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IEEE International Conference on systems, Man and Cybernetics

作者： Ning Tan Peng Yu Fenglei Ni Zhenglong Sun School of Computer Science and Engineering Sun Yat-sen University Guangzhou PR China State Key Laboratory of Robotics and Systems (HIT) Harbin PR China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9781665442084

Bio-inspired robots, e.g., soft continuum robots, have broad application prospects due to their structural dexterity and interaction safety. But these features also bring great challenges to the precise control of soft continuum robots. In this work, we investigate how to achieve the kinematic control of soft continuum robots without knowing model parameters of the robots. To this end, a model-free scheme based on varying-parameter recurrent neural networks (VP-RNN) is proposed. The scheme involves two components, one of which solves the inverse kinematics problem based on a VP-RNN model, and the other employs another VP-RNN model to estimate the pseudo-inverse of Jacobian matrix of continuum robots. Finally, the feasibility and robustness of the proposed control strategy are validated by simulations, including comparisons with other methods and case study with jammed actuation.

关键词： Jacobian matrices Recurrent neural networks Trajectory tracking Prototypes Kinematics Interference Robustness

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Ranging-aided Aerobridge Navigation Using Dual Quaternion Based Multiplicative Extended Kalman Filter

Ranging-aided Aerobridge Navigation Using Dual Quaternion Ba...

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International Symposium on Autonomous systems (ISAS)

作者： Ruican Xia Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China

ISBN: (数字)9781665418829

ISBN: (纸本)9781665447997

The autopilot of aerobridge has attracted considerable attention recently since human error and high labor costs. To estimate the pose of an aerobridge, this paper proposes a navigation algorithm with the aid of pairwise distances and inertial measurements. We first locate the joints of the aerobridge with the distance measurement. Then the pose of the aerobridge is calculated using its kinematic in closed-form and the associated covariance is derived. A dual quaternion based multiplicative extended Kalman filter is designed to fuse inertial measurements with the estimated pose, which produces a smooth pose. To verify the precision of this algorithm, a simulation is constructed based on robotics Toolbox and the experiment results are positive.

关键词： Navigation Fuses Robot kinematics Quaternions Pose estimation Kinematics Kalman filters

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Robotic transcatheter tricuspid valve replacement with hybrid enhanced intelligence: a new paradigm and first-in-vivo study

arXiv

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arXiv 2024年

作者： Wang, Shuangyi Lin, Haichuan Xie, Yiping Wang, Ziqi Chen, Dong Tan, Longyue Hou, Xilong Chen, Chen Zhou, Xiao-Hu Lin, Shengtao Pan, Fei So, Kent Chak-Yu Hou, Zeng-Guang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100043 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong Jenscare International Co. Limited Hong Kong Division of Cardiology Department of Medicine and Therapeutics Faculty of Medicine The Chinese University of Hong Kong Hong Kong Division of Cardiology Department of Medicine and Therapeutics Prince of Wales Hospital Hong Kong

Transcatheter tricuspid valve replacement (TTVR) is the latest treatment for tricuspid regurgitation and is in the early stages of clinical adoption. Intelligent robotic approaches are expected to overcome the challenges of surgical manipulation and widespread dissemination, but systems and protocols with high clinical utility have not yet been reported. In this study, we propose a complete solution that includes a passive stabilizer, robotic drive, detachable delivery catheter and valve manipulation mechanism. Working towards autonomy, a hybrid augmented intelligence approach based on reinforcement learning, Monte Carlo probabilistic maps and human-robot co-piloted control was introduced. Systematic tests in phantom and first-in-vivo animal experiments were performed to verify that the system design met the clinical requirement. Furthermore, the experimental results confirmed the advantages of co-piloted control over conventional master-slave control in terms of time efficiency, control efficiency, autonomy and stability of operation. In conclusion, this study provides a comprehensive pathway for robotic TTVR and, to our knowledge, completes the first animal study that not only successfully demonstrates the application of hybrid enhanced intelligence in interventional robotics, but also provides a solution with high application value for a cutting-edge procedure. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Lane clearance for emergency vehicle passage in connected and automated environment: A graph convolutional soft actor-critic method

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Expert systems with Applications 2025年 290卷

