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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第6期8卷 3657-3667页

作者： Shi, Pengcheng Zhu, Zhikai Sun, Shiying Rong, Zheng Zhao, Xiaoguang Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China NIO Inc Shanghai 201804 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China

Pose graph optimization helps reduce drift accumulated in pure odometry of visual simultaneous localization and mapping (SLAM) systems by solving a nonlinear least square problem, including both sequential constraints and loop-closing constraints. However, the covariances of all constraints are set to constant matrices or by manual setting. In this paper, we propose a novel approach to approximate covariances of constraints in pose graph optimization to better represent the true uncertainty of the underlying visual-inertial navigation system (VINS) that fuses inertial measurements and visual observations. Specifically, for sequential constraints, we propose to utilize nonlinear factor recovery to optimally extract covariance matrices from the accumulated visual-inertial odometry (VIO). For loop-closing constraints, we propose a dynamic scale estimation method to approximate the scales of the information matrices. To evaluate the effectiveness and robustness of the proposed method, we conduct extensive experiments on public and self-collected datasets in various environments. Results show that our proposed method achieves higher accuracy compared with naively-formulated pose graph optimization adopted by several state-of-the-art visual-inertial navigation systems.

关键词： Covariance estimation loop closing pose graph optimization visual-inertial odometry

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PiAGI: The Concept, Mechanism and Framework 4

PiAGI: The Concept, Mechanism and Framework

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Li, Qiang Ju, Zhuoya Fan, Lili Liu, Xin Liu, Wenhua Wang, Feiyue Scientific and Technological Innovation Center Beijing China Beijing Institute of Technology The School of Automation Beijing China China University of Petroleum School of Big Data & Cyber Security Qingdao China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

As social production and living spaces gradually expand from the physical and information domains to the cognitive and social domains, this article addresses the new changes brought about by these expansions. Starting from the new impact of era of intelligent connectivity on social cognitive management, it analyzes the urgent need to enhance social cognitive management capabilities centered on cognitive decision-making, identifying the core key as Adversarial Generated Intelligence (AGI). It delves into the conceptual characteristics, operating mechanisms, and winning strategies of AGI, identifies the new challenges it faces, and proposes a Parallel Intelligence empowering AGI (PiAGI) system architecture through the empowerment of parallel intelligence. This provides theoretical support for improving social cognitive management capabilities centered on cognitive decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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SynPoses: Generating Virtual Dataset for Pedestrian Detection in Corner Cases

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 801-804页

作者： Nie, Yunhao Lu, Bo Chen, Qiyuan Miao, Qinghai Lv, Yisheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Pedestrian detection based on deep learning methods makes a big hit during these days. The key to achieve excellent results for deep learning-based pedestrian detection methods is a high-quality dataset. There are lots of outstanding datasets for pedestrian detection like EuroCity Persons, CityPersons, Caltech, etc. However, their efforts are not dedicated to the traffic scenarios with pedestrians in various poses. When the state-of-the-art detectors trained on datasets described above encounter the scene with pedestrians in variously complicated poses, the results are mostly worse than expected. In order to alleviate this problem, we propose a framework called SynPoses to create synthetic human with complicated poses in high quality. Experimental results show that, when facing scenarios with human in diverse poses, the performance of detectors trained on augmented dataset outperforms those trained on original dataset.

关键词： Pedestrian detection augmentation dataset

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LLM-Based Operating systems for Automated Vehicles: A New Perspective

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4563-4567页

作者： Ge, Jingwei Chang, Cheng Zhang, Jiawei Li, Lingxi Na, Xiaoxiang Lin, Yilun Li, Li Wang, Fei-Yue Tsinghua Univ Dept Automat BNRist Beijing 100084 Peoples R China Indiana Univ Purdue Univ Indianapolis Dept Elect & Comp Engn Indianapolis IN 46202 USA Univ Cambridge Dept Engn Cambridge CB2 1TN England Shanghai AI Lab Shanghai 200232 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The deployment of large language models (LLMs) brings challenges to intelligent systems because its capability of integrating large-scale training data facilitates contextual reasoning. This paper envisions a revolution of the LLM based (Artificial) Intelligent Operating systems (IOS, or AIOS) to support the core of automated vehicles. We explain the structure of this LLM-OS and discuss the resulting benefits and implementation difficulties.

关键词： Operating systems Task analysis Software Hardware Cognition Safety Memory management Automated vehicles LLM operating system

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The DAO to Social Transportation: Towards Smart Mobility in Cyber-Physical-Social Space

The DAO to Social Transportation: Towards Smart Mobility in ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuanyuan Chen Yisheng Lv Fei-Yue Wang State Key Lab. of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Institute of Automation Beijing China State Key Lab. for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

The rapid advancement of intelligence and connectivity technology (ICT) has enhanced the efficiency and safety of intelligent transportation systems (ITS). However, this also increases the complexity of the transportation systems, especially the social complexity, and poses new challenges for their management. This paper discusses the emergence of social transportation, as part of natural evolutionary adaptation to new traffic conditions and technology, which is a paradigm shift from engineering-centered technical systems to society-centered ecosystems. In social transportation systems, all participants including administrators, individual travelers and other stakeholders play a more proactive role in the operation and management of transportation systems. Therefore, we propose to employ a decen-tralized autonomous organization (DAO) as a model for the social transportation system that allows participants to collaborate and coordinate without relying on a central authority. To meet the challenges brought by this transition, we propose to apply ACP-based parallel system theory to restructure the methodology of transportation management. Finally, we present a case study of personal carbon trading system where economic incentives are used to manage traffic demand, affecting others' travel behaviors, and reduce carbon emissions. In the conclusive remarks, we provide our visions and future directions of social transportation systems.

