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arXiv 2025年

作者： Tang, Kang Xu, Sheng Yang, Yuqi Kong, He Ma, Yongsheng Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science University of Nottingham Nottingham United Kingdom Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China

This paper focuses on static source localization employing different combinations of measurements, including time-difference-of-arrival (TDOA), received-signal-strength (RSS), angle-of-arrival (AOA), and time-of-arrival (TOA) measurements. Since sensor-source geometry significantly impacts localization accuracy, the strategies of optimal sensor placement are proposed systematically using combinations of hybrid measurements. Firstly, the relationship between sensor placement and source estimation accuracy is formulated by a derived Cramér-Rao bound (CRB). Secondly, the A-optimality criterion, i.e., minimizing the trace of the CRB, is selected to calculate the smallest reachable estimation mean-squared-error (MSE) in a unified manner. Thirdly, the optimal sensor placement strategies are developed to achieve the optimal estimation bound. Specifically, the specific constraints of the optimal geometries deduced by specific measurement, i.e., TDOA, AOA, RSS, and TOA, are found and discussed theoretically. Finally, the new findings are verified by simulation studies. Copyright © 2025, The Authors. All rights reserved.

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED

A High-Precision Edge Detection Algorithm Based on Improved ...

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IEEE International Conference on Cyber Technology in Automation, control, and intelligent systems

作者： Yeborui Yuan Wenyuan Chen Jie Tang Jia Yang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology.

关键词： Automation Image edge detection Image processing Neural networks control systems Iterative algorithms intelligent systems

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Learning-Based Model Predictive control for Vehicles with Modeling Bias

Learning-Based Model Predictive Control for Vehicles with Mo...

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Chinese control Conference (CCC)

作者： Xudong Qi Libin Zhang Ping Wang Yu Han Wanquan Lin School of Electronics and Control Engineering Chang'an University Xi'an P.R. China School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen P.R.China Guangdong Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou P.R.China

The dynamic uncertainty system has modeling bias, which has led to an increasing interest in learning-based model-driven control technology. In order to integrate the advantages of self-exploration of reinforcement learning (RL) and high robustness of model predictive control (MPC), we propose a learning-based model predictive control (LMPC) method. This algorithm is a method that can find the optimal control strategy under the guidance of simplified model and fuzzy cost function. The results show that taking vehicle motion control as an example. By adjusting the variable horizon, the vehicle model built can quickly and effectively achieve the control objectives. Compared with DDQN, LMPC reduces the disorder enumeration of RL, accelerates the optimization process of the control system to the target, and makes the exploration process more stable. Compared with MPC, it solves the problem that the control accuracy of the control system is not high and the model and parameters cannot be adjusted adaptively under the simplified modeling.

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Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment

Towards Efficient Trajectory Generation for Ground Robots be...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jingping Wang Long Xu Haoran Fu Zehui Meng Chao Xu Yanjun Cao Ximin Lyu Fei Gao State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Huzhou Institute of Zhejiang University Huzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Applicaion Innovation Laboratory Huawei Technologies Co. Ltd.

With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in $\mathbb{R}^{3}$ such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm.

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Design of control System for Lower Limb Rehabilitation Robot on the Healthy Side sEMG Signal

Design of Control System for Lower Limb Rehabilitation Robot...

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IEEE International Conference on Mechatronics and Automation

作者： Shiqin Yu Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

With the number of stroke patients increasing year by year, rehabilitation exoskeleton robot has been paid more and more attention. For the rehabilitation exoskeleton robot, human-computer interaction ability is an important index, which affects the effect of rehabilitation therapy to a great extent. Surface Electromyography (sEMG) signals are the combined effect of sEMG signals and electrical activities on nerve stem on the skin surface, which can reflect neuromuscular activities in advance and can be used to predict movement intention by sEMG signals. Therefore, this article proposes to use sEMG signals to monitor the motion information of the healthy leg in real-time, extract the characteristics of the electromyography signals, use the sEMG of the healthy leg as the control signal, reflect the motion patterns reflected by the sEMG collected from the healthy side, and then use the motion intention recognition method of Long Short Term Memory(LSTM) neural network to identify the motion intention of the prosthetic limb by identifying the motion patterns of the swing phase of the healthy side. The results indicate that the predicted maximum Root Mean Squared Error(RMSE) is 5.3729, which proves the feasibility of using LSTM model for motion intention recognition and contributes to the real-time and accuracy of lower limb exoskeleton rehabilitation.

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Study on 6D Pose Estimation System of Occlusion Targets for the Spherical Amphibious Robot based on Neural Network

Study on 6D Pose Estimation System of Occlusion Targets for ...