作者： Xu Yang Meng Li Ke Zhang Qianchuan Zhao Yaming Guo The Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing 100084 PR China Department of Civil Engineering State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing 100084 PR China Department of Civil Engineering Tsinghua University Beijing 100084 PR China The Center for Intelligent and Networked Systems Department of Automation and BNRist Tsinghua University Beijing 100084 PR China

In urban traffic systems, the rapid response of emergency vehicles (EV) is critical for improving rescue success rates and minimizing property damage. The emergence of connected and autonomous vehicle (CAV) technology offers new opportunities to enhance the efficiency through lane clearance. However, existing research on lane clearance for EV remains limited, with current mathematical models facing challenges such as low control precision and long computational time. Thus, we formulate the lane clearance problem as a multi-agent reinforcement learning (MARL) task and propose a parameter-sharing distributed training framework that efficiently clears lanes for EV while minimizing disruptions to regular traffic. Furthermore, we introduce the Graph Convolutional Soft Actor-Critic (GSAC) algorithm, which integrates Graph Convolutional Networks (GCN) with a reinforcement learning controller. GSAC is specifically designed for dynamic traffic scenarios characterized by evolving communication topologies. The algorithm employs a novel reward function that encourages both longitudinal and lateral cooperation among vehicles. Additionally, we introduce a lane clearance and re-acceleration strategy for long road segments, offering a comprehensive solution to the EV lane clearance problem. Experimental results demonstrate that our model outperforms three baseline methods in terms of safety, task completion rate, and traffic efficiency under both normal and hard scenarios. It also exhibits robust performance across varying traffic densities. Long-segment testing further shows that the model maintains stable, low-latency characteristics and supports efficient and scalable deployment even with small-scale training.

关键词： Connected and autonomous vehicles Emergency vehicle priority Lane clearance Multi-agent deep reinforcement learning Trajectory control and optimization

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The Heterogeneous Deployment Tool for Hardware and Software Co-design

The Heterogeneous Deployment Tool for Hardware and Software ...

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International Conference on Computer, Information and Telecommunication systems (CITS)

作者： Bingjie Zhao Ziyang Li Tao Zhang Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Acsdemy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781728165448

ISBN: (纸本)9781728165455

In order to solve the shortcomings of manually writing code that cannot meet the requirements of rapid development and product verification, we designed a heterogeneous deployment tool based on Simulink model development. This heterogeneous deployment tool can deploy the algorithm designed by Simulink model to the CPU and FPGA platforms and can communicate between CPU and FPGA via PCI Express. Users do not need to concern the underlying hardware and drivers and but only need to build an algorithm, they can quickly deploy the algorithm on a heterogeneous hardware platform to verify the performance of the algorithm.

关键词： Field programmable gate arrays Tools Mathematical model Software packages Hardware design languages Matlab Load modeling

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Gradient Eigen-decomposition Invariance Biogeography-based Optimization for Mobile Robot Path Planning

Research Square

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Research Square 2021年

作者： Wang, Jiaqian Na, Xiaodong Han, Min Li, Deicai Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Professional Technology Innovation Center of Distributed Control for Industrial Equipment of Liaoning Province Dalian University of Technology Dalian116024 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

The path planning for mobile robots has attracted extensive attention, and evolutionary algorithms have been applied to this problem increasingly. In this paper, we propose a novel gradient eigen-decomposition invariance biogeography-based optimization (GEI-BBO) for mobile robot path planning, which has the merits of high rotation invariance and excellent search performance. In GEI-BBO, we design an eigen-decomposition mechanism for migration operation, which can reduce the dependence of biogeography-based optimization (BBO) on the coordinate system, improve the rotation invariance and share the information between eigen solutions more effectively. Meanwhile, to find the local optimal solution better, gradient descent is added, and the system search strategy can reduce the occurrence of local trapping phenomenon. In addition, combining the GEI-BBO with cubic spline interpolation will solve the problem of mobile robot path planning through a defined coding method and fitness function. A series of experiments are implemented on benchmark functions, whose results indicated that the optimization performance of GEI-BBO is superior to other algorithms. And the successful application of GEI-BBO for path planning in different environments confirms its effectiveness and practicability. © 2021, CC BY.