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An Underwater Robotic System With a Soft Continuum Manipulator for Autonomous Aquatic Grasping

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第2期29卷 1007-1018页

作者： Liu, Jiaqi Song, Zhuheng Lu, Yue Yang, Hui Chen, Xingyu Duo, Youning Chen, Bohan Kong, Shihan Shao, Zhuyin Gong, Zheyuan Wang, Shiqiang Ding, Xilun Yu, Junzhi Wen, Li Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Chinese Acad Sci Inst Deep Sea Sci & Engn Sanya 572000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Guangdong Acad Sci Inst Semicond Guangzhou 510610 Guangdong Peoples R China Peking Univ Coll Engn Dept Adv Mfg & Robot Beijing 100871 Peoples R China Univ Toronto Toronto ON M5S 1A1 Canada Peking Univ Coll Engn State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Delicate underwater manipulation tasks such as biological specimen collection are promising fields that require new robotic designs and intelligent robotic technologies. In this study, we proposed an automatic aquatic object-collecting system with a soft manipulator controlled by a reinforcement learning-based controller. For underwater sensing, we implemented a visual perception framework to restore the quality of the underwater image, detect the seafood animals, and track the target's position. The online learning ability of the reinforcement learning-based controller endowed strong adaptability for the soft manipulator against underwater disturbances. The water tank grasping tests show a 91.7% successful grasping rate without flow disturbance and 83.3% with flow disturbances. We demonstrated that the soft robotic collecting system gripped seafood animals in a lab aquarium as well as the natural seabed environment. The real-world experimental results showed that the robot successfully collected 28 shells within 40 min at a water depth of 15 m and even completed grasping tasks in a dark environment. Our results demonstrated that this manipulator prototype is potentially applicable for fully autonomous delicate objects underwater.

关键词： Grasping Robots Soft robotics Manipulator dynamics Task analysis Robot kinematics Biology Machine learning soft robotics underwater grasping

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Gait Planning and Motion control Based on Vrep Simulation for Quadruped Robot 5

Gait Planning and Motion Control Based on Vrep Simulation fo...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Parallel Treasury for TRUE DAOs: Model, Indicators and Mechanism

Parallel Treasury for TRUE DAOs: Model, Indicators and Mecha...

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Australian and New Zealand control Conference (ANZCC)

作者： Guan, Sangtian Li, Juanjuan Ding, Wenwen Wang, Fei-Yue Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China

ISBN: (纸本)9798350314977

In response to concerns over the centralization tendency in the decentralized autonomous organizations (DAOs), TRUE autonomous organizations and operations (TAOs or TRUE DAOs) have been proposed recently. TAOs aim at spreading equitable value distribution and democratized decision-making, distinguishing them from their DAOs counterparts. This study focuses on the treasury within TAOs, which acts as a central fund pool and a crucial element in the decentralized economy (DeEco) system. First, against a backdrop of potential black swan events and other long-tail unforeseen challenges, a reference model for the intelligent treasury management of TAOs is proposed. Then, an evaluation system, namely VALID, is presented with metrics including verifiability, anti-volatility, legitimacy, inclusiveness, and decentralization. Furthermore, a novel parallel treasury management mechanism is proposed to demonstrate a virtual-real interactive closed-loop management and control paradigm of the treasury, thereby fostering the formulation and development of DeEco. This research provides a comprehensive perspective on intelligent treasury management of TAOs and their role in sustainable advancement of DeEco.

关键词： Decision making

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Parallel Factories for Smart Industrial Operations:From Big AI Models to Field Foundational Models and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2079-2086页

作者： Jingwei Lu Xingxia Wang Xiang Cheng Jing Yang Oliver Kwan Xiao Wang Qingdao Academy of Intelligent Industries Qingdao 266114 The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Motion G Inc.Singapore 068898Singapore IEEE School of Artificial Intelligence Anhui UniversityHefei 230031

The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems,QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called Eu Artisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.

关键词： Cyber-physical-social system(CPSS) Industry5.0 Metaverses Parallel factories Parallel intelligence

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A novel varying-parameter periodic rhythm neural network for solving time-varying matrix equation in finite energy noise environment and its application to robot arm

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NEURAL COMPUTING & APPLICATIONS 2023年第30期35卷 22577-22593页

作者： Li, Chunquan Zheng, Boyu Ou, Qingling Wang, Qianqian Yue, Chong Chen, Limin Zhang, Zhijun Yu, Junzhi Liu, Peter X. Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Peking Univ Coll Engn State Key Lab Turbulence & Complex Syst Dept Mech & Engn SciBIC ESAT Beijing 100871 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

Solving matrix equation with noise interference is a challenging problem in mathematical and engineering applications. Unlike the traditional recurrent neural network, a novel varying-parameter periodic rhythm neural network (VP-PRNN) is proposed and used to solve the time-varying matrix equation in finite energy noise environment online. Particularly, VP-PRNN can enable the state solution to converge to the theoretical solution rapidly and robustly, which is also proved by theoretical analysis. Four kinds of noise are used to test the system, which proves the effectiveness of VP-PRNN. Compared with the zeroing neural network and circadian rhythms learning network with fixed parameters, VP-PRNN with variable parameters shows superior convergence performance in the disturbance of finite energy noise.

关键词： Zeroing neural network Varying-parameter Periodic rhythm Time-varying matrix equation Finite energy noise

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