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56th International Symposium on Robotics, ISR Europe 2023

作者： Kernbach, Andreas Hoffmann, Kathrin Sawodny, Oliver Eivazi, Shahram Institute of Industrial Manufacturing and Management IFF University of Stuttgart Department Cyber Cognitive Intelligence CCI Fraunhofer Institute for Manufacturing Engineering and Automation IPA Germany University of Stuttgart Institute for System Dynamics Germany Festo SE & Co. KG University of Tübingen Germany

ISBN: (纸本)9783800761418

Deep reinforcement learning (DRL) is an approach by which an agent can learn to solve complex sequential tasks in a try-and-error manner. As such, it has been widely explored in robotic domain for solving manipulation tasks. In this paper we benchmark DRL approaches utilizing pure image-based observations and simulation-based numerical ground truth (oracle) observations. The underlying benchmark task is inspired by an industrial application where a robotic arm is required to position up to nine boxes of random sizes into a container with variable pose. Central for the comparison is the robustness of the task execution and the duration of DRL training. We first simulate the task based on a real world logistic setup and parameters for a vision based reinforcement learning agent that operates in real time. Second, we compare DRL algorithms and observation types with policies that decide the desired end-effector pose of the to-be-placed box. Our results show the Proximal Policy Optimization (PPO) algorithm has the highest performance with satisfactory robustness and learning time. Moreover, we successfully perform the placing task by relying only upon image-based input in which the robot arm robustly places 8.8+0−0.261 of nine objects on average into the container, increasing the required learning time by 233 % compared to oracle observations. © VDE VERLAG GMBH Berlin Offenbach.

关键词： AI in Robotics Image-based Reinforcement learning

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Vision-Based Autonomous Landing of a Quadrotor UAV on a Double-Layered Nested Landing Marker

Vision-Based Autonomous Landing of a Quadrotor UAV on a Doub...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Mu, Lingxia Zhang, Yuan Xin, Jing Zhang, Youmin School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical and Industrial Engineering Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9789811966125

This paper focuses on the vision-based autonomous landing mission of a quadrotor unmanned aerial vehicle (UAV). A double-layered nested Aruco landing marker is designed which can adapt to the situation that the field of sight of airborne camera changes as the UAV’s altitude decreases during the landing. Then, considering that the landing marker might be obstructed in certain situation, traditional Aruco detection method cannot effectively recognize marker. A YOLOv4 deep learning algorithm is used for marker detection. In addition, to address the problem of false detection in some test results, normalized Wasserstein distance is used to replace the non-maximum suppression to deal with redundant frames. The UAV’s relative location to the marker is then calculated and landing mission is finally achieved by the image-based visual servoing strategy. The simulation on Gazebo demonstrates that the designed marker is effective for the UAV landing mission and the revised YOLOv4 detection algorithm can increase detection results accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking control

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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Improved Auction Algorithm for Weapon Target Assignment with Firepower-Health Model

Improved Auction Algorithm for Weapon Target Assignment with...

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第42届中国控制会议

作者： Shuobo Wang Yong Heng Yongzhao Hua Xiwang Dong Zhang Ren Institute of Artificial Intelligence Beihang University Beijing Institute of Electronic System Engineering School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University

This paper proposes an improved auction algorithm to optimize the efficiency of dynamic weapon target assignment(WTA) ***,the dynamic battlefield environment of firepower-health model is analyzed and the constraints of weapon assignment problem are *** firepower-health model is about the idea of adding constraints of weapon firepower and target health to promote the result of assignment more ***,in order to improve the computing speed of WTA algorithm,the penalty mechanism is introduced in the compensation auction *** main idea of improved algorithm is to reduce the extra consuming of the weapon during the assignment,from which penalty mechanism can optimize the assignment results and reduce the number of algorithm iterations by punishing the overkilled target under certain ***,the simulation results based on the dynamic firepower-health model show that the proposed algorithm can meet the requirements of firepower allocation,and can promote the effectiveness of assignment with high real-time response.

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MA-RRT* Algorithm for Path Planning with Maximum Rotation Angle Constraint and Adaptive Crossing Threat Zone

MA-RRT* Algorithm for Path Planning with Maximum Rotation An...

