检索结果-内蒙古大学图书馆

2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Chen, Yuanyuan Lv, Yisheng Wang, Fei-Yue Institute of Automation State Key Lab. of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China Chinese Academy of Sciences State Key Lab. for Management and Control of Complex Systems Beijing100190 China

ISBN: (纸本)9798350337020

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. © 2023 IEEE.

关键词： block chain cyber-physical-social system decentralized and autonomous organization social transportation

来源：评论

学校读者我要写书评

暂无评论

Civilian Autonomous Carriers for Peaceful Services: Intelligent Vehicle Carries Enabled Future Transportation and Logistics

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第11期8卷 4463-4467页

作者： Wang, Fei-Yue Huang, Hailong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

Our editorial focuses on intelligent vehicle carries-enabled future transportation and logistics, aiming at facilitating the realization of smart urban environments. Various promising applications can be expected ranging from low-altitude air parcel delivery to offshore aquaculture. Our goal is to attract more good ideas in this direction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning for Depth control of a Robotic Penguin: A Data-Driven Model Predictive control Approach

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第11期70卷 11422-11432页

作者： Pan, Jie Zhang, Pengfei Wang, Jian Liu, Mingxin Yu, Junzhi Peking Univ Dept Adv Mfg & Robot Coll Engn State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China 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 Guangdong Ocean Univ Coll Elect & Informat Engn Zhanjiang 524088 Peoples R China

For bionic underwater robots, it is a great challenge for depth control due to model uncertainty and strong nonlinearity. To this end, we propose a data-driven model predictive control (MPC) approach using reinforcement learning (RL) for robotic penguin depth control. First, by imitating the underwater mode of the biological penguin, a robotic prototype with a tendon-driven head, two-degrees-of-freedom wings, and a tendon-driven tail was designed. Then, a data-driven MPC framework is proposed considering the structure and motion properties of the robotic penguin. Especially, a data-based learning environment is constructed using a motion capture system, computational fluid dynamics, and a backpropagation neural network. Meanwhile, to maximize the benefits of the controller while ensuring safety and stability, a data-driven MPC using the RL scheme is applied to approximate the optimal policy. Combined with an appropriate reward design and periodic training, the closed-loop controller performance is significantly improved, and the validity of the proposed framework is finally tested by extensive simulations and experiments. Notably, this work will provide valuable insights into the learning-based motion control of bionic underwater robots.

关键词： Data-driven model predictive control (MPC) depth control motion control reinforcement learning (RL) robotic penguin

来源：评论

学校读者我要写书评

暂无评论

Multiagent-Reinforcement-Learning-Based Stable Path Tracking control for a Bionic Robotic Fish With Reaction Wheel

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第12期70卷 12670-12679页

作者： Qiu, Changlin Wu, Zhengxing Wang, Jian Tan, Min Yu, Junzhi 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 Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Guangdong Ocean Univ Coll Elect & Informat Engn Zhanjiang 524088 Peoples R China

The path tracking of the robotic fish is a hotspot with its high maneuverability and environmental friendliness. However, the periodic oscillation generated by bionic fish-like propulsion mode may lead to unstable control. To this end, this article proposes a novel framework involving a newly designed platform and multiagent reinforcement learning (MARL) method. First, a bionic robotic fish equipped with a reaction wheel is developed to enhance the stability. Second, an MARL-based control framework is proposed for the cooperative control of tail-beating and reaction wheel. Correspondingly, a hierarchical training method including initial training and iterative training is designed to deal with the control coupling and frequency difference between two agents. Finally, extensive simulations and experiments indicate that the developed robotic fish and the proposed MARL-based control framework can effectively improve the accuracy and stability of path tracking. Remarkably, headshaking is reduced about 40%. It provides a promising reference for the stability optimization and cooperative control of bionic swimming robots featuring oscillatory motions.

