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Collision and Deadlock Avoidance in Multi-Robot systems Based on Glued Nodes

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1327-1330页

作者： Zichao Xing Xinyu Chen Xingkai Wang Weimin Wu Ruifen Hu The authors are with the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China IEEE

Dear editor,In this letter,we would like to discuss a method to avoid collisions and deadlocks in multi-robot systems based on a new concept of glued *** terms of collision and deadlock avoidance,many methods are based on zone control which has two ***,unless all nodes are collision-free,the roadmap must be divided into disjoint zones,which increases the difficulty of applying the ***,each zone should be able to accommodate a robot,which leads to imprecision and waste of *** letter proposes the concept of glued nodes,which can dynamically determine the mutual influence between nodes based on the real-time sizes and paths of the *** on the glued nodes,this letter proposes a collision and deadlock avoidance algorithm,which can be applied to multi-robot systems with variable-sized robots and roadmaps with any *** experimental results indicate that the method proposed in this letter is effective and efficient.

关键词： lock collision letter

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Recognition of Coil Position Information in Dynamic Wireless Charging System Based on Multiple Linear Regression 49

Recognition of Coil Position Information in Dynamic Wireless...

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49th Annual Conference of the IEEE industrial Electronics Society, IECON 2023

作者： Xue, Xuchi Liu, Zhitao Liu, Jia Chen, Wenjie Zhang, Mengting Yue, Jiawang Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9798350331820

The fluctuation in system efficiency caused by coil misalignment in wireless power transfer has drawn the attention of researchers in the ***, this paper proposes a method to obtain coil misalignment. By utilizing the acquired coil position information, anti-misalignment correction can be applied to enhance system efficiency. Firstly, two auxiliary coils are added at the receiving end. Then, the misalignment between the X and Y axes is converted into polar coordinates. The output current and coil position misalignment angle are used as inputs to establish a multiple linear regression model for identifying the receiving end's position information. Finally, a random motion trajectory is set and simulated in the system to validate the feasibility of the proposed model. © 2023 IEEE.

关键词： Multiple linear regression

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Exact Output Regulation for the Receiver-Side Buck Converter of Electric Vehicle Dynamic Wireless Charging System 49

Exact Output Regulation for the Receiver-Side Buck Converter...

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49th Annual Conference of the IEEE industrial Electronics Society, IECON 2023

作者： Zhang, Mengting Liu, Zhitao Su, Hongye Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9798350331820

Dynamic wireless charging (DWC) of electric vehicles (EVs) is a promising technology that can promote the widespread of EVs. However, the output power fluctuation occurs due to the varying mutual inductance between the receiver coil and the transmitter coils caused by the EV motion, which would lead to control performance deterioration and even instability of the system. To mitigate the power fluctuation, this paper proposes a control strategy based on exact output regulation (EOR) theory for the receiver-side buck converter of the DWC system to compensate the disturbance arising from the mutual inductance fluctuation. The mutual inductance fluctuation can be approximated as a sinusoidal signal and the FEA analysis informanation is used to obtain an predefined exosystem that can characterize the dynamics of the mutual inductance fluctuation. A state feedback controller combined with a state observer is designed to achieve a constant output voltage of the DWC system by compensating for such disturbance. The simulation results verify the effectiveness and superiority of the proposed control strategy as compared with the traditional control methods. © 2023 IEEE.

关键词： Electric vehicles

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Adaptive and Robust Terrain Classification control Algorithm for a Spherical Robot 49

Adaptive and Robust Terrain Classification Control Algorithm...

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49th Annual Conference of the IEEE industrial Electronics Society, IECON 2023

作者： Wang, Yixu Liu, Yifan Lin, Boyu Guan, Xiaoqing Hu, Tao Zhang, Ziang Wang, You Li, Guang Institute of Cyber Systems and Control Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9798350331820

This article proposes an adaptive and robust terrain classification control algorithm for a pendulum-driven spherical robot, aiming to solve the problem of insufficient control accuracy caused by using the same controller for different terrains. The common terrains are classified into three categories, and a terrain classification dataset is established based on the vibration signal of the robot. Using LightGBM, combined with the feature window and window voter algorithm proposed in this article, the terrain classification results are corresponded with three proposed controllers. Physical experiment results show that the proposed classification control algorithm can work stably in different terrains, guiding the spherical robot to select the optimal controller to improve its motion performance. © 2023 IEEE.

