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Missile guidance with assisted deep reinforcement learning for head-on interception of maneuvering target

complex & INTELLIGENT systems 2022年第2期8卷 1205-1216页

作者： Li, Weifan Zhu, Yuanheng Zhao, Dongbin 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 Beijing 101408 Peoples R China

In missile guidance, pursuit performance is seriously degraded due to the uncertainty and randomness in target maneuverability, detection delay, and environmental noise. In many methods, accurately estimating the acceleration of the target or the time-to-go is needed to intercept the maneuvering target, which is hard in an environment with uncertainty. In this paper, we propose an assisted deep reinforcement learning (ARL) algorithm to optimize the neural network-based missile guidance controller for head-on interception. Based on the relative velocity, distance, and angle, ARL can control the missile to intercept the maneuvering target and achieve large terminal intercept angle. To reduce the influence of environmental uncertainty, ARL predicts the target's acceleration as an auxiliary supervised task. The supervised learning task improves the ability of the agent to extract information from observations. To exploit the agent's good trajectories, ARL presents the Gaussian self-imitation learning to make the mean of action distribution approach the agent's good actions. Compared with vanilla self-imitation learning, Gaussian self-imitation learning improves the exploration in continuous control. Simulation results validate that ARL outperforms traditional methods and proximal policy optimization algorithm with higher hit rate and larger terminal intercept angle in the simulation environment with noise, delay, and maneuverable target.

关键词： Reinforcement learning Missile guidance Auxiliary learning Self-imitation learning

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NA-CPG: A robust and stable rhythm generator for robot motion control

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Biomimetic Intelligence & Robotics 2022年第4期2卷 30-38页

作者： Ru Tong Changlin Qiu Zhengxing Wu Jian Wang Min Tan Junzhi Yu State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Central pattern generators(CPGs)have been widely applied in robot motion control for the spontaneous output of coherent periodic ***,the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces,and the coupling of oscillators affects the network output accuracy in complex topological ***,CPGs may diverge when parameters change drastically,and the divergence is irreversible,which is catastrophic for the control of robot ***,normalized asymmetric CPGs(NA-CPGs)that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are ***-CPGs can realize parameter decoupling,precise amplitude output,and stable and rapid convergence,as well as asymmetric output ***,it can effectively cope with large parameter changes to avoid network oscillations and *** optimize the parameters of the NA-CPG model,a reinforcement-learning-based online optimization method is further ***,a biomimetic robotic fish is illustrated to realize the whole optimization *** demonstrated that the designed NA-CPGs exhibit stable,secure,and accurate network outputs,and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7%and 41.7%,*** proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.

关键词： Normalized asymmetric CPGs Rhythm generator Motion control Robotic fish

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Completed Interaction Networks for Pedestrian Trajectory Prediction

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zhong Zhou, Jianglin Liu, Shuang Xiao, Baihua Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin300387 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing100190 China

The social and environmental interactions, as well as the pedestrian goal are crucial for pedestrian trajectory prediction. This is because they could learn both complex interactions in the scenes and the intentions of the pedestrians. However, most existing methods either learn the one-moment social interactions, or supervise the pedestrian trajectories using long-term goal, resulting in suboptimal prediction performances. In this paper, we propose a novel network named Completed Interaction Network (CINet) to simultaneously consider the social interactions in all moments, the environmental interactions and the short-term goal of pedestrians in a unified framework for pedestrian trajectory prediction. Specifically, we propose the Spatio-Temporal Transformer Layer (STTL) to fully mine the spatio-temporal information among historical trajectories of all pedestrians in order to obtain the social interactions in all moments. Additionally, we present the Gradual Goal Module (GGM) to capture the environmental interactions under the supervision of the short-term goal, which is beneficial to understanding the intentions of the pedestrian. Afterwards, we employ the cross-attention to effectively integrate the all-moment social and environmental interactions. The experimental results on three standard pedestrian datasets, i.e., ETH/UCY, SDD and inD demonstrate that our method achieves a new state-of-the-art performance. Furthermore, the visualization results indicate that our method could predict trajectories more reasonably in complex scenarios such as sharp turns, infeasible areas and so on. © 1999-2012 IEEE.

关键词： Prediction models

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Towards Automated Segmentation of Human Abdominal Aorta and Its Branches Using a Hybrid Feature Extraction Module with LSTM 29th

Towards Automated Segmentation of Human Abdominal Aorta and ...