关键词： Multiagent reinforcement learning (MARL) path tracking control reaction wheel robotic fish underwater robot

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial of the Special Issue on the 3rd IEEE International Conference on Digital Twins and Parallel Intelligence (IEEE DTPI 2023)

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

引用

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2024年 8卷 860-864页

作者： Wang, Yutong Wang, Xiao Bennett, Gisele Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230031 Peoples R China MEPSS LLC Indian Harbor Beach FL 32937 USA Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

来源：评论

学校读者我要写书评

暂无评论

Event-Triggered Deep Reinforcement Learning Using Parallel control: A Case Study in Autonomous Driving

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2821-2831页

作者： Lu, Jingwei Han, Liyuan Wei, Qinglai Wang, Xiao Dai, Xingyuan Wang, Fei-Yue Qingdao Acad Intelligent Ind Parallel Intelligence Innovat Res Ctr Qingdao 266109 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China 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 Anhui Univ Sch Artificial Intelligence Hefei 230031 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266114 Peoples R China

This paper utilizes parallel control to investigate the problem of event-triggered deep reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for decision-making of autonomous driving, without training an explicit triggering condition. Based on the framework of parallel control, the developed ETDQN incorporates information of actions into the feedback and constructs a dynamic control policy. First, in the realization of the dynamic control policy, we integrate the current state and the previous action to construct the augmented state as well as the augmented Markov decision process. Meanwhile, it is shown theoretically that the goal of the developed dynamic control policy is to learn the variation rate of the action. The augmented state contains information of the current state and the previous action, which enables the developed ETDQN to directly design the immediate reward considering communication loss. Then, based on dueling double deep Q-network (dueling DDQN), we establish the augmented action-value, value, and advantage functions to directly learn the optimal event-triggered decision-making policy of autonomous driving without an explicit triggering condition. It is worth noticing that the developed ETDQN applies to various deep Q-networks (DQNs). Empirical results demonstrate that, in event-triggered control, the developed ETDQN outperforms dueling DDQN and reduces communication loss effectively.

关键词： Autonomous vehicles Decision making Path planning Training Optimal control Deep learning complex systems Autonomous driving deep reinforcement learning deep Q-network event-triggered control parallel control

来源：评论

学校读者我要写书评

暂无评论

Vision-Based Underwater Target Following control of an Agile Robotic Manta With Flexible Pectoral Fins

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第4期8卷 2277-2284页

作者： Meng, Yan Wu, Zhengxing Li, Yating Chen, Di Tan, Min Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Guangdong Ocean Univ Coll Elect & Informat Engn Zhanjiang 524088 Peoples R China

Manta-inspired robots have been increasingly applied in vision-based underwater tasks. However, owing to the refraction effect and motion coupling problem, their accurate target following control is still poorly understood. This letter proposes a vision-based underwater target following scheme for robotic manta. First, an agile robotic manta equipped with a binocular camera system is constructed. The motion modes are further detailed based on the propulsion analysis to the flexible pectoral fins. Second, a color-based underwater target detection and localization method is conceived. Specifically, the binocular refraction correction is employed to improve the localization accuracy. Third, combing the nonsingular terminal sliding mode controller and fuzzy inference system, a robust target following controller is presented to solve the coupling between distance and azimuth control of the robot. Finally, various aquatic experiments are conducted, verifying the effectiveness and robustness of the proposed underwater target following scheme. The obtained results provide valuable insights into the intelligent control of agile bioinspired robots performing vision-based tasks in dynamic underwater environments.

关键词： Robot kinematics Location awareness Cameras Task analysis Robot vision systems Turning Sports Biologically-inspired robots robotic manta target following control binocular underwater localization sliding mode control

来源：评论

学校读者我要写书评

暂无评论

A Wire-driven Elastic Robotic Fish and its Design and CPG-Based control

引用

JOURNAL OF INTELLIGENT & ROBOTIC systemS 2023年第1期107卷 1-13页

作者： Liao, Xiaocun Zhou, Chao Wang, Jian Fan, Junfeng Zhang, Zhuoliang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence 19 Yuquan Rd Beijing 100049 Peoples R China

