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SSRN 2023年

作者： Liu, Wenbo Kuang, Zhian Zhou, Bo Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shanghai Friendess Electronic Technology Shanghai201100 China

In this article, the complex task assignment, scheduling and planning of complex heterogeneous multi-robot welding process are studied. For large workpiece welding tasks that involve complex heterogeneous multi-robot task allocation under different internal and external constraints, the traditional multi-robot task allocation (MRTA) framework is extended to a novel and unified heterogeneous multi-robot (H-MRTA) framework, and a hierarchical optimization algorithm is proposed. Firstly, an optimization model based on the H-MRTA framework is established. Taking into account constraints such as accessibility, collision and welding process, the optimization objectives are formulated by combining the total welding time and the task balance between robots. Then, to obtain the optimal solution, a stepwise optimization algorithm is devised to solve the MRTA problem from top to bottom in two layers: working area division problem and multi-robot path planning problem. For the working area division problem, a task-centered optimization model is proposed, which aims to balance the workload among robots, and is solved by branch-and-bound method. For the multi-robot path planning problem, multiple process constraints are considered and the total working time is minimized as the objective function. The effectiveness of the optimization algorithm is verified by a case study on the welding task of a large plate element in a ship hull. compared to two other state-of-the-art algorithms, the proposed optimization algorithm achieves a task allocation scheme with a shorter welding time, a shorter waiting time and a higher welding productivity, which demonstrates the effectiveness and feasibility of the stepwise optimization-based task allocation algorithm for complex heterogeneous multi-robot. © 2023, The Authors. All rights reserved.

关键词： Industrial robots

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Enabling few-shot learning with PID control: a layer adaptive optimizer 24

Enabling few-shot learning with PID control: a layer adaptiv...

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Proceedings of the 41st International Conference on Machine Learning

作者： Le Yu Xinde Li Pengfei Zhang Zhentong Zhang Fir Dunkin School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China and Southeast University Shenzhen Research Institute Shenzhen China School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China

Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these successes, MAMLbased approaches encounter significant challenges when there is a substantial discrepancy in the distribution of training and testing tasks, resulting in inefficient learning and limited generalization across domains. Inspired by classical proportional-integral-derivative (PID) control theory, this study introduces a Layer-Adaptive PID (LA-PID) Optimizer, a MAML-based optimizer that employs efficient parameter optimization methods to dynamically adjust task-specific PID control gains at each layer of the network, conducting a first-principles analysis of optimal convergence conditions. A series of experiments conducted on four standard benchmark datasets demonstrate the efficacy of the LA-PID optimizer, indicating that LA-PID achieves state-of-the-art performance in few-shot classification and cross-domain tasks, accomplishing these objectives with fewer training steps. Code is available on https://***/yuguopin/LA-PID.

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Asymptotic Stabilization of Time-Varying Delay System with Actuator Saturation via Multi-dimensional Taylor Network

Asymptotic Stabilization of Time-Varying Delay System with A...

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International Conference on Robotics and Automation engineering (ICRAE)

作者： Guo-Biao Wang Hong-Sen Yan School of Automation Southeast University Nanjing Jiangsu China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing Jiangsu China

This paper addresses the stabilization problem of time-varying delay systems subject to actuator saturation. A novel class of Lyapunov functional with a nonlinear metric is developed to guarantee that there exists a Lyapunov-Razumikhin function that strictly decreases for the controlled system. Such a nonlinear metric is parameterized by a multi-dimensional Taylor network with concise topology, which brings into the high-order dynamic information and guarantees real-time performance. A sum-of-squares programming approach based on the designed Lyapunov functional is formulated to deduce the stability criteria, which maximizes the estimate of the region of attraction and ensures the uniform asymptotic stability of the closed-loop system theoretically. A numerical example demonstrates the effectiveness of the proposed stabilization control scheme.