关键词： Mobile robots

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D-VPnet: A Network for Real-time Dominant Vanishing Point Detection in Natural Scenes

arXiv

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arXiv 2020年

作者： Bo Liu, Yin Ming, Zeng Meng, Qing-Hao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin300072 China

As an important part of linear perspective, vanishing points (VPs) provide useful clues for mapping objects from 2D photos to 3D space. Existing methods are mainly focused on extracting structural features such as lines or contours and then clustering these features to detect VPs. However, these techniques suffer from ambiguous information due to the large number of line segments and contours detected in outdoor environments. In this paper, we present a new convolutional neural network (CNN) to detect dominant VPs in natural scenes, i.e., the Dominant Vanishing Point detection Network (D-VPnet). The key component of our method is the feature line-segment proposal unit (FLPU), which can be directly utilized to predict the location of the dominant VP. Moreover, the model also uses the two main parallel lines as an assistant to determine the position of the dominant VP. The proposed method was tested using a public dataset and a Parallel Line based Vanishing Point (PLVP) dataset. The experimental results suggest that the detection accuracy of our approach outperforms those of state-of-the-art methods under various conditions in real-time, achieving rates of 115fps. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Novel Methods Inspired by Reinforcement Learning Actor-Critic Mechanism for Eye-in-Hand Calibration in robotics

Novel Methods Inspired by Reinforcement Learning Actor-Criti...

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IEEE International Conference on Development and Learning, ICDL

作者： Chenxing Li Yinlong Liu Yingbai Hu Fabian Schreier Jan Seyler Shahram Eivazi Department of Computer Science University of Tübingen Tübingen Germany Advanced Development Analytics and Control Festo SE and Co. KG Esslingen Germany State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) University of Macau Macau China HUA Cloud Intelligent Healthcare Co. Ltd. Ningbo China Multi-Scale Medical Robotics Centre and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong China

Eye-in-hand camera calibration is an essential step in robotic vision systems. Traditional methods perform the calibration via geometry computation methods where the calibration points are acquired by moving the robot arm while capturing images of a static object (often a checkerboard) of known geometry. Our work aims to explore deep reinforcement learning (DRL) techniques for solving common calibration optimization problems. Inspired by the actor-critic mechanism of DRL, we propose a novel approach to perform eye-in-hand camera calibration on the robot arm. Numerical and real-world experiments prove the feasibility of the proposed method as such here we show the potential of the reinforcement learning method to solve the camera calibration problem.

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Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression

arXiv

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arXiv 2020年

作者： Liu, Yin-Bo Zeng, Ming Meng, Qing-Hao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin300072 China

Unstructured road vanishing point (VP) detection is a challenging problem, especially in the field of autonomous driving. In this paper, we proposed a novel solution combining the convolutional neural network (CNN) and heatmap regression to detect unstructured road VP. The proposed algorithm firstly adopts a lightweight backbone, i.e., depthwise convolution modified HRNet, to extract hierarchical features of the unstructured road image. Then, three advanced strategies, i.e., multi-scale supervised learning, heatmap super-resolution, and coordinate regression techniques are utilized to achieve fast and high-precision unstructured road VP detection. The empirical results on Kong’s dataset show that our proposed approach enjoys the highest detection accuracy compared with state-of-the-art methods under various conditions in real-time, achieving the highest speed of 33 fps. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Coordinate dispatch of combined heat and power system based on approximate dynamic programming 4

Coordinate dispatch of combined heat and power system based ...

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4th IEEE Conference on Energy Internet and Energy System Integration, EI2 2020

作者： Hu, Bo Liu, Ding Zang, Chuanzhi Wang, Yue Li, Wanting Liu, Yuqi Zeng, Peng State Grid Liaoning Electric Power Co. Ltd. Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control System Liaoning Shenyang China Chinese Academy of Sciences University Beijing China Technical Service Center Shenyang Liaoning Lightning Protection Liaoning China

ISBN: (纸本)9781728196060

Combined heat and power (CHP) can simultaneously produce electrical and thermal energy. CHP is an important and main component of the power system in areas with heat demand. However, its operating mode limits the flexibility of the system and aggravates the waste of renewable energy. This paper presents a coordinate dispatch algorithm for electric heating integrated energy system (EHIES), a combined heat and power system with wind power. An efficient optimization algorithm based on dynamic programming (DP) is developed, which overcomes the challenges of solving high dimensional optimization problems for the EHIES. The results of simulation experiments show that the proposed coordinate dispatch algorithm can meet the scheduling time requirements, reduce the operating cost of the system and realize the full consumption of wind power. © 2020 IEEE.

关键词： Wind power

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