To simulate the complex and continuous undulation of fishtail in nature, the method of adopting the discrete Multi-Joint mechanism requires a certain number of degrees of freedom, which results in the complexities of mechanism and control necessarily. Compared with Multi-Joint, flexible tail is a better scheme due to continuum, robustness, and simpler control. Hence, this paper proposes a wire-driven elastic robotic fish with flexible tail, which simulates fish muscle through multi-wire drive and adopts a fishlike spine design based on elastic component. Due to these distinctive designs, our robotic fish not only realizes the compliant simulation of fishlike swimming gait, but also owns higher bionic degree. Further, the kinematics model and speed estimation model of the wire-driven elastic robotic fish are developed, and the error between the body wave and the desired fishlike swimming gait is further optimized so as to determine the appropriate parameters of central pattern generator. The results show that the optimized body wave of fishtail matches well with the desired fishlike swimming gait and the RMSE of the stable swimming speed between simulations and experiments is 0.045 m/s, which validates the proposed model and optimization method. Finally, the relationships between the frequency and swimming speed under small amplitude are explored, from which we find that amplitude has a greater impact on speed than frequency at high frequency, and the maximum swimming speed of about 0.54 m/s, i.e., 1.02 BL/s, is obtained.

关键词： Robotic fish Wire-driven mode Elastic component Kinematics model Body wave

来源：评论

学校读者我要写书评

暂无评论

Invariant Extended Kalman Filtering for Tightly Coupled LiDAR-Inertial Odometry and Mapping

引用

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第4期28卷 2213-2224页

作者： Shi, Pengcheng Zhu, Zhikai Sun, Shiying 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

In this article, we extend the invariant extended Kalman filter (EKF) to light detection and ranging (LiDAR)-inertial odometry and mapping systems using invariant observer design and the theory of Lie groups for directly fusing LiDAR and inertial measurement unit (IMU) measurements. We consider this from two different aspects and implement two independent systems. Specifically, we propose a robo-centric invariant EKF LiDAR-inertial odometry termed Inv-LIO1. Its mapping module is an ordinary used one and two modules run in separate threads. A world-centric invariant EKF LiDAR-inertial odometry termed Inv-LIO2 is designed and implemented, which has an integrated odometry and mapping architecture. In Inv-LIO1, the output of the filter is the pose estimated by the scan-to-scan match method, which serves as the initial estimate of the mapping module that refines the odometry and constructs a 3-D map. The robo-centric formulation represents that the state in a local frame shifted at every LiDAR time to prevent filter divergence. Inv-LIO2 directly fuses LiDAR feature points and IMU data to obtain the map-refined odometry by scan-to-map match method, followed by global map update. To validate the effectiveness and robustness of the proposed method, we conduct extensive experiments in various indoor and outdoor environments. Overall, Inv-LIO1 offers pure odometry with higher accuracy than other state-of-the-art systems, improving the overall performance. Inv-LIO2 achieves superior accuracy over other state-of-the-art systems in the map-refined odometry comparison.

关键词： Laser radar Feature extraction Simultaneous localization and mapping Robustness Point cloud compression Optimization Kalman filters Invariant extended kalman filter (EKF) light detection and ranging (LiDAR)-inertial odometry multisensor fusion localization state estimation

来源：评论

学校读者我要写书评

暂无评论

Towards Better Generalization of Deep Neural Networks via Non-Typicality Sampling Scheme

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2023年第10期34卷 7910-7920页

作者： Peng, Xinyu Wang, Fei-Yue Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

Improving the generalization performance of deep neural networks (DNNs) trained by minibatch stochastic gradient descent (SGD) has raised lots of concerns from deep learning practitioners. The standard simple random sampling (SRS) scheme used in minibatch SGD treats all training samples equally in gradient estimation. In this article, we study a new data selection method based on the intrinsic property of the training set to help DNNs have better generalization performance. Our theoretical analysis suggests that this new sampling scheme, called the nontypicality sampling scheme, boosts the generalization performance of DNNs through biasing the solution toward wider minima, under certain assumptions. We confirm our findings experimentally and show that more variants of minibatch SGD can also benefit from the new sampling scheme. Finally, we discuss an extension of the nontypicality sampling scheme that holds promise to enhance both generalization performance and convergence speed of minibatch SGD.

关键词： Training Estimation Deep learning Standards Optimization Noise measurement Convergence Deep learning generalization performance nontypicality sampling scheme stochastic gradient descent (SGD)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：