关键词： measurement Actuators Asymptotic stability Delay systems Stability criteria Programming Real-time systems

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A Robot Coordinate measurement System Based on Pull Wire Sensor and Its Parameter Identification Method 12

A Robot Coordinate Measurement System Based on Pull Wire Sen...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Gao, Shuang Ma, Kaiwei Gao, Yang Shen, Xin Yang, Mingxing Xu, Fengyu College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Jiangsu Nanjing China Anhui Province Key Laboratory of Special Heavy Load Robot Anhui Maanshan China College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Aiming at the problems of large robot error and difficult coordinate detection, a robot coordinate measurement system and parameter identification method based on pull wire sensor are proposed. Firstly, a robot coordinate measurement system based on pull wire sensor is established. Secondly, Levenberg-Marquarelt algorithm is used to solve the kinematics parameters of the robot. Finally, the neural network of the whole connection layer is used to predict the optimal compensation amount for secondary compensation. In order to verify the above theory, the robot coordinate measurement system is built and verified on the five degree of freedom robot experimental platform. The results show that the robot coordinate measurement system can measure the robot coordinates quickly and accurately. The secondary compensation method controls the accuracy of the robot to about 2mm, and the optimal error is controlled within 0.012mm. The absolute positioning accuracy of the robot is effectively improved. © 2022 IEEE.

关键词： Robots

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Double STAR-RIS Enhanced Secure Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Cai, Yujin Yu, Wenwu Nie, Xiaokai Cheng, Qiang Cui, Tiejun Southeast University School of Mathematics Jiangsu Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently been employed to enhance the secure transmission due to the ability to manipulate the full-space electromagnetic signals. Prior works mainly investigate the single STAR-RIS aided secure communication system, which does not reveal the potential of the cooperative inter-STAR-RIS link. In this paper, a double STAR-RIS enhanced secure communication network involving multiple legitimate users (LUs) and eavesdroppers (Eves) is proposed. For fairness, the minimum secrecy rate maximization problem is formulated by jointly optimizing the active beamforming and passive transmission and reflection (T&R) beamforming. To deal with the highly non-convex nonlinear optimization problem, an active and passive beamforming optimization algorithm is proposed by employing the alternating optimization method. Wherein, a low-complexity sequential constraint relaxation (SCR) based algorithm is designed to solve the passive T&R beamforming optimization problem. Simulation results demonstrate that the secure performance in the double STAR-RIS assisted system outperforms that in the single RIS/double RIS/single STAR-RIS aided network. Furthermore, the proposed algorithm significantly reduces computational time (i.e., resource allocation time) compared with the traditional SCR based beamforming optimization algorithm. © 2002-2012 IEEE.

关键词： Cooperative communication

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Probabilistic Decomposition Transformer for Time Series Forecasting

SSRN

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SSRN 2022年

作者： Tong, Junlong Xie, Liping Yang, Wankou Zhang, Kanjian School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 China

Time series forecasting is crucial for many fields, such as disaster warning, weather prediction, and energy consumption. The Transformer-based models are considered to have revolutionized the field of sequence modeling. However, the complex temporal patterns of the time series hinder the model from mining reliable temporal dependencies. Furthermore, the autoregressive form of the Transformer introduces cumulative errors in the inference step. In this paper, we propose the probabilistic decomposition Transformer model that combines the Transformer with a conditional generative model, which provides hierarchical and interpretable probabilistic forecasts for intricate time series. The Transformer is employed to learn temporal patterns and implement primary probabilistic forecasts, while the conditional generative model is used to achieve non-autoregressive hierarchical probabilistic forecasts by introducing latent space feature representations. In addition, the conditional generative model reconstructs typical features of the series, such as seasonality and trend terms, from probability distributions in the latent space to enable complex pattern separation and provide interpretable forecasts. Extensive experiments on several datasets demonstrate the effectiveness and robustness of the proposed model, indicating that it compares favorably with the state of the art. © 2022, The Authors. All rights reserved.

