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arXiv 2023年

作者： Li, Zhi Xin, Jinghao Li, Ning Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptability to unknown map sizes, and slow simulation speed. To speed up convergence, we combine curriculum learning (CL) with DRL, and first propose a Cumulative Curriculum Reinforcement Learning (CCRL) training framework to alleviate the issue of catastrophic forgetting faced by general CL. Besides, we present a novel state representation, which considers a local egocentric map and a global exploration map resized to the fixed dimension, so as to flexibly adapt to environments with various sizes and shapes. Additionally, for facilitating the fast training of DRL models, we develop a lightweight grid-based simulator, which can substantially accelerate simulation compared to popular robot simulation platforms such as Gazebo. Based on the customized simulator, comprehensive experiments have been conducted, and the results show that the CCRL framework not only mitigates the catastrophic forgetting problem, but also improves the sample efficiency and generalization of DRL models, compared to general CL as well as without a curriculum. Our code is available at https://***/BeamanLi/CCRL_Exploration. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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control Strategy of Multi-axis Serial Manipulator Based on Improved Active Disturbance Rejection control

Control Strategy of Multi-axis Serial Manipulator Based on I...

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第34届中国控制与决策会议

作者： Pengcheng Hu Chaochen Gu Kaijie Wu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information ProcessingMinistry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Aiming at the sudden change of load torque and uncertain external disturbance that multi-axis serial manipulators may face during operation,an improved Active Disturbance Rejection controller(ADRC) assisted by a Nonlinear Disturbance Observer(NLDOB) is designed and implemented in this *** realizes the estimation and compensation of known model parameter perturbation,and NLDOB is introduced to estimate and compensate the load torque and other unmodeled dynamic factors of the ***,the lumped control object model of three-bar mechanism and permanent magnet DC motor is established,and the control response curves of improved ADRC,traditional ADRC,and PID under the same conditions are compared and analyzed through *** experimental results show that compared with the traditional ADRC and PID,the improved ADRC has faster response speed,smaller overshoot and stronger robustness.

关键词： ADRC Multi-axis serial manipulator Permanent magnet DC motor NLDOB Load estimation

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FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection

arXiv

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Wu, Jinxing Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China The Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China

Promising complementarity exists between the texture features of color images and the geometric information of LiDAR point clouds. However, there still present many challenges for efficient and robust feature fusion in the field of 3D object detection. In this paper, first, unstructured 3D point clouds are filled in the 2D plane and 3D point cloud features are extracted faster using projection-aware convolution layers. Further, the corresponding indexes between different sensor signals are established in advance in the data preprocessing, which enables faster cross-modal feature fusion. To address LiDAR points and image pixels misalignment problems, two new plug- and-play fusion modules, LiCamFuse and BiLiCamFuse, are proposed. In LiCamFuse, soft query weights with perceiving the Euclidean distance of bimodal features are proposed. In BiLiCamFuse, the fusion module with dual attention is proposed to deeply correlate the geometric and textural features of the scene. The quantitative results on the KITTI dataset demonstrate that the proposed method achieves better feature-level fusion. In addition, the proposed network shows a shorter running time compared to existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Initialization-Free Distributed Constrained Optimization Algorithms with a Pre-Specified Time

Initialization-Free Distributed Constrained Optimization Alg...

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IEEE Conference on Decision and control

作者： Xiasheng Shi Chaoxu Mu Changyin Sun Lu Ren Yanxu Su School of Artificial Intelligence Anhui University Hefei China Ministry of Education Engineering Research Center of Autonomous Unmanned System Technology Anhui University Hefei China Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Anhui University Heifei China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology School of Electrical and Information Engineering Tianjin University Tianjin China

The distributed constrained optimization problem over an undirected communication topology is investigated in this study. It focuses on addressing a global coupled equality constraint that applies to all agents. To tackle this problem, a distributed approach with arbitrary initialization is developed by virtue of the aperiodic sampling control idea and the consensus-based multi-agent system(MAS) technology. This approach is developed to address constrained optimization problems within a pre-specified time. In addition, this predefined time is freely defined by users and irrelevant to the initial states, control coefficients, and network structure of systems. The Lyapunov stability theory completes the convergence proof of the developed method. Then, the developed method is extended to handle distributed nonlinear constrained optimization problems. Finally, The availability of two developed methods is demonstrated through two simulation examples.

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A Secure Parameter Self-Tuning control Scheme for Vehicle Platoons Under DoS Attacks

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

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

作者： Qiaoni Han Guowen Cheng Zhenning Tang Chang Liu Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security.