关键词： controllers

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Residual Tensor Train: A Quantum-Inspired Approach for Learning Multiple Multilinear Correlations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1101-1113页

作者： Chen, Yiwei Pan, Yu Dong, Daoyi Zhejiang University Institute of Cyber-Systems and Control College of Control Science and Engineering Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Hangzhou310027 China University of New South Wales School of Engineering and Information Technology CanberraACT2600 Australia

states of quantum many-body systems are defined in a high-dimensional Hilbert space, where rich and complex interactions among subsystems can be modeled. In machine learning, complex multiple multilinear correlations may also exist within input features. In this article, we present a quantum-inspired multilinear model, named residual tensor train (ResTT), to capture the multiple multilinear correlations of features, from low to high orders, within a single model. ResTT is able to build a robust decision boundary in a high-dimensional space for solving fitting and classification tasks. In particular, we prove that the fully connected layer and the Volterra series can be taken as special cases of ResTT. Furthermore, we derive the rule for weight initialization that stabilizes the training of ResTT based on a mean-field analysis. We prove that such a rule is much more relaxed than that of tensor train (TT), which means that ResTT can easily address the vanishing and exploding gradient problem that exists in the existing TT models. Numerical experiments demonstrate that ResTT outperforms the state-of-the-art tensor network and benchmark deep learning models on MNIST and Fashion-MNIST datasets. Moreover, ResTT achieves better performance than other statistical methods on two practical examples with limited data, which are known to have complex feature interactions. © 2020 IEEE.

关键词： Neural networks

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Learning Adaptive Manipulation of Objects with Revolute Joint: A case Study on Varied Cabinet Doors Opening 42

Learning Adaptive Manipulation of Objects with Revolute Join...

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42nd Chinese control Conference, CCC 2023

作者： Yu, Hongxiang Guo, Dashun Zhou, Zhongxiang Wang, Yue Xiong, Rong The Institute of Cyber-Systems and Control The State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou310058 China

ISBN: (纸本)9789887581543

This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constraints of the objects. To open various kinds of doors, we add encoded environment parameters that define the various environments to the input of out policy. To transfer the policy into the real world, we train an adaptation module in simulation and fine-tune the adaptation module to cut down the impact of the policy-unaware environment parameters. We design a series of experiments to validate the efficacy of our framework. Additionally, we testify to the model's performance in the real world compared to the traditional door opening method. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Reinforcement learning

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Autonomous Trajectory Planning Based on Bézier Curve with Curvature Constraints and Piecewise-Jerk Speed-Time Optimization

Autonomous Trajectory Planning Based on Bézier Curve with C...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Wang, You Zou, Ziyi Zhang, Ziang Guan, Xiaoqing Lin, Boyu Li, Xiang Li, Guang Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Hangzhou310027 China

ISBN: (纸本)9798350325706

In this paper, we propose an efficient trajectory planning algorithm with path smoothing based on the Bézier curve with curvature constraints and piecewise-jerk speed-time optimization. We use hybrid A∗ to generate a rough path and construct a safe corridor by inflating the path. After that, we formulate the smooth problem as a nonlinear programming(NLP) with piecewise Bézier curves. Since the curvature constraints for Bézier curves are difficult, we employ quartic Bézier Curves with special forms and compute the closed-form solution for the maximum curvature to simplify the representation of the maximum curvature. By using the special Bézier curves, we realize the gear shifts and easily guarantee the security, continuity, and feasibility of the path. Meanwhile, we add time variables based on PJSO, improving the quality of trajectory within an acceptable increase in time, making the allocation of time and speed better. Simulation and real-world experiments with a car-like robot in various environments confirm that our algorithm can generate a smooth, feasible, and high-quality trajectory for robots. © 2023 IEEE.

