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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Hongqi, Yang Xie, Guangming 96037 PLA Troops Baoji721000 China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789819639762

With the development of computational fluid dynamics and deep reinforcement learning, it has become possible to combine deep reinforcement learning with computational fluid dynamics to study the control problem of underwater robots. This article uses Computational Fluid Dynamics (CFD) and Deep Reinforcement Learning to study planar pose control of robotic fish. A two-dimensional model of the robotic fish is built in CFD, and the surrogate model environment is built based on CFD simulation results. Actor-Critic method is used to train the pose control in surrogate model environment. Sparse reward problem is solved by using course learning method during training. Finally, pose control experiment of robotic fish is carried out. The experimental results show that this pose control method is effective and feasible, control accuracy is higher. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Deep reinforcement learning

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Cooperative Output Regulation of Heterogeneous systems over Directed Graphs:A Dynamic Adaptive Event-Triggered Strategy

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Journal of systems Science & complexity 2023年第3期36卷 909-921页

作者： SU Hang CHENG Bin LI Zhongkui State Key Laboratory for Turbulence and Complex Systems and Department of Mechanics and Engineering Science College of EngineeringPeking UniversityBeijing 100871China Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 201804China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210China

This paper investigates the cooperative output regulation problem of heterogeneous linear multi-agent systems over directed graphs with the constraint of communication *** that there exists an exosystem whose state information is not available to all agents,the authors develop distributed adaptive event-triggered observers for the followers based on relative information between neighboring *** should be pointed out that,two kinds of time-varying gains are introduced to avoid relying on any global information associated with the network,and dynamic triggering conditions are designed to get rid of continuous *** the basis of the designed observers,the authors devise a local controller for each *** with the existing related works,the main contribution of the current paper is that the cooperative output regulation problem for general directed graphs is solved requiring neither global information nor continuous communications.

关键词： Adaptive control consensus cooperative output regulation directed graph event-triggered control multi-agent systems

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Collaborative Pushing and Grasping in complex Scenarios via a Generator-Evaluator Network with Multiple Action Primitives 17th

Collaborative Pushing and Grasping in Complex Scenarios vi...

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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zuo, Guoyu Wang, Zihao Luo, Yongkang Yu, Shuangyue Zhao, Min School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819607761

As robotic technologies advance, robotic arm manipulation tasks in complex environments become increasingly important. This paper presents a new collaborative pushing and grasping strategy to address two major challenges robotic arms encounter in complex environments: invisibility and tight enclosure of target objects. We propose a generator-evaluator network architecture based on multiple action primitives, incorporating hierarchical reinforcement learning and Double DQN techniques to optimize the operational efficiency of the robotic arm. By introducing a new action primitive to prioritize pushing operations around the topmost objects in space, our approach effectively reveals and locates obscured target objects. Additionally, utilizing the Spatial Correlation Test (SCT), our method explores all possible collision-free operational positions in the action space, selecting the optimal position from among them. Experimental results demonstrate that our approach achieves high efficiency and success rates in scenarios with randomly distributed objects, objects tightly enclosed by others, and invisible objects. This strategy enhances the speed and accuracy of tasks and improves the robot’s adaptability to complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Relevance Search over Knowledge Graph with User-entity Interaction Data 12

Relevance Search over Knowledge Graph with User-entity Inter...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Li, Jianyu Yue, Kun Yunnan University School of Information Science and Engineering China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9798350340181

Relevance search is to find a list of entities in a knowledge graph (KG), which are associative to a query entity. However, many entities are not linked in KG but are actually associated by user interactions. To this end, we propose a joint weighting function to evaluate the entity associations from both KG and user-entity interaction data simultaneously. Upon the subgraph extracted from KG w.r.t the query entity, we obtain the associative entities by calculating their association degrees. Experimental results show that the effectiveness of our method outperforms other competitors. © 2023 IEEE.

关键词： Association reactions

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Optimized Hybrid Control for Attitude Adjustment of Downhole Directional Drilling Tools 50

Optimized Hybrid Control for Attitude Adjustment of Downhole...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Xiao, Jing Wang, She Cai, Zhen School of Information Engineering Wuhan City Polytechnic University Wuhan430064 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (纸本)9781665464543

This study delves into the critical role of attitude control in downhole directional drilling tools, focusing on the complex coupling between inclination and azimuth in the motion model, particularly the time-varying nature of azimuth adjustments. By implementing separate control strategies for inclination and azimuth, the research integrates a comprehensive hybrid control system. It rigorously examines the stability of the closed-loop systems and validates the effectiveness and applicability of the hybrid control method through extensive simulations. The system demonstrates significant robustness and adaptability, enhancing drilling precision and efficiency. © 2024 IEEE.