关键词： Time series

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Transformer Based Multi-modal Fusion For Place Recognition with Self-attention Mechanism

Transformer Based Multi-modal Fusion For Place Recognition w...

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Chinese Automation Congress (CAC)

作者： Yan Pan Xiang Yu Zeqing Chang Fang Fang Bo Zhou Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing P. R. China

Autonomous driving still leaves a challenging task that how to apply the complementary information captured from different sensors, i.e. cameras and LiDAR, to handle place recognition task. In this paper, a brand new pipeline was designed to tackle the retrieval problem with a multi-modal late fusion. The light-weighted convolutional blocks is applied to encode features from images, followed by converting them into pseudo point clouds. Inspired by the progress of the Transformer, the network employs a residual point transformer module to extract feature vectors for 3D points and pseudo point clouds respectively. Finally, the two corresponding local descriptors are fused to get a robust fused global descriptor, which is capable of place recognition task after end-to-end training. Experimental results collected from sequence 00, 02, 05, 06 of KITTI dataset validate that fusing information from two modalities is able to enhance performance.

关键词： Point cloud compression Training Laser radar Three-dimensional displays Streaming media Transformers Feature extraction

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Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection

arXiv

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arXiv 2022年

作者： Yu, Yi Da, Feipeng School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://***/open-mmlab/mmrotate. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Parallax-Tolerant Image Stitching with Epipolar Displacement Field

arXiv

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arXiv 2023年

作者： Yu, Jian Da, Feipeng School of Automation Southeast University No.2 Sipailou Jiangsu Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education No.2 Sipailou Jiangsu Nanjing210096 China

Image stitching with parallax is still a challenging task. Existing methods often struggle to maintain both the local and global structures of the image while reducing alignment artifacts and warping distortions. In this paper, we propose a novel approach that utilizes epipolar geometry to establish a warping technique based on the epipolar displacement field. Initially, the warping rule for pixels in the epipolar geometry is established through the infinite homography. Subsequently, the epipolar displacement field, which represents the sliding distance of the warped pixel along the epipolar line, is formulated by thin-plate splines based on the principle of local elastic deformation. The stitching result can be generated by inversely warping the pixels according to the epipolar displacement field. This method incorporates the epipolar constraints in the warping rule, which ensures high-quality alignment and maintains the projectivity of the panorama. Qualitative and quantitative comparative experiments demonstrate the competitiveness of the proposed method for stitching images with large parallax. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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Modified Deep Deterministic Policy Gradient Based Optimal Speed Tracking Strategy for PMSMs

Modified Deep Deterministic Policy Gradient Based Optimal Sp...

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Data Driven control and Learning systems (DDCLS)

作者： Zhong-Xin Fan Lintao Tang Shihua Li Rongjie Liu School of Automation Southeast University Nanjing P. R. China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing P. R. China Department of Statistics Florida State University Tallahassee FL USA

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Achieving optimal speed regulation for permanent magnet synchronous motors (PMSMs) remains a challenging task, particularly in selecting the most suitable controller to meet desired objectives. This paper considers the optimal speed tracking problem of PMSMs. We employ a reinforcement learning algorithm to obtain optimal controller with a linear form. The reinforcement learning approach incorporates a critic-actor network structure, which approximates the value function and optimal control, respectively. Specifically, a weight updating method for the critic network is introduced, where the update of the actor network is guided by the critic network. Through data collection and neural network training, dynamic acquisition of the optimal controller gain is achieved. In addition, a novel activation function is implemented in the network, leading to improved control performance concerning dataset size and reduced training time. At final, a real-world PMSM application is presented along with comparisons to demonstrate effectiveness. The desire idea of resulting optimal controllers is not only applicable to linear systems but also capable of approximating arbitrary nonlinear optimal controllers, thus offering practical engineering versatility.

关键词： Training Linear systems Learning systems Neural networks Optimal control Reinforcement learning Data collection

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