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第42届中国控制会议

作者： Jiong Luo Yong Heng Qi Li Yongzhao Hua Xiwang Dong Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Beijing Institute of Electronic System Engineering Institute of Artificial Intelligence Beihang University

With the gradual diversification of modern application scenes,the demand for UAV path planning is becoming more and more *** paper first introduces a possible battle scenario,and the problems that must be considered in path planning in this scene are analyzed:the maximum rotation angle constraint and the demand that the UAV can cross the threat zone *** rotation angle and Adaptive crossing threat zone RRT*(MA-RRT*) algorithm is proposed to solve the above *** algorithm improves the reconnection process of neighbor set in RRT*,and adds the maximum rotation angle constraint to the RRT* framework,so that the generated path strictly meets this *** addition,the above process also reduces the time consumption of RRT*,and the speed advantage of MA-RRT*becomes more and more obvious with the increase of iteration ***,the obstacle information is regarded as one of the components of the node cost of the path,and the original obstacle is regarded as a soft *** enables the path to adaptively cross the threat zone,which is more in line with the actual *** compare our algorithm with RRT* in numerical simulation,which verifies the effectiveness and time consumption advantages of the algorithm.

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Real-time Image Identification for Vehicle Positioning system

Real-time Image Identification for Vehicle Positioning Syste...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Chun-Yu Liu Ching-Huei Huang Rong-Terng Juang Department of Automatic Control Engineering Feng Chia University Taichung City Taiwan Institute of Aerospace and System Engineering Taipei Tech Taipei City Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

An innovative vehicle real-time positioning method was developed to maximize the use of existing visible light camera systems on vehicles, especially in areas without Global Positioning system (GPS) signals or blind spots. This approach eliminates the need for additional positioning devices and leverages the potential of existing vehicle driver assistance systems. As vehicles are equipped with cameras for monitoring the surrounding environment, we optimized the use of these existing resources for an efficient and accurate innovation in vehicle positioning. Firstly, we used cameras in four directions (the front, rear, left, and right) of the vehicle to capture images. By integrating images in four directions and using a transformation algorithm, a complete top-down view was formed with geographic information to enhance positioning accuracy. The integrated top-down view image is useful for obtaining information on surrounding environments and identifying the vehicle's real-time location in parking lots. The developed method serves as a foundation for future applications in automated driving planning and positioning.

关键词： Location awareness Technological innovation Accuracy Wheels Cameras Real-time systems Planning

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Dynamic Performance Prediction in Batch-Based Assembly system with Bernoulli Machines and Changeovers

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Complex system Modeling and Simulation 2022年第3期2卷 224-237页

作者： Zunjun Wang Zhiyang Jia Xiuxuan Tian Jingchuan Chen Bei Pan State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Industrial and Manufacturing Systems Engineering University of Hong KongHong Kong 999077China School of Engineering Tokyo University of TechnologyTokyo 192-0982Japan

Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.

关键词： flexible assembly system dynamic performance Bernoulli machines finite buffers changeovers

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EFEAR-4D: Ego-Velocity Filtering for Efficient and Accurate 4D radar Odometry

arXiv

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arXiv 2024年

作者： Wu, Xiaoyi Chen, Yushuai Li, Zhan Hong, Ziyang Hu, Liang The Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

Odometry is a crucial component for successfully implementing autonomous navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these sensors may encounter challenges in extreme weather conditions, such as snowfall and fog. The emergence of FMCW radar technology offers the potential for robust perception in adverse conditions. As the latest generation of FWCW radars, the 4D mmWave radar provides point cloud with range, azimuth, elevation, and Doppler velocity information, despite inherent sparsity and noises in the point cloud. In this paper, we propose EFEAR-4D, an accurate, highly efficient, and learning-free method for large-scale 4D radar odometry estimation. EFEAR-4D exploits Doppler velocity information delicately for robust ego-velocity estimation, resulting in a highly accurate prior guess. EFEAR-4D maintains robustness against point-cloud sparsity and noises across diverse environments through dynamic object removal and effective region-wise feature extraction. Extensive experiments on two publicly available 4D radar datasets demonstrate state-of-the-art reliability and localization accuracy of EFEAR-4D under various conditions. Furthermore, we have collected a dataset following the same route but varying installation heights of the 4D radar, emphasizing the significant impact of radar height on point cloud quality - a crucial consideration for real-world deployments. Our algorithm and dataset will be available soon at https://***/CLASS-Lab/EFEAR-4D. © 2024, CC BY-NC-SA.

关键词： Millimeter waves

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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