proposed to formulate a Hamilton-Jacobi-Isaacs equation. Then, only a critic neural network is employed to learn the optimal value function and further obtain the optimal controller, which enables the architecture of adaptive dynamic programming implementation to be simpler. An NN weight updating law is constructed, by which the restriction of initial admissible control is removed. Based on the Lyapunov theory, the convergence of critic NN weights and the stability of closed-loop system are guaranteed. Finally, numerical results verify the efficiency of the proposed method.

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Design of a Novel Autonomous Docking system With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

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

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.

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