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arXiv 2023年

作者： Chen, Long Li, Yuchen Huang, Chao Li, Bai Xing, Yang Tian, Daxin Li, Li Hu, Zhongxu Na, Xiaoxiang Li, Zixuan Teng, Siyu Lv, Chen Wang, Jinjun Cao, Dongpu Zheng, Nanning Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd United Kingdom BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon999077 Hong Kong The Department of Industrial and System Engineering Hong Kong Polytechnical University Hong Kong The College of Mechanical and Vehicle Engineering Hunan University China The School of Aerospace Transport and Manufacturing Cranfield University United Kingdom The School of Transportation Science and Engineering Beihang University China The Department of Automation Tsinghua University China The Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore The Centre for Sustainable Road Freight University of Cambridge United Kingdom The School of Mechanical Engineering Tsinghua University China The College of Artificial Intelligence Xi’an Jiaotong University China

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Moving Target Tracking control of a Biomimetic Robotic Fish

Moving Target Tracking Control of a Biomimetic Robotic Fish

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Chinese Automation Congress (CAC)

作者： Yangfan Zhou Yingnian Wu Chao Zhou Pengfei Zhang Zhengxing Wu School of Automation Beijing Information Science and Technology University Beijing China Naval Research Academy Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper proposes a moving target tracking control framework for a biomimetic robotic fish. Inspired by a boxfish in nature, a single-joint robotic fish with a pair of one-degree-of-freedom pectoral fins is first developed. A camera and an infrared sensor are specially employed to recognize the underwater target. In order to describe the fishlike swimming, a dynamic model based on the Newton-Euler method is constructed. Meanwhile, according to the control approach of fishlike swimming motion, a target tracking control framework including a speed controller and a direction controller is proposed to track a static or moving target, where the oscillation frequency and directional bias parameters are adopted to regulate the swimming speed and direction, respectively. Finally, simulation results verify the effectiveness of the proposed target tracking control method, where the robotic fish is controlled to track a red target moving in a circle at a certain speed.

关键词： Robots Target tracking Sports Robot kinematics Service robots Automation Mechatronics

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Programming by visual demonstration for pick-and-place tasks using robot skills

Programming by visual demonstration for pick-and-place tasks...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Hao, Peng Lu, Tao Cai, Yinghao Wang, Shuo State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781728163215

In this paper, we present a vision-based robot programming system for pick-and-place tasks that can generate programs from human demonstrations. The system consists of a detection network and a program generation module. The detection network leverages convolutional pose machines to detect the key-points of the objects. The network is trained in a simulation environment in which the train set is collected and auto-labeled. To bridge the gap between reality and simulation, we propose a design method of transform function for mapping a real image to synthesized style. Compared with the unmapped results, the Mean Absolute Error (MAE) of the model completely trained with synthesized images is reduced by 23% and the False Negative Rate FNR (FNR) of the model fine-tuned by the real images is reduced by 42.5% after mapping. The program generation module provides a human-readable program based on the detection results to reproduce a real-world demonstration, in which a longshort memory (LSM) is designed to integrate current and historical information. The system is tested in the real world with a UR5 robot on the task of stacking colored cubes in different orders. © 2019 IEEE.

关键词： Demonstrations

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Color-sensitive person re-identification 28

Color-sensitive person re-identification

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28th International Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Wang, Guan'an Yang, Yang Y. Cheng, Jian Wang, Jinqiao Hou, Zengguang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9780999241141

Recent deep Re-ID models mainly focus on learning high-level semantic features, while failing to explicitly explore color information which is one of the most important cues for person Re-ID model. In this paper, we propose a novel Color-Sensitive Re-ID to take full advantage of color information. On one hand, we train our model with real and fake images. By using the extra fake images, more color information can be exploited and it can avoid over-fitting during training. On the other hand, we also train our model with images of the same person with different colors. By doing so, features can be forced to focus on the color difference in regions. To generate fake images with specified colors, we propose a novel Color Translation GAN (CTGAN) to learn mappings between different clothing colors and preserve identity consistency among the same person with the clothing color. Extensive evaluations on two benchmark datasets show that our approach significantly outperforms state-of-the-art Re-ID models. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

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Analyzing the Evolutionary Characteristics of the Cluster of COVID-19 under Anti-contagion Policies

Analyzing the Evolutionary Characteristics of the Cluster of...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Pu Miao Hu Tian Xingwei Zhang Saike He Xiaolong Zheng Desheng Dash Wu Daniel Zeng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Stockholm Business School Stockholm University Stockholm Sweden

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatiotemporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies.