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Design of Dynamic Wireless Charging System Based on Coupling Coefficient Estimation

Design of Dynamic Wireless Charging System Based on Coupling...

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International Conference on Wireless Power Transfer, ICWPT 2022

作者： Xue, Xuchi Liu, Zhitao Liu, Jia Chen, Wenjie Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789819906307

During the dynamic wireless charging process, the coupling coefficient between the wireless charging transmitter coil and the receiver coil varies greatly, thereby reducing the charging efficiency. Firstly, the change law of the magnetic coupling coefficient of the magnetic coupling mechanism in the process of dynamic wireless charging is analyzed. Secondly, the wireless charging network is analyzed to derive the relationship of the duty cycle under the optimal load. According to the changing law of the coupling coefficient, this paper analyzes the Kalman prediction model and the cubic exponential smoothing model, and proposes an improved error adaptive model based on the two algorithms. When the coupling coefficient changes rapidly, the error of the coefficient estimation can improve the efficiency of the whole wireless charging system. The method proposed in this paper can effectively improve the dynamic wireless charging efficiency by 9.5%, which is conducive to the popularization of electric AGV in the fields of transportation and logistics sorting. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Improved hybrid Gaussian process regression Model for state-of-health Estimation of Li-ion Batteries

Improved hybrid Gaussian process regression Model for State-...

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2024 China Automation Congress, CAC 2024

作者： An, Tao Liu, Zhitao Yan, Chizhou Xu, Ke Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Wanxiang A123 Systems Corp. Hangzhou China

ISBN: (纸本)9798350368604

Accurate state-of-health (SOH) estimation contributes to the healthy and stable operation of the Li-ion batteries. This paper proposes an improved hybrid Gaussian process regression (GPR) method incorporating physical information for the high-accuracy estimation of battery SOH. The estimation process considers the effects of temperature, discharge rate, and depth of discharge (DOD) on the battery health, and the relevant features of the electrode degradation process are extracted for battery SOH estimation. The kernel function of GPR comprehensively considers the mechanical characteristics of battery degradation and can effectively track the battery degradation process. The validation experiments were conducted on the A123 LFP/Graphite battery dataset. Sufficient comparative experiments have validated the accuracy and robustness of the proposed method. © 2024 IEEE.

关键词： state of charge

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A Novel Trajectory Planning Method Based on Trust Region Policy Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Bao-Lin Zhang, Jiajie Li, Lingxi Wu, Weimin Jiaxing Key Laboratory of Smart Transportations the School of Information Science and Engineering Jiaxing University China Elmore Family School of Electrical and Computer Engineering Purdue University Indianapolis USA State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Trajectory planning method is a research hotspot in autonomous driving. Existing reinforcement learning-based trajectory planning methods suffer from unstable performance due to the strong randomness of network weight parameter updates during the training process. Therefore, this paper proposes a novel trajectory planning method based on deep reinforcement learning trust region policy optimization (TRPO). Firstly, in order to enhance the robustness of the trajectory planning method based on deep reinforcement learning TRPO, a TRPO-LSTM based decision model was proposed. More specifically, a long short term memory (LSTM) based state feature extraction network was designed and embeded into a TRPO-based decision model to enhance the ability of TRPO to extract information from the environmental state space. Secondly, in order to make the planned trajectory adaptive to the dynamic changes of traffic environment, we presented a novel TRPO-LSTM trajectory fitting algorithm. To the best of our knowledge, this is the first work aiming at applying the TRPO-LSTM based decision model in the trajectory fitting process to search the optimal longitudinal trajectory speed. Finally, the proposed trajectory planning method was implemented and simulated on the CARLA simulator. The experimental results show that, compared with existing trajectory planning methods based on deep reinforcement learning algorithms, our proposed method achieves a cumulative reward improvement of over 28.9% in the scenario of four lane highway, and has better robustness. Meanwhile, the proposed method can achieve a lower collision rate of 0.93% while improving the average speed and comfort of vehicle driving. IEEE

关键词： Heuristic algorithms

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