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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4383-4393页

作者： Chen, Long Li, Yuchen Silamu, Wushour Li, Qingquan Ge, Shirong Wang, Fei-Yue Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China WAYTOUS Inc Beijing 100083 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China BNU HKBU United Int Coll Fac Sci & Technol Zhuhai 519087 Peoples R China Hong Kong Baptist Univ Kowloon Hong Kong 999077 Peoples R China Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China Shenzhen Univ Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China

The increasing importance of mineral resources in contemporary society is becoming more prominent, playing an indispensable and crucial role in the global economy. These resources not only provide essential raw materials for the global economic system but also play an irreplaceable role in supporting the development of modern industry, technology, and infrastructure. With the rapid development of intelligent technologies such as Industry 5.0 and advanced Large Language Models (LLMs), the mining industry is facing unprecedented opportunities and challenges. The development of smart mines has become a crucial direction for industry progress. This article aims to explore the strategic requirements for the development of smart mines by combining advanced products or technologies such as Chat-GPT (one of the successful applications of LLMs), digital twins, and scenario engineering. We propose a comprehensive architecture consisting of three different levels, the mining industrial Internet of Things (IoT) platform, mining operating systems, and foundation models. The systems and models empower the mining equipment for transportation. The architecture delivers a comprehensive solution that aligns perfectly with the demands of Industry 5.0. The application and validation outcomes of this intelligent solution showcase a noteworthy enhancement in mining efficiency and a reduction in safety risks, thereby laying a sturdy groundwork for the advent of Mining 5.0.

关键词： Digital twins Fifth Industrial Revolution Industries Task analysis Production Ontologies Biological system modeling Mining 5.0 smart mining autonomous driving industry 5.0 architectures mining transportation trucks

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Learning Hierarchical Graph Convolutional Neural Network for Object Navigation 31st

Learning Hierarchical Graph Convolutional Neural Network for...

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31st International Conference on Artificial Neural Networks (ICANN)

作者： Xu, Tao Yang, Xu Zheng, Suiwu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Huizhou Zhongke Adv Mfg Ltd Co Huizhou Peoples R China

ISBN: (纸本)9783031159312;9783031159305

The goal of object navigation is to navigate an agent to a target object using visual input. Without GPS and the map, one challenge of this task is how to locate the target object in the unseen environment, especially when the target object is not in the field of view. Previous works use relation graphs to encode the concurrence relationships among all the object categories, but these relation graphs are usually too flat for the agent to locate the target object efficiently. In this paper, a Hierarchical Graph Convolutional Neural Network (HGCNN) is proposed to encode the object relationships in a hierarchical manner. Specifically, the HGCNN consists of two graph convolution blocks and a graph pooling block, which constructs the hierarchical relation graph by learning an area-level graph from the object-level graph. Consequently, the HGCNN based framework enables the agent to locate the target object efficiently in the unseen environment. The proposed model is evaluated in the AI2-iTHOR environment, and the performance of object navigation shows a significant improvement.

关键词： Object navigation Hierarchical relation graph AI2-iTHOR

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Proactivity of fish and leadership of self-propelled robotic fish during interaction

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BIOINSPIRATION & BIOMIMETICS 2023年第3期18卷 036011-036011页

作者： Zhou, Ziye Liu, Jincun Pan, Jie Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Fish interacting with biomimetic robotic fish is beneficial for animal behavior research, particularly in the study of collective behavior. Compared with passive-dragging robotic fish, self-propelled robotic fish floats in water, and its movement matches the flow field formed by the caudal fin oscillation, leading to more realistic interaction with animals. In this paper, we propose a self-propelled koi-mimicking robotic fish entity, develop a system for robotic fish and koi fish interaction, and conduct extensive experiments on quantity variation and parameter variation. The results showed that fish exhibited significantly lower proactivity when alone, and the most proactive case is one robotic fish interacting with two real fish. The experiments on parameter variation indicated that fish may respond more proactivity to robotic fish that swim with high frequency and low amplitude, but may also move together with high-frequency and high-amplitude swimming robotic fish. These findings could provide insights into fish collective behavior, guide the design of further fish-robot interaction experiments, and suggest directions for future improvements in goal-oriented robotic fish platforms.

关键词： biomimetics animal interaction robotic fish self-propelled

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A New Recursive Composite Adaptive controller for Robot Manipulators

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Space(Science & Technology) 2021年第1期2021卷 77-83页

作者： Jianfei Li Yaobing Wang Zhiyong Liu Xin Jing Chengwei Hu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System EngineeringBeijing 100094China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

In this paper,a new recursive implementation of composite adaptive control for robot manipulators is *** investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form,which,to our knowledge,is the first result on this point in the *** proposed algorithm has an amount of computation O(n),which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree *** manipulator of the Chinese Space Station is employed as a simulation example,and the results verify the effectiveness of this proposed recursive algorithm.

关键词： Robot manipulators robot

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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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Recurrent Neural Network based Partially Observed Feedback control of Musculoskeletal Robots 7

Recurrent Neural Network based Partially Observed Feedback C...

