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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jiawei Shao Yahui Gan Xianzhong Dai Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University NanJing China

To realize dual-robot autonomous path planning and realization, the trajectory planning research of dual-robot is carried out using the open-source robot operating system ROS as the simulation platform. The motion planning-related file configuration is completed by using MoveIt! In trajectory planning, cubic spline interpolation is performed on the trajectory points generated by the RRT-Connect path planning algorithm to complete the planning of the dual-arm assembly task. The communication between ROS nodes and ROS is established in the controller, and the processed trajectory points are communicated with the controller through ROS-Industrial. The experimental results show that the trajectory of the dual robot movement process is smooth and continuous, the stability is strong, and the error is small, which can ensure the completion of basic assembly tasks.

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An Improved YOLOv5 Detection Algorithm with Pruning and OpenVINO Quantization

An Improved YOLOv5 Detection Algorithm with Pruning and Open...

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2023 China Automation Congress, CAC 2023

作者： Chen, Cheng Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

ISBN: (纸本)9798350303759

In order to address the problem of current object detection models being too large to be deployed on robot controllers, this paper proposes improvements to YOLOv5 for real-time detection. The YOLOv5s model is pruned at the Batch Normalization (BN) layer, and further optimized using the OpenVINO tool for quantization. The results demonstrate that our improvements effectively improve the model's inference speed while maintaining its accuracy. Compared to the original YOLOv5 model, the pruned model achieves the same accuracy without degradation, with a 36.6% reduction in parameter count and a 35% reduction in weight storage file size. Furthermore, after optimization with OpenVINO, the final model achieves a FPS of 56. © 2023 IEEE.

关键词： Object detection

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Adaptive Augmented Kalman Filter-Based control for Electronic Throttle systems with Disturbances and Noises

Adaptive Augmented Kalman Filter-Based Control for Electroni...

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第42届中国控制会议

作者： Wen Li Jinya Su Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

In this study, we investigate the control problem of electronic throttle systems in the presence of practical challenges such as disturbances and measurement noises. To address these challenges, we propose an adaptive augmented Kalman filter(AAKF)based control approach that combines the strengths of extended state observer in disturbance estimation and adaptive Kalman filter in adaptive noise filtering. The outputs of AAKF are integrated into the Backstepping control design, resulting in a composite control that concurrently achieves fast disturbance rejection and noise suppression. We conduct a comparative simulation study against conventional methods without adaptive filtering to validate the effectiveness of the proposed AAKF-based control strategy, which exhibits superior position control accuracy and disturbance attenuation performance. We envision that our proposed control strategy will contribute to improving vehicle power, fuel economy, and emission performance.

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Research on Evolutionary Guiding Strategy for Self-organized Aggregates of Massive Flexible Resources 4

Research on Evolutionary Guiding Strategy for Self-organized...

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4th International Conference on Advanced Electrical and Energy systems, AEES 2023

作者： Mao, Tingfeng Zhang, Kaifeng Yin, Changwei Pan, Xiaohui Ma, Nan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co. Ltd. Nanjing210019 China

ISBN: (纸本)9798350342659

With the improvement of communication technology and the management of demand-side, more and more researches focus on the aggregation technology of flexible resources on the demand side. This paper proposes to build a self-organizing response framework in the scenario of massive resources, and resources can independently choose to join or exit the aggregates to participate in the regulation of grid active power balance. However, self-organizing aggregates cannot generate aggregation scenarios that accurately match the dynamic needs of the power grid during autonomous operation, so the grid needs to further guide the evolution of the aggregates. Firstly, the process and principle of realizing self-organization and evolution of resources in cyberspace are outlined. Then, the evolutionary driving sources of the self-organizing aggregates are analyzed to construct the evolutionary dynamics model framework from both the steady and the dynamic dimensions, and the data-driven modeling is carried out through the back propagation neural network to obtain the evolution law of resource self-organization. Finally, an iterative evolutionary guiding strategy based on price feedback is designed. The optimal guiding value of the market price cap is obtained through iteration under the expected aggregation scale target of the power grid, and also the dynamic evolution time of the aggregates is predicted to determine the timing of the release of the guiding strategy. The proposed strategy is constructed and tested on the NETLOGO resource self-organization simulation platform. The results show that the self-organizing aggregates can evolve to the expected state of power grid demand with high accuracy. © 2023 IEEE.

关键词： Aggregates

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Semantic Map Construction via Multi-sensor Fusion 36

Semantic Map Construction via Multi-sensor Fusion

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Qiu, Houhan Lin, Zhicheng Li, Jun Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9781665437127

Semantic SLAM can achieve the acquisition of environmental semantic information and increase the understanding of the environmental content. Therefore, it has received extensive attention from academia and industry. In the most existing work, the visual SLAM and deep learning algorithms are combined, and the point cloud fusion method is used to build maps. It is difficult to be directly applied to robot navigation due to problems of time misalignment of sensor data, mismatched detection ranges, and inconsistent accuracy. In this paper, a method of building a semantic map is proposed by combining lidar, camera, odometer. First, the semantic information and position information of the target object are obtained by the YOLO v3 algorithm through the camera, and the grid map is constructed by using the Gmapping algorithm. Second, the semantic information and position information are merged into the grid semantic map to form a semantic grid map. Finally, the effectiveness and feasibility of this method are verified by these experiments. © 2021 IEEE.