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29th International Conference on Neural Information Processing

作者： Zhang, Bo Liu, Shiqi Xie, Xiaoliang Zhou, Xiaohu Hou, Zengguang Song, Meng Ma, Xiyao Zhang, Linsen Univ Sci & Technol Beijing Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9789819916474;9789819916481

Abdominal aortic aneurysm (AAA) is a disease with high rates of morbidity and mortality. For AAA treatment, appropriately covered stents are placed to prevent blood from entering the aneurysm. Before the interventional procedure, the morphology of the abdominal arteries (the aorta and its branches) is of the utmost importance to formulate surgical plans and prepare surgical instruments. Due to accurate vessel measurements, in particular, custom-made fenestrated stent grafts and chimney stent grafts are widely used for complicated AAAs. Therefore, rapid and accurate reconstruction of the vascular from CTA images is necessary. In this study, we proposed a novel network for automated segmentation of the human abdominal aorta and its branches. The hybrid feature extraction module, which combines convolution and transformer, represents intra-frame vessel feature information well. And the inter-frame vessel structure information is extracted by a special attention mechanism that includes the LSTM module. The results of 38 patients' CTA image sequences show that the proposed network achieves state-of-the-art performance. The proposed method can assist physicians with preoperative diagnosis.

关键词： Abdominal Aortic Aneurysm Segmentation Computed Tomography Angiography

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VistaGPT: Generative Parallel Transformers for Vehicles With Intelligent systems for Transport Automation

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第9期8卷 4198-4207页

作者： Tian, Yonglin Li, Xuan Zhang, Hui Zhao, Chen Li, Bai Wang, Xiao Wang, Xiao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA Anhui Univ Hefei 266114 Peoples R China

Diverse transport demands have resulted in the wide existence of heterogeneous vehicle automation systems. While these systems have demonstrated effectiveness, they also pose challenges in terms of the share of technological advancements among different organizations and lead to poor generalization ability of individual systems. This article proposes a Transformer-based unified framework, VistaGPT, to address these challenges. VistaGPT, composed of Modular Federations of Vehicular Transformers (M-FoV) and Automated Composing of Autonomous Driving systems (AutoAuto), aims to overcome the information barriers due to system-level and module-level heterogeneity. M-FoV collects and organizes Transformer-based models in a modular fashion to facilitate system integration by providing diversity and versatility. AutoAuto utilizes large language models (LLMs) to automatically compose end-to-end autonomous driving systems with a "Dividing and Recombining" strategy. Besides, we deploy Scenario Engineering systems to evaluate the composed systems and provide systematic feedback for the optimization of AutoAuto, and Federated intelligence to contribute to diverse training samples and applications. With its capacity, scalability, and diversity, VistaGPT provides a new paradigm of LLM-aided system development for transport automation, which promotes virtual-real interactive parallel driving and advances progress toward "6S" objectives.

关键词： Transformers Task analysis Autonomous vehicles Planning Biological system modeling Navigation Automation Generative parallel transformers end-to-end driving transport automation large-language models federation of vehicular transformers scenario engineering

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Constructing Constraint Force Field in Musculoskeletal Robot by Co-optimizing Muscle Arrangements and Constant Activations

Constructing Constraint Force Field in Musculoskeletal Robot...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Zhong, Shanlin Zhou, Junjie Wu, Wei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Robots with high-precision motion and operation ability are of great application significance. By referring to the biomechanical structure and neural control mechanism of human motion system, the research of musculoskeletal robot system with rigid-flexible coupling characteristics is one of the important ways to improve the operation flexibility and control robustness of robot. Inspired by the equilibrium point hypothesis proposed in neuroscience, this paper proposes a co-optimization algorithm of muscle arrangement and activation to construct constraint force field in the workspace of musculoskeletal robot. When the muscle arrangement is rough due to the insufficient precision of the mechanical structure, the musculoskeletal robot can maintain accurate motion with the help of constraint force field by adopting the optimized constant activation. Experiments are carried out on a musculoskeletal robot model with human-mimetic muscle to demonstrate the effectiveness of the proposed algorithm in movement accuracy, noise robustness and generalization. This work may be of great significance for the further introduction of constraint force field into hardware system of musculoskeletal robot.

关键词： Musculoskeletal robot Constraint Force Field High precision control Schemes of robot

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A Framework and Operational Procedures for Metaverses-Based Industrial Foundation Models

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2037-2046页

作者： Wang, Jiangong Tian, Yonglin Wang, Yutong Yang, Jing Wang, Xingxia Wang, Sanjin Kwan, Oliver 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 Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China China North Ind North Automatic Control Technol Re North Automat Control Technol Inst Taiyuan 030006 Peoples R China Mot G Inc Shenzhen 518071 Peoples R China

Industrial processes are typical cyber-physical-social systems (CPSSs), where the effective management of employees and the efficient control of machines play important roles. Traditional industries heavily rely on human labor and neglect the development of collection-utilization-transmission integrated information loops, thereby leading to high costs and low efficiency in operational procedures. To facilitate the natural interactions and smart operations for humans and machines, industrial foundation models (IFMs) based on metaverses are proposed in this article, serving as the operating systems of industrial parallel machines that provide sustainable data resources and scenarios for management and control experiments. On this basis, IFM comprised of vision foundation models, language foundation models, as well as operational foundation models, are constructed to manage resources in industrial parallel machines and provides comprehensive services for industrial procedures. On the one hand, IFM can efficiently manage various resources including computing power, digital assets, enterprise resources, and platform I/O via the proposed CPSS-based competing, sharing, scheduling, monitoring, allocating, and recovering mechanisms. On the other hand, imaginative intelligence, linguistic intelligence, and algorithmic intelligence can be achieved through vivid visualization of vision foundation models, natural conversations of language foundation models, and smart manipulation of operational foundation models. With the proposed IFM, cyber-physical-social intelligence (CPSI) can be achieved to enhance the efficient management and control of industrial processes.