关键词： State feedback

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Tea Picking Path Planning Based on Ant Colony Algorithm 41

Tea Picking Path Planning Based on Ant Colony Algorithm

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第41届中国控制会议

作者： Guangjun Wang Yi Zhao Zhi Wang Yangzheng Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In order to improve the efficiency of tea picking,a scheme combining ant colony algorithm and tea picking technology is proposed to plan the picking *** this scheme,k-means clustering algorithm is used to divide the tea picking regions with irregular *** ant colony algorithm is used to carry out path planning for tea in different picking areas,and the best picking effect is selected according to the simulation *** solve the problem of long searching time of ant colony algorithm,the condition of iteration termination is changed to adaptive value,and the optimal route after the last iteration is recorded as the initial value of the next *** simulation results show that the best effect is to divide two picking *** improved ant colony algorithm reduces the operation time and the planned picking path is shorter,which can enhance the real-time tea picking and improve the efficiency of tea picking.

关键词： Tea plucking Path planning Ant colony algorithm K-means clustering

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LRDE:Long-Range Dependency and Salient Feature Enhancement for Co-SOD 41

LRDE:Long-Range Dependency and Salient Feature Enhancement f...

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第41届中国控制会议

作者： LongSheng Wei SiYuan Guo GuanYu Zong Jiu Huang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Different from other salient object detection(SOD) for the task of extracting salient obj ects in a image,collaborative saliency object detection(Co-SOD) targets a set of related image groups,and uses algorithms to find and locate the common collaborative salient objects in related image *** current SOD has achieved remarkable results,but most of the previous researches focus on how to obtain the consistency between images,while the contextual semantic information of images and the features of salient objects are less *** solve these problems,we propose a novel network to capture long-range dependencies on images and enhance(LRDE) the representation of salient ***,In LRDE,we capture the longrange dependencies by the Non-local module to obtain contextual semantic ***,in order to highlight the features of salient objects and enhance the representation of salient objects,we propose the Coordinate Attention module,which embeds the location-attention channel attention and encodes the feature maps as orientation-aware and position-aware attention maps,and It is applied complementary to the input feature map,thereby enhancing the representation of salient *** show that the method outperforms twelve improved methods on four publicly available datasets.

关键词： Co-SOD Long-Range Dependency Feature Enhancement Non-local Module Coordinate Attention Module

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Position Attention Network for Object Tracking 41

Position Attention Network for Object Tracking

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第41届中国控制会议

作者： Mingzhong Du Longsheng Wei Shiyu Liu School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,Siamese-based trackers have achieved excellent performance,most of them usually calculate the similarity of each position on the search region to the object through the cross-correlation layer for tracking obj *** solve the problem that the above method neglects the correspondence of the local information between the object and the search region and cannot adapt to the object deformation well,we propose a Siamese network-based tracker with position attention network(SiamPA).First,we use Siamese backbone network to extract template and search region ***,we adopt the boxguided object feature selection strategy to avoid similarity calculations for background *** addition,we introduce the position attention network instead of the cross-correlation layer to learn the part-level relationship between the object and the search region ***,the classification-regression sub-network is used to decode the similarity respond map obtained by the position attention network and predict the position of the *** contribution,one is to propose a box-guided method for refining object features,and the other is to introduce a position attention network for information *** on three challenging benchmarks including GOT-10 k,UAV123 and OTB-100 demonstrate that our SiamPA achieves excellent tracking performance with a real-time speed.

关键词： Object tracking Siamese network Box-guided Position attention network

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Tea Bud Recognition Based on Machine Learning 41

Tea Bud Recognition Based on Machine Learning

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第41届中国控制会议

作者： Guangjun Wang Zhi Wang Yi Zhao Yangzheng Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Due to the low efficiency and high cost of manual tea picking,as well as the development and application of machine vision and image recognition technology,the mechanization and intelligence of tea picking will become a *** of all,this paper analyzed the advantages and disadvantages of several common target detection methods by using Matlab ***,considering cost,accuracy and real-time performance,the method of combining K-means clustering and image morphology processing is finally selected to extract tea ***,the method is reproduced on STM32 single chip ***,the effect was verified in the actual tea garden,which laid a foundation for the subsequent intelligent picking.

关键词： Machine learning K-means clustering STM32

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A Lightweight Object Detection Network for Industrial Robot Based YOLOv5

A Lightweight Object Detection Network for Industrial Robot ...

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2023 China Automation Congress, CAC 2023

作者： Zhong, Tao Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University 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

With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detection, most industrial vision robots still rely on traditional object detection methods due to computational constraints of robot controllers. In order to improve the performance of object detection for vision robots, a lightweight 3D object detection network based on You Only Look Once version 5 (YOLOv5) is proposed for satisfying industrial production. YOLOv5 work to efficiently object detection using deep convolutional networks. In terms of model deployment, we adopt a novel OpenVINO-based model deployment approach. The OpenVINO framework significantly enhances the inference speed of models by model optimization and compression. Our model achieves a 70% reduction in inference time compared to the baseline model on CPU. The effectiveness of the proposed method is demonstrated through experiments. © 2023 IEEE.

关键词： Object detection

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