关键词： Cameras

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Data-Based Optimal Tracking of Autonomous Nonlinear Switching systems

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 227-238页

作者： Xiaofeng Li Lu Dong Changyin Sun the School of Automation Southeast UniversityNanjing 210096 the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China the College of Electronics and Information Engineering Tongji UniversityShanghai 201804China

In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching *** system state is forced to track the reference signal by minimizing the performance ***,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled *** linear-in-parameter(LIP)neural network is taken as the value function *** the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable ***,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is ***,the effectiveness of the algorithm is demonstrated with three simulation examples.

关键词： Adaptive dynamic programming approximation error data-based control Q-learning switching system

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A Pursuit Strategy for Multi-Agent Pursuit-Evasion Game via Multi-Agent Deep Deterministic Policy Gradient Algorithm

A Pursuit Strategy for Multi-Agent Pursuit-Evasion Game via ...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Ye, Jianfeng Wang, Qing Ma, Bei Wu, Yongbao Xue, Lei School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China SEU-Monash Joint Graduate School Southeast University Nanjing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

This paper studies a classical pursuit-evasion problem. The pursuer attempts to capture the faster evader in a bounded area. The velocity of evader is 1.2 times as fast as the pursuers'. All of them have adaptive strategies. We use game theory to model the multi-agent pursuit-evasion game and prove that the game model has Nash equilibrium. Then, we modify the multi-agent deep deterministic policy gradient (MADDPG) algorithm for seeking the Nash equilibrium. The simulation examples are given to illustrate the effectiveness of the designed method. © 2022 IEEE.

关键词： Game theory

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Secure output synchronization of heterogeneous multi-agent systems against false data injection attacks

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Science China(Information Sciences) 2022年第6期65卷 142-154页

作者： Shicheng HUO Dalin HUANG Ya ZHANG School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Cyber Science and Engineering Southeast University

This paper studies secure output synchronization for heterogeneous multi-agent systems against false data injection(FDI) attacks. Both the sensors and actuators of agents may be injected into FDI *** mitigate the malicious impact of these attacks on synchronization performance, a desired reference model,which is combined with an auxiliary system, is first designed for each agent to simulate its normal system dynamics. Then, based on this reference model, a state feedback cooperative controller and a static output feedback cooperative controller, which consist of adaptive compensators, are proposed, respectively. The integrated control protocols can ensure that the output synchronization error is small by adjusting some designed parameters even in the presence of FDI attacks. An illustrative example is employed to demonstrate the effectiveness of the proposed method.

关键词： output synchronization false data injection attacks adaptive compensators heterogeneous multi-agent systems secure control

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Effective Indoor Fire Detection with Channel Shuffle Module 21

Effective Indoor Fire Detection with Channel Shuffle Module

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5th International Conference on Computer Science and Application Engineering, CSAE 2021

作者： Ge, Haotian Cao, Yichao Lu, Xiaobo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781450389853

In recent years, methods based on computer vision and deep learning become the mainstream approaches in fire detection. However, the expensive computation cost of 3D convolutional neutral network (CNN) is unbearable and it is difficult for them to capture the fire regions of videos in time. In this paper, we design a module named channel shuffle module (CSM) based on 2D CNN to keep the balance between computation cost and accuracy. By fusing RGB frame and differential frame, CSM improves the ability of 2D CNN in temporal information extraction which much less cost than methods based on 3D CNN. Four different structures of CSM are proposed and we choose the best one by experiment results. Also, experiments prove that the performances of TSN and TSM are improved with CSM in sequence classification. The accuracy of TSM with CSM is 99.2045%, false positive rate reaches 0.7890% and false negative rate reaches 0.4530%, which demonstrates the efficiency of CSM in temporal feature modeling.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Fires

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Task Sequence Planning Algorithm for Cooperative Assembly of Dual-arm Robot

Task Sequence Planning Algorithm for Cooperative Assembly of...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Jin Gan, Yahui Wang, Zhengwei Dai, Xianzhong School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665426473

Dual-arm collaborative robots have gradually become a research hotspot in the field of robotics due to their unique collaboration and flexibility, and they have a wide range of application prospects especially in the assembly manufacturing industry. However, the existing dual-arm robot lacks autonomy in assembly operations, especially when faced with a large number of randomly placed assembly parts, how to make the robot have independent assembly task decision-making ability is a challenge. In response to this problem, this paper proposes a new dual-arm robot collaborative assembly task sequence planning algorithm, which fully considers the constraints of the dual-arm robot's mechanical structure, kinematics, characteristics of objects to be assembled, interference between objects, etc.. Firstly, The algorithm uses the Monte Carlo method to analyze the working space of the left and right arms of the dual-arm robot, while taking into account the structural constraints of the robot. Then, according to the characteristics of the object to be assembled, the idea of interference sphere is proposed to analyze the interference between objects in the assembly task. The influence of the operation, and finally combined with the movement cost of the dual-arm assembly, the assembly task sequence is effectively planned. The algorithm was verified by simulation using the ABB YuMi dual-arm collaborative robot, and the experimental results proved the feasibility and effectiveness of the algorithm. © 2021 IEEE

关键词： Monte Carlo methods

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