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7th IEEE International Conference on Advanced Robotics and Mechatronics

作者： Chen, Jiahao Huang, Xiao Wang, Xiaona Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Technol Sch Mechatron Engn Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

The musculoskeletal robot has become a promising research direction. However, the control problem of the musculoskeletal robot still limits its application. Especially in the real-world application, only some incomplete and imprecise feedback states could be obtained due to the limitation of sensors. Therefore, this paper proposes a recurrent neural network (RNN) based partially observed feedback control method of musculoskeletal robots. The RNN has the ability of working memory and can realize muscle control through implicitly inferring sufficient states from partially observed states. It can also generate muscle excitations with synergies to guarantee great generalization. The effectiveness of the proposed method is verified on a simulated musculoskeletal system. Compared with other deep reinforcement learning method, the proposed method achieves similar performance under sufficient feedback states and better performance under partially observed feedback states.

关键词： Deep learning Recurrent neural networks Mechatronics Neuromuscular Reinforcement learning Robot sensing systems Hardware

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SWDPM: A Social Welfare-Optimized Data Pricing Mechanism

arXiv

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arXiv 2023年

作者： Yu, Yi Yao, Shengyue Wang, Fei-Yue Li, Juanjuan Lin, Yilun Urban Computing Lab Shanghai AI Laboratory Shanghai China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Privacy-Preserving Computing techniques, the market can now protect the privacy and complete transactions using progressively disclosed information, which creates a technical foundation for generating greater social welfare through data usage. In this study, we propose a novel approach to modeling multi-round data trading with progressively disclosed information using a matchmaking-based Markov Decision Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. To the best of our knowledge, this is the first study to model multi-round data trading with progressively disclosed information. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54% in trading feasibility, 43% in trading efficiency, and 25% in trading fairness by encouraging better matching of demand and price negotiation among traders. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Traffic Signal control Using Offline Reinforcement Learning

Traffic Signal Control Using Offline Reinforcement Learning

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2021 China Automation Congress, CAC 2021

作者： Dai, Xingyuan Zhao, Chen Li, Xiaoshuang Wang, Xiao Wang, Fei-Yue School of Artificial Intelligence University of Chinese Academy of Sciences China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781665426473

The problem of traffic signal control is essential but remains unsolved. Some researchers use online reinforcement learning, including the off-policy one, to derive an optimal control policy through interaction between agents and environments in simulation. However, it is difficult to deploy the policy in real transportation systems due to the gap between simulated and real traffic data. In this paper, we consider an offline reinforcement learning method to tackle the problem. First, we construct a realistic traffic environment and obtain offline data based on a classic actuated traffic signal controller. Then, we use an offline reinforcement learning algorithm, namely conservative Q-learning, to learn an efficient control policy via offline datasets. We conduct experiments on a typical road intersection and compare the conservative Q-learning policy with the actuated policy and two data-driven policies based on off-policy reinforcement learning and imitation learning. Empirical results indicate that in the offline-learning setting the conservative Q-learning policy performs significantly better than other baselines, including the actuated policy, but the other two data-driven policies perform poorly in test scenarios. © 2021 IEEE

关键词： Reinforcement learning

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Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in Visual Cognitive Development 5th

Fusing Knowledge and Experience with Graph Convolutional Ne...

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5th International Conference on Cognitive systems and Signal Processing, ICCSIP 2020

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong Zhang, Lu Ren, Dongchun Fan, Mingyu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Meituan-Dianping Group Beijing100190 China

ISBN: (纸本)9789811623356

Visual cognitive ability is important for intelligent robots in unstructured and dynamic environments. The high reliance on large amounts of data prevents prior methods to handle this task. Therefore, we propose a model called knowledge-experience fusion graph (KEFG) network for novel inference. It exploits information from both knowledge and experience. With the employment of graph convolutional network (GCN), KEFG generates the predictive classifiers of the novel classes with few labeled samples. Experiments show that KEFG can decrease the training time by the fusion of the source information and also increase the classification accuracy in cross-task learning. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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Visual Pencil: Design of Portable Human-Computer Interaction Based on 2D Visual Tracking 17

Visual Pencil: Design of Portable Human-Computer Interaction...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Tong, Ru Wang, Tianzhu Yu, Junzhi Institute of Automation Cas State Key Lab Management and Control for Complex Systems Beijing100190 China

ISBN: (纸本)9781728164151

Convenient and efficient human-computer interaction has always been the goal pursued by researchers. In this paper, we present a vision-based handwriting interaction device for non-touch screens called Visual Pencil. Unlike the conventional interactive method that captures the user's gestures using a motionless visual sensor, Visual Pencil directly mounts the visual sensor in the device. More specifically, the visual data are processed by the feature detection algorithm, and the movement of the pen tip in a 2D plane is calculated, so as to control the cursor and achieve the purpose of interaction. We further define performance indicators for evaluating the design and select more reasonable parameters through specific experiments to achieve the best interaction performance. The experiments verify that the proposed design is able to fulfill the desired operations. The significance of this design lies in that it integrates the functions of the mouse and smart pen, thus removing the requirement of more auxiliary equipment. This work sheds light on the interaction with non-touch screen computers. © 2020 IEEE.

关键词： Human computer interaction

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