关键词： Cyber-physical-social intelligence (CPSI) cyber-physical-social systems (CPSSs) industrial foundation models (IFMs) intelligent enterprises metaverses operational processes parallel intelligence

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Mobility Pattern Analysis during 2020 Chinese National Day under the COVID-19 pandemic

Mobility Pattern Analysis during 2020 Chinese National Day u...

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2022 Australian and New Zealand control Conference

作者： Chen, Yuanyuan Lv, Yisheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

With the fast development of new technologies, such as Internet of Things, big data and Internet plus, Intelligent Transportation systems (ITS) have made remarkable achievements and the intelligence in ITS has also been continuously increased, which a new field, i.e., "Social Transportation", is emerging. In social transportation systems, physical and cyber elements are tightly conjoined, coordinated, and integrated with human and social characteristics. In this paper, we collect and analyze traffic data from physical world and social media data from cyberspace to sense the human mobility patterns during holidays under the COVID-19 pandemic.

关键词： COVID-19 Social networking (online) Pandemics Transportation Cyberspace Big Data Internet of Things

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Tumor lesion detection in patients with cervical cancer by indocyanine green near-infrared imaging

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EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2023年第4期50卷 1252-1261页

作者： Li, Pengfei Liu, Jiaqi He, Kunshan Gong, Shipeng Chi, Chongwei Liu, Pan Su, Guidong Li, Weili Duan, Hui Liu, Ping Tian, Jie Chen, Chunlin Southern Med Univ Nanfang Hosp Dept Obstet & Gynecol 1838 Guangzhou Ave Guangzhou 510515 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med & Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Big Data Based Precis Med Minist Ind & Informat Technol Beijing 100191 Peoples R China Chinese Acad Sci Inst Automation CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Purpose To investigate the feasibility and accuracy of near-infrared fluorescence (NIRF) imaging for detecting the extent of tumor invasion in cervical cancer using indocyanine green (ICG). Methods We enrolled 51 patients who were diagnosed with cervical cancer with FIGO stage IB1-IIA2 disease. Patients were administered indocyanine green (ICG) at a dose of 5 mg/kg 24 h prior to surgery. A customized near-infrared fluorescence (NIRF) imaging system was used to identify the extent of tumor invasion when radical hysterectomy specimens were harvested. The relationship between tumor fluorescence intensity and clinicopathological characteristics was analyzed. Results Of the 51 enrolled patients, 3 patients did not have residual tumors after cervical conization, and tumor lesions were identified by NIRF imaging in all the remaining 48 patients. The results of NIRF imaging were in agreement with the postoperative pathological findings in 95.8% of the patients with stromal invasion, 100% of those with surgical margin invasion, 100% of those with parametrial tumor involvement, and 100% of patients with uterine corpus invasion. The mean signal-to-background ratio (SBR) of the cervical tumors was 2.91 +/- 1.64, and the SBR was independent of clinicopathological characteristics. Fluorescence microscopy confirmed that ICG fluorescence was present in the tumor nests. Conclusions NIRF imaging enables objective, accurate, and safe identification of tumor invasion during cervical cancer surgery.

关键词： Cervical cancer Near-infrared fluorescence Indocyanine green Tumor invasion

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Efficient Calibration of Agent-Based Traffic Simulation Using Variational Auto-Encoder 25

Efficient Calibration of Agent-Based Traffic Simulation Usin...

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IEEE 25th International Conference on Intelligent Transportation systems (ITSC)

作者： Ye, Peijun Zhu, Fenghua Lv, Yisheng Wang, Xiao Chen, Yuanyuan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

In agent-based traffic simulation, calibration is an essential stage before the models applied to reproduce the individual/group travel behaviors. While traditional methods suffer from a high computational complexity, this paper proposes an improved method to alleviate the computational burden for large-scaled simulations. Specifically, we introduce variational auto-encoder to compress the original agent state vector into a lower dimensional hidden space, where the state transfer probability is calculated fast. Then the probability is mapped into the original space through a decoder, to achieve the agent travel parameters. The dynamic calibration method is tested with other baselines in urban travel demand analysis. Experiment results demonstrate that our method brings about 19% elevation of efficiency with the same accuracy of calibration.

关键词： Technological innovation Computational modeling Traffic control Probability Calibration Space exploration Decoding

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