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Chinese control Conference (CCC)

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

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.

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

Recognition of Coil Position Information in Dynamic Wireless...

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Annual Conference of industrial Electronics Society

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

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.

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Event-Triggered Fully Distributed control: A Model-Free Adaptive Learning Algorithm

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IEEE Transactions on Automatic control 2025年

作者： Ma, Yong-Sheng Che, Wei-Wei Wu, Zheng-Guang Northeastern University College of Information Science and Engineering Shenyang266071 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China

This paper studies the consensus problem in multi-agent systems (MASs) under the challenge of an unknown system model and limited communication resources. A novel model-free adaptive learning algorithm is developed to learn the controller from system data. A model-based event-triggered fully distributed control (ET-FDC) algorithm is proposed to achieve consensus while saving the limited communication resources. Furthermore, a data-driven systematic learning methodology for the ET-FDC algorithm is introduced to eliminate the need for a system model. Compared with existing approaches, the main advantages of the proposed method are that it not only avoids using the system model and global topology information, but also reduces unnecessary communication. Finally, simulations are presented to illustrate the superiority of the theoretical results. © 2025 IEEE.

关键词： Contrastive Learning

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A Decoupled and Linear Framework for Global Outlier Rejection over Planar Pose Graph

A Decoupled and Linear Framework for Global Outlier Rejectio...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Tianyue Wu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

We propose a robust framework for planar pose graph optimization contaminated by loop closure outliers. Our framework rejects outliers by first decoupling the robust PGO problem wrapped by a Truncated Least Squares kernel into two subproblems. Then, the framework introduces a linear angle representation to rewrite the first subproblem that is originally formulated in rotation matrices. The framework is configured with the Graduated Non-Convexity (GNC) algorithm to solve the two non-convex subproblems in succession without initial guesses. Thanks to the linearity property of the angle representation, our framework requires only a linear solver to optimally solve the optimization problems encountered in GNC. We extensively validate the proposed framework, named DEGNC- LAF (DEcoupled Graduated Non-Convexity with Linear Angle Formulation) in planar PGO benchmarks. It turns out that it runs significantly (sometimes up to over 30 times) faster than the standard and general-purpose GNC while resulting in high-quality estimates.

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Simulated Ising Annealing Algorithm with Gaussian Augmented Hamiltonian Monte Carlo 42

Simulated Ising Annealing Algorithm with Gaussian Augmented ...

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42nd Chinese control Conference, CCC 2023

作者： Li, Langyu Wang, Hao Xie, Zhixin Liu, Zixuan Cui, Wei Pan, Yu Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China School of Mechanical Engineering and Automation Harbin Institude of Technology Shenzhen518055 China

ISBN: (纸本)9789887581543

A numerical technique called Simulated Ising Annealing (SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization (QUBO) problem in combinatorial optimization, which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo (GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Simulated annealing

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Hetero-Source Sensors Localization Based on High-Precision Map

Hetero-Source Sensors Localization Based on High-Precision ...

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Chinese Intelligent Automation Conference, CIAC 2023

作者： Cui, Zhuofan Tao, Junyi He, Bin Zhang, Yu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (纸本)9789819961863

High-precision maps and localization techniques are important modules in robot systems. However, the existing simultaneous localization and mapping methods using the same sensors in both localization and mapping tasks suffers from high costs and inflexibility. Therefore, the method of implementing a low-cost visual sensor to achieve indoor localization with the pre-obtained high-precision point cloud maps is proposed in this paper. To find a similar area represented by point cloud of the maps and the sensors, a localization strategy based on ICP for point cloud matching is completed on the premise of giving a rough initial pose. Meanwhile, visual odometry is integrated into the localization framework to conquer the localization failure caused by repeated point cloud features in indoor environments. The sub-meter level localization accuracy in the indoor building environment is achieved in the experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Mapping

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Adaptive and Robust Terrain Classification control Algorithm for a Spherical Robot

Adaptive and Robust Terrain Classification Control Algorithm...

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Annual Conference of industrial Electronics Society

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

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.

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Robust Real-Time Motion Retargeting via Neural Latent Prediction

Robust Real-Time Motion Retargeting via Neural Latent Predic...

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

作者： Tiantian Wang Haodong Zhang Lu Chen Dongqi Wang Yue Wang Rong Xiong State Key Laboratory of Industrial Control and Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Human-robot motion retargeting is a crucial approach for fast learning motion skills. Achieving real-time retargeting demands high levels of synchronization and accuracy. Even though existing retargeting methods have swift calculation, they still cause time-delay effect on the synchronous retargeting. To mitigate this issue, this paper proposes a motion retargeting method guided by prediction, which effectively reduces the adverse impact of time-delay. The proposed pipeline contains motion retargeting in spatial-temporal graph-based structure and motion prediction in the latent space. The motion sequence retargeting builds mapping and paired data from human poses to corresponding robot configurations for training prediction model, and generated robot motion satisfies limit and self-collision constrains. The controller guided by prediction imports future robot joint motion to achieve advanced trajectory tracking, thereby compensating for delay time spent on calculation and tracking. Experimental results show that our method outperforms other methods in terms of synchronization and similarity. Furthermore, our method exhibits fault-tolerant capability in scenarios involving the loss of human information input.

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An MPC-based Optimal Motion control Framework for Pendulum-driven Spherical Robots

arXiv

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arXiv 2023年

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

Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot’s attitude and motors’ current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion control

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Whole-body Dynamic Collision Avoidance with Time-varying control Barrier Functions

arXiv

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arXiv 2023年

作者： Huang, Jihao Chi, Xuemin Liu, Zhitao Su, Hongye The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Recently, there has been increasing attention in robot research towards the whole-body collision avoidance. In this paper, we propose a safety-critical controller that utilizes time-varying control barrier functions (time varying CBFs) constructed by Robo-centric Euclidean Signed Distance Field (RC-ESDF) to achieve dynamic collision avoidance. The RC-ESDF is constructed in the robot body frame and solely relies on the robot’s shape, eliminating the need for real-time updates to save computational resources. Additionally, we design two control Lyapunov functions (CLFs) to ensure that the robot can reach its destination. To enable real-time application, our safety-critical controller which incorporates CLFs and CBFs as constraints is formulated as a quadratic program (QP) optimization problem. We conducted numerical simulations on two different dynamics of an L-shaped robot to verify the effectiveness of our proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： controllers

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