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IEEE International Conference on Mechatronics and Automation

作者： Chaofeng Du Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications in Complicated systems and Intelligent Robot Laboratory Tianjin University of Technology BinshuiXidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

The amphibious robot needs to accurately estimate the 6D pose of the target in tasks such as target tracking, docking with the recovery module, and target grasping. The current research on target 6D pose estimation is mainly applied to unoccluded targets, but when the target is occluded, the target’s pose cannot be accurately identified. Compared with other algorithms, the PVNet algorithm shows better robustness when target is occluded, but the accuracy is still low. To improve the accuracy of the PVNet algorithm, this paper adds the confidence score prediction of the prediction vector at the last layer of the PVNet network, and designs a vector confidence score loss function to train the network. Before generating the hypothetical keypoints, the pixels whose confidence score is lower than the set threshold are screened out, so that the generated hypothetical 2D keypoints are closer to the true 2D keypoints. Finally, the method in this paper is compared with the Tekin, PoseCNN, Oberweger and Pvnet algorithm, and demonstrate the superiority of the proposed method.

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Study on control Strategy of the Vascular Interventional Surgical Robot Based on LADRC

Study on Control Strategy of the Vascular Interventional Sur...

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IEEE International Conference on Mechatronics and Automation

作者： Wenqiang Zhang Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications In Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5Zhongguancun South Street Beijing China

As a medical device used in surgery, the vascular interventional surgical robot itself needs to have good control accuracy to ensure the safety of the surgery process. Given this security problem, this paper selects the Line Active Disturbance Rejection control (LADRC) as the control strategy of the vascular interventional robot, aiming at improving the master-slave tracking accuracy of the vascular interventional robot. This article compares the LADRC and PID(Proportional-Integral-Derivative) control strategies through simulation and experiments. The experimental results show that LADRC has good control performance for master-slave tracking and can be applied to the vascular interventional surgical robots.

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Study on Autonomous Surgical control System of The Vascular Interventional Surgical Robot Based on Catheter Tip Location Tracking

Study on Autonomous Surgical Control System of The Vascular ...

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IEEE International Conference on Mechatronics and Automation

作者： Zhimin Feng Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications In Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5Zhongguancun South Street Beijing China

The control system of vascular interventional surgical robot is a complex nonlinear system. When the catheter is inserted into human blood vessels, due to the irregular and curved shape of human blood vessels, the catheter will undergo small deformation, which will affect the displacement of the catheter. In order to reduce the displacement tracking error, a BP neural network PID controller was designed to compensate the displacement error of the autonomous vascular interventional surgical robot. Firstly, the dynamic analysis is carried out according to the axial displacement motion of the vascular interventional surgical robot, and the catheter displacement tracking controller was designed. According to the displacement error, BP neural network PID control is used to output three parameters of PID. Finally, in the experiment and PID control simulation comparison, the results show that the BP neural network PID control has a small displacement tracking error and superior tracking performance, which can meet the requirements.

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A DDPG-Based Method of Autonomous Catheter Navigation in Virtual Environment

A DDPG-Based Method of Autonomous Catheter Navigation in Vir...

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IEEE International Conference on Mechatronics and Automation

作者： Wei Tian Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications In Complicated systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5Zhongguancun South Street Beijing China

Vascular interventional surgery is the main method for treating cardiovascular diseases. But navigating endovascular catheters through the vascular tree is a highly challenging task even for highly trained specialists. Automation of this task can reduce the burden on surgeons and is expected to improve the surgical outcomes. Although there have been relevant studies utilizing reinforcement learning algorithms to realize autonomous navigation of catheter in the virtual environment Cathsim. However, the kinematics model of the catheter in Cathsim does not conform to the operating mode of the catheter in real vascular interventional surgery. Besides, there are problems such as low success rates of catheter autonomous navigation tasks. To address these issues, this paper modifies the kinematics model of the catheter in Cathsim and designs a catheter autonomous navigation model based on reinforcement learning DDPG (Deep Deterministic Policy Gradient) algorithm. The experimental results show that the agents trained through DDPG in this paper performs better than the agents trained through PPO (Proximal Policy optimization) in other studies in terms of navigation task success rate, completion time, and contact force between the catheter and vascular wall during the navigation process.

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Robust Output Synchronization of Discrete-Time Linear-Time-Invariant Multi-Agent systems Using Phase Tool

Robust Output Synchronization of Discrete-Time Linear-Time-I...

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European control Conference (ECC)

作者： Xin Mao Dan Wang Wei Chen Li Qiu School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Southern University of Science and Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong S.A.R. China

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.

关键词： Sufficient conditions Europe Heterogeneous networks Synchronization Multi-agent systems

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