关键词： COVID-19 Epidemics Simulation Sociology Market research Spatiotemporal phenomena Statistics

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Double Deep Q-learning for Anti-saturation Attack Problem of Warship Group

Double Deep Q-learning for Anti-saturation Attack Problem of...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wei Li Gang Xiong Hao Tao Yunjun Han Fang Wang Xisong Dong Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan China School of Control Science and Engineering (CSE) Shandong University Jinan China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios.

关键词： Industries Heuristic algorithms Digital twin Conferences Knowledge based systems Reinforcement learning Markov processes

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A Two-Stage Learning Framework for Driver Lane Change Intention Inference

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IFAC-PapersOnLine 2020年第5期53卷 638-643页

作者： Yang Xing Bin Tian Chen Lv Dongpu Cao School of Mechanical and Aerospace Engineering Nanyang Technological University 639798 Singapore State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100190 China Department of Mechanical and Mechatronics Engineering University of Waterloo ON N2L3G1 Canada

With the rapid development of intelligent vehicles, human drivers are sharing the control authority with the automation functionalities. The mutual understanding between the intelligent vehicle and the human driver is the key to the efficient multi-agent teaming and the collaborative driving system. In this study, a two-stage learning framework for spatial-temporal driver lane change intention inference is developed. The two-stage learning framework contains two separate parts. First, an operational level driving behavior recognition system based on the deep convolutional neural network (CNN) is developed to recognize the driver behaviors, including mirror checking and normal driving. Then, based on the driver behavior features from the CNN, the sequential feature vector will be used for the construction of driver intention inference model based recurrent neural networks (RNN) and long short-term memory (LSTM). The proposed model achieved a state-of-the-art result on the driver lane change intention inference task. Based on the naturalistic driving dataset, the model achieved over 91% accuracy for lane change and lane-keeping intention prediction. The two-stage learning framework can significantly increase the model flexibility and accuracy, which makes it easier to be implemented and updated in intelligent vehicles.

关键词： Driver Behavior Driver Model Deep Learning LSTM Intelligent Vehicle

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Optimal vaccination program for two infectious diseases with cross immunity

arXiv

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arXiv 2020年

作者： Ye, Yang Zhang, Qingpeng Cao, Zhidong Zeng, Daniel Dajun School of Data Science City University of Hong Kong Hong Kong Hong Kong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shenzhen China

There are often multiple diseases with cross immunity competing for vaccination resources. Here we investigate the optimal vaccination program in a two-layer Susceptible-Infected-Removed (SIR) model, where two diseases with cross immunity spread in the same population, and vaccines for both diseases are available. We identify three scenarios of the optimal vaccination program, which prevents the outbreaks of both diseases at the minimum cost. We analytically derive a criterion to specify the optimal program based on the costs for different vaccines. Copyright © 2020, The Authors. All rights reserved.

关键词： Vaccines

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Deep Imitation Learning for Traffic Signal control and Operations Based on Graph Convolutional Neural Networks

Deep Imitation Learning for Traffic Signal Control and Opera...

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International Conference on Intelligent Transportation

作者： Xiaoshuang Li Zhongzheng Guo Xingyuan Dai Yilun Lin Junchen Jin Fenghua Zhu Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Harbin University Of Science And Technology Harbin China Enjoyor Co. Ltd. Hangzhou China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Traffic signal control plays an essential role in the Intelligent Transportation systems (ITS). Due to the intrinsic uncertainty and the significant increase in travel demand, in many cases, a traffic system still has to rely on human engineers to cope with the complicated and challenging traffic control and operation problem, which cannot be handled well by the traditional methods alone. Thus, imitating the good working experience of engineers to solve traffic signal control problems remains a practical, smart, and cost effective approach. In this paper, we construct a modelling framework to imitate how engineers cope with complex scenarios through learning from the historical record of manipulations by traffic operators. To extract spatial-temporal traffic demand features of the entire road network, a specially designed mask and a graph convolutional neural network (GCNN) are employed in this framework. The simulation experiments results showed that, compared with the original deployed control scheme, our method reduced the average waiting time, average time loss of vehicles, and vehicle throughput by 6.6%, 7.2%, and 6.85%, respectively.

关键词： Data models Roads Feature extraction Neural networks Detectors Convolutional neural networks Task analysis

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