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Self-supervised Multi-frame Monocular Depth Estimation with Pseudo-LiDAR Pose Enhancement

Self-supervised Multi-frame Monocular Depth Estimation with ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wenhua Wu Guangming Wang Jiquan Zhong Hesheng Wang Zhe Liu MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Insititute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Depth estimation is one of the most important tasks in scene understanding. In the existing joint self-supervised learning approaches of depth-pose estimation, depth estimation and pose estimation networks are independent of each other. They only use the adjacent image frames for pose estimation and lack the use of the estimated geometric information. To enhance the depth-pose association, we propose a monocular multi-frame unsupervised depth estimation framework, named PLPE-Depth. There are a depth estimation network and two pose estimation networks with image input and pseudo-LiDAR input. The main idea of our approach is to use the pseudo-LiDAR reconstructed from the depth map to estimate the pose of adjacent frames. We propose depth re-estimation with a better pose between the image pose and the pseudo-LiDAR pose to improve the accuracy of estimation. Besides, we improve the reconstruction loss and design a pseudo-LiDAR pose enhancement loss to facilitate the joint learning. Our approach enhances the use of the estimated depth information and strengthens the coupling between depth estimation and pose estimation. Experiments on the KITTI dataset show that our depth estimation achieves state-of-the-art performance at low resolution. Our source codes will be released on https://***/IRMVLabIPLPE-Depth.

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Design of echo state network with multi-objective optimization algorithm and l1 regularization

Design of echo state network with multi-objective optimizati...

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Chinese control and Decision Conference, CCDC

作者： Zhanhong Wu Cuili Yang Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing University of Technology Beijing

ISBN: (纸本)9781665478977

In this paper, a new approach for optimizing the structure and prediction error of echo state network (ESN) is proposed. ESN is a kind of recurrent neural network with simple training and strong generalization ability. Reservoir is an important structure of ESN, which determine network performance. Thus, multi-objective optimization algorithm is used to optimize network structure and training error simultaneously. Moreover, a local search algorithm based on l 1 regularization is used to accelerate convergence. The experiment results of time series prediction and standard classification show that MESN can improve the network prediction performance while sparse network structure.

关键词： Training Recurrent neural networks Time series analysis Benchmark testing Prediction algorithms Reservoirs Classification algorithms

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A Co-Simulation system in CarMaker and MATLAB for HEVs under V2X Connected Environment 5

A Co-Simulation System in CarMaker and MATLAB for HEVs under...

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5th CAA International Conference on Vehicular control and Intelligence, CVCI 2021

作者： Pang, Zitong You, Xiongxiong Jiao, Xiaohong Zhang, Yahui Yanshan University Eng. Res. Ctr. of the Ministry of Education for Intelligent Control System and Intelligent Equipment School of Electrical Engineering Qinhuangdao China Yanshan University Hebei Innovation Center for Equipment Lightweight Design and Manufacturing School of Mechanical Engineering Qinhuangdao China

ISBN: (纸本)9781665408462

A co-simulation system in CarMaker and MAT-LAB is built for the study of control problems such as energy management optimization of hybrid electric vehicles (HEVs) under a V2X connected environment in this paper. The Carmaker can provide a complete V2X connected environment and certain vehicle models, but can't establish an individual and complete powertrain model for specific control research. Meanwhile, it is also necessary to build some control strategies of HEVs in MATLAB/Simulink environment. As a case study, testing results of energy management control of a connected HEV are demonstrated on this platform to verify the applicability of the established co-simulation platform function. © 2021 IEEE.

关键词： Simulation platform

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Automatic Image Generation of Peking Opera Face using StyleGAN2

Automatic Image Generation of Peking Opera Face using StyleG...

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2022 International Conference on Culture-Oriented Science and Technology, CoST 2022

作者： Xin, Xiaoyu Shen, Yinghua Xiong, Rui Lin, Xiahan Yan, Ming Jiang, Wei Communication University of China School of Information and Communication Engineering Beijing China Center for Ethnic and Folk Literature and Art Development Ministry of Culture and Tourism of the P.R.C Beijing China Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing China

ISBN: (数字)9781665462488

ISBN: (纸本)9781665462488

Image generation technology, which is often used in various applications of intelligent image generation, can learn the feature distribution of real images and sample from the distribution to obtain the generated images with high fidelity. This paper focuses on the feature extraction and intelligent generation techniques of Peking opera face with Chinese cultural characteristics. Based on the creation of a Peking opera face dataset, this paper compares the impact of different variants of a Style-based generator architecture for Generative Adversarial Networks (StyleGAN2) and different sizes of datasets on the quality of face generation. The experimental results verify that the synthetic images generated by StyleGAN2 with the addition of the Adaptive Discriminator Augmentation (ADA) module are visually better and have good local randomness when the dataset is small and unbalanced in distribution. © 2022 IEEE.

关键词： Generative adversarial networks

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Automated Assembly by Two-Fingered Microhand for Fabrication of Soft Magnetic Microrobots

Automated Assembly by Two-Fingered Microhand for Fabrication...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yue Zhao Xiaoming Liu Ruixi Wang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Intelligent Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Materials Engineering Science Osaka University Osaka Japan Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

关键词： Three-dimensional displays Shape Micromanipulators Magnetization Manuals Three-dimensional printing Soft magnetic materials

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