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

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

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Annual Conference of industrial Electronics Society

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

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.

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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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IEEE International Conference on Robotics and Biomimetics

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

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.

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Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy

arXiv

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arXiv 2022年

作者： Wu, Jun Liu, Lilu Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology Institue of Cyber-Systems and Control Zhejiang University Zhejiang China

Current RGB-based 6D object pose estimation methods have achieved noticeable performance on datasets and real world applications. However, predicting 6D pose from single 2D image features is susceptible to disturbance from changing of environment and textureless or resemblant object surfaces. Hence, RGB-based methods generally achieve less competitive results than RGBD-based methods, which deploy both image features and 3D structure features. To narrow down this performance gap, this paper proposes a framework for 6D object pose estimation that learns implicit 3D information from 2 RGB images. Combining the learned 3D information and 2D image features, we establish more stable correspondence between the scene and the object models. To seek for the methods best utilizing 3D information from RGB inputs, we conduct an investigation on three different approaches, including Early-Fusion, Mid-Fusion, and Late-Fusion. We ascertain the Mid-Fusion approach is the best approach to restore the most precise 3D keypoints useful for object pose estimation. The experiments show that our method outperforms state-of-the-art RGB-based methods, and achieves comparable results with RGBD-based methods. © 2022, CC BY.

关键词： Textures

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