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arXiv 2023年

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long The School of Artificial Intelligence Hubei University Hubei Wuhan430062 China The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Beijing Huairou Academy of Parallel Sensing Beijing101499 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100864 China The Waytous Inc. Beijing100083 China

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Grasp-Anything: Large-scale Grasp Dataset from Foundation Models

arXiv

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arXiv 2023年

作者： Vuong, An Dinh Vu, Minh Nhat Le, Hieu Huang, Baoru Huynh, Binh T.T. Vo, Thieu Kugi, Andreas Nguyen, Anh FPT Software AI Center Viet Nam Automation & Control Institute TU Wien Vienna Austria AIT Austrian Institute of Technology Vienna Austria Imperial College London United Kingdom Hanoi University of Science and Technology Viet Nam Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science University of Liverpool United Kingdom

Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains. In this paper, we leverage foundation models to tackle grasp detection, a persistent challenge in robotics with broad industrial applications. Despite numerous grasp datasets, their object diversity remains limited compared to real-world figures. Fortunately, foundation models possess an extensive repository of real-world knowledge, including objects we encounter in our daily lives. As a consequence, a promising solution to the limited representation in previous grasp datasets is to harness the universal knowledge embedded in these foundation models. We present Grasp-Anything, a new large-scale grasp dataset synthesized from foundation models to implement this solution. Grasp-Anything excels in diversity and magnitude, boasting 1M samples with text descriptions and more than 3M objects, surpassing prior datasets. Empirically, we show that Grasp-Anything successfully facilitates zero-shot grasp detection on vision-based tasks and real-world robotic experiments. Our dataset and code are available at https://***. © 2023, CC BY-NC-SA.

关键词： Robotics

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Results of a real world trial with a mobile social service robot for older adults 16

Results of a real world trial with a mobile social service r...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Jürgen Pripfl Tobias Körtner Daliah Batko-Klein Denise Hebesberger Markus Weninger Christoph Gisinger Susanne Frennert Hakan Eftring Margarita Antona Ilia Adami Astrid Weiss Markus Bajones Markus Vincze Akademie für Altersforschung am Haus der Barmherzigkeit Vienna Austria Zentrum für Geriatrische Medizin und Geriatrische Pflege Donau Universität Krems Krems Austria Department of Design Sciences Lund University Lund Sweden Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Heraklion Greece Automation and Control Institute (ACIN) Vienna University of Technology Vienna Austria

ISBN: (纸本)9781467383707

Robots are an increasingly discussed solution for assistance of seniors. Importance of testing natural interaction therefore becomes crucial. This paper presents first results of a study with an autonomous mobile social service robot prototype that was deployed in 18 private households of senior adults aged 75 years and older for a total of 371 days. Findings show that utility met the users' expectations. However, the robot was rather seen as a toy instead of being supportive for independent living. Furthermore, despite of an emergency function of the robot, perceived safety did not increase. Reasons for this might be the good health conditions of our users, a lack of technological robustness and slow performance of the prototype. However, users believed that a market ready version of the robot would be vital for supporting people who are more fragile and more socially isolated.

关键词： Prototypes Safety Service robots Mobile communication Usability Aging

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Rules-based autonomous obstacle avoidance for mobile robots

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 493-497页

作者： Wang, Qi-Zhi Xu, De School of Computer Science and Information Technology Beijing Jiaotong University Beijing 100044 China Key Laboratory of Intelligent Control of Complex Systems Institute of Automation Chinese Acad. of Sci. Beijing 100080 China

A real time and autonomous obstacle avoidance method based on rules for mobile robots was presented. Wall- along algorithm was dynamically implemented in unknown environment without collision. The results show that th... 详细信息

关键词： Mobile robots

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Auto-tuning PID controller of Internet congestion control system

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Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) 2005年第SUPPL.期35卷 67-70页

作者： Yang, Hongyong Zhang, Fuzeng Lin, Xiangze Zong, Guangdeng Department of Computer Science and Technology Yantai Normal College Yantai 264025 China Department of Automatic Control Engineering Southeast University Nanjing 210096 China Institute of Automation Qufu Normal University Qufu 273165 China

Internet congestion control system is considered as a closed-loop feedback system with time-delay. A proportional-integral-differential (PID) controller is then established as active queue management (AQM) scheme to adjust the congestion marked possibility at the links in Internet, whose parameters is auto-tuned by applying the minimization of an integral criterion for adapting the change of the network system. The simulations show that the new AQM scheme can control the size of the queue in the buffer to an expected length and has the robustness for the loading perturbation and the parameter change in the network.

关键词： Congestion control (communication)

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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A novel method to increase energy density of permanent magnet machine based on multi-physical field analysis

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UPB Scientific Bulletin, Series D: Mechanical Engineering 2020年第4期82卷 79-92页

作者： Zheng, Qingchun Zhao, Yangyang Zhu, Peihao Wang, Sen Liu, Jingna Ma, Wenpeng Li, Xiang Xiao, Yinhong Wang, Naixin Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China School of Computer Science Tianjin University of Technology Tianjin300384 China School of Automation Shenyang Institute of Engineering Shenyang110136 China Baoding Power Supply Company Hebei Electric Power Company State Grid Corporation of China Baoding071000 China

As an important component of all-electric aircraft, the main propulsion machine is developing towards high energy density, high efficiency and high fault tolerance. In order to improve the energy density of main propulsion machine for all-electric aircraft, a novel approach, which can be used for excellent performance improvement of electric machine of all-electric aircraft, is reported in this paper. The analytical formula of energy density is put forward and the design method of high energy density permanent magnet machine is presented. A high energy density machine with rated power of 30kW is designed, and the electromagnetic field simulation analysis, strength and stress structure verification, temperature field simulation analysis and multi-physical field coupling analysis are obtained. A prototype is developed, the experiment platform is established, and experiments are performed. The energy density of main propulsion machine designed is up to 21kW·N·m/kg2, and the index is increased by 3.2 times. The optimal method based on the multi-physical field analysis can be also applied to other high energy density machines. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Electromagnetic fields

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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

arXiv

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arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： computer operating systems

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Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search

arXiv

引用

arXiv 2021年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Chen, C.L. Philip State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China School of Computer Science & Engineering South China University of Technology Guangdong Guangzhou510006 China College of Navigation Dalian Maritime University Dalian116026 China

Different from other deep scalable architecture-based NAS approaches, Broad Neural Architecture Search (BNAS) proposes a broad scalable architecture which consists of convolution and enhancement blocks, dubbed Broad Convolutional Neural Network (BCNN), as the search space for amazing efficiency improvement. BCNN reuses the topologies of cells in the convolution block so that BNAS can employ few cells for efficient search. Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology. However, BNAS suffers some drawbacks: 1) insufficient representation diversity for feature fusion and enhancement and 2) time consumption of knowledge embedding design by human experts. This paper proposes Stacked BNAS, whose search space is a developed broad scalable architecture named Stacked BCNN, with better performance than BNAS. On the one hand, Stacked BCNN treats mini BCNN as a basic block to preserve comprehensive representation and deliver powerful feature extraction ability. For multi-scale feature enhancement, each mini BCNN feeds the outputs of deep and broad cells to the enhancement cell. For multi-scale feature fusion, each mini BCNN feeds the outputs of deep, broad and enhancement cells to the output node. On the other hand, Knowledge Embedding Search (KES) is proposed to learn appropriate knowledge embeddings in a differentiable way. Moreover, the basic unit of KES is an over-parameterized knowledge embedding module that consists of all possible candidate knowledge embeddings. Experimental results show that 1) Stacked BNAS obtains better performance than BNAS-v2 on both CIFAR-10 and ImageNet, 2) the proposed KES algorithm contributes to reducing the parameters of the learned architecture with satisfactory performance, and 3) Stacked BNAS delivers a state-of-the-art efficiency of 0.02 GPU days. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Intelligent modified predictive optimal control of reheater steam temperature in a large-scale boiler unit

Intelligent modified predictive optimal control of reheater ...

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IEEE General Meeting Power& Energy Society

作者： Kwang Y. Lee Liangyu Ma Chang-Jin Boo Won-Hee Jung Sung-Ho Kim Department of Electrical and Computer Engineering Baylor University Waco TX USA Automation Department School of Control Science and Engineering North China Electric Power University Baoding Hebei China Department of Electrical Engineering Cheju National University Jelu South Korea Corporate Research and Development Institute Doosan Heavy Industries and Construction Company Limited Taejon South Korea

A modified predictive optimal control (MPOC) scheme based on neural network modeling and particle swarm optimization (PSO) techniques is proposed in this paper for reheater steam temperature (RST) control of a large-scale boiler unit. A recurrent neural network is trained to directly model the temperature dynamic response of the reheater system. The neural network direct model is then used to evaluate the performance of the MPOC in search of the optimal control, where optimization is carried out with the PSO. A simplified PSO algorithm with search direction control is designed to find the nearest and optimal controls for the reheater steam temperature. To further improve the optimal search accuracy, each last-step prediction error between the direct model output and the actual RST is added to the current-step cost function to compensate for the model error. control tests on a full-scope simulator of a large scale power generating unit have shown the validity of the proposed method.

关键词： Optimal control Large-scale systems Boilers Predictive models Neural networks Temperature control Particle swarm optimization Recurrent neural networks Algorithm design and analysis Accuracy

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