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arXiv 2024年

作者： Xie, Junjun Hu, Liang Qin, Jiahu Yang, Jun Gao, Huijun The Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China The Department of Automation University of Science and Technology of China Hefei230085 China The Department of Aeronautical and Automotive Engineering Loughborough University LeicestershireLE11 3TU United Kingdom The Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

The control barrier function (CBF) has become a fundamental tool in safety-critical systems design since its invention. Typically, the quadratic optimization framework is employed to accommodate CBFs, control Lyapunov functions (CLFs), other constraints and nominal control design. However, the constrained optimization framework involves hyper-parameters to tradeoff different objectives and constraints, which, if not well-tuned beforehand, impact system performance and even lead to infeasibility. In this paper, we propose a hierarchical optimization framework that decomposes the multi-objective optimization problem into nested optimization sub-problems in a safety-first approach. The new framework addresses potential infeasibility on the premise of ensuring safety and performance as much as possible and applies easily in multi-certificate cases. With vivid visualization aids, we systematically analyze the advantages of our proposed method over existing QP-based ones in terms of safety, feasibility and convergence rates. Moreover, two numerical examples are provided that verify our analysis and show the superiority of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Designing a Secure Smart Remote Patient Monitoring and Warning System using Big Data and Internet of Things Ecosystems 10

Designing a Secure Smart Remote Patient Monitoring and Warni...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ramesh, S.P. Abdulwahid, Ali Hadi Anjum, Afsana Venkatesh, N. Singh, Rajesh Chakravarthi, M. Kalyan Amet University Department of Computer Science and Engineering Tamil Nadu Kanathur India Southern Technical University Engineering Technical College Department of Control and Automation Basra Iraq Jazan University Department of Information Technology and Security Jazan Saudi Arabia School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9789380544472

The demand on healthcare is growing as a result of the expanding global population, rising demands for successful treatment, and an underlying improvement in life quality. Consequently, healthcare remains one of the most significant social and economic problems in the world, necessitating the development of new and more sophisticated scientific and technological treatments.E-health services are provided via personalised healthcare systems to meet the medical and support needs of the ageing population. An important development in the Big Data age is the Internet of Things, which facilitates several real-time engineering solutions through improved services. Healthcare systems are now using analytics over IoT data streams as a stream of user data to find new information, forecast rapid recognition, and make decisions on life-threatening situations. In this study, researchers have carried out a thorough review of the hottest recent revolutionary technologies in bespoke healthcare systems, with a focus on hypervisor, cloud infrastructure, big data analytic, the Web of Things, and smartphone apps. In order to make early disease detection and diagnosis possible, it is important to develop a better healthcare system. We have examined these problems and suggested potential solutions while offering secure e-health services. The purpose of this study is to design a smart and secure remote monitoring and warning system using the latest emerging technologies in 21stcentury such as big data analytics and Internet of Things (IoT) ecosystems. In this study the researchers discussed a detailed research methodology to meet the outcomes of the reerach. All the relevant findings and analysis are explained in descriptive manner. Moreover, this study highlights the constantly expanding requirements for better medical systems in the present and offers potential directions for future research. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Enhancing Human-Drone Interaction with Human-Meaningful Visual Feedback and Shared-control Strategies

Enhancing Human-Drone Interaction with Human-Meaningful Visu...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Riccardo Franceschini Matteo Fumagalli Julian Cayero Becerra Eurecat Centre Tecnològic de Catalunya Robotics and Automation Unit Barcelona Spain Department of Electrical Engineering Automation and Control Group Danish Technical University Lyngby Denmark

Recent developments in the capabilities of unmanned aerial vehicles (UAVs) have made them suitable for use in various industrial settings. Their ability to access difficult and remote locations, as well as providing remote manipulation and visual inspection capabilities, make them valuable for various industrial applications. However, operating UAVs can be challenging, particularly in cluttered environments. This research aims to enhance the teleoperation experience by providing human-meaningful information on the remote user interface, thereby improving the operator’s situational awareness. Shared autonomy routines utilizing the previously collected information are also developed to further assist the operator with challenging control tasks. The proposed system has been tested in simulated environments and on actual hardware.

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Aggressive Trajectory Generation for a Swarm of Autonomous Racing Drones

Aggressive Trajectory Generation for a Swarm of Autonomous R...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yuyang Shen Jin Zhou Danzhe Xu Fangguo Zhao Jinming Xu Jiming Chen Shuo Li College of Control Science and Engineering Zhejiang University Hangzhou China Department of Automation Zhejiang University of Technology Hangzhou China School of Automation Northwestern Polytechnical University Xi'an China

Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track in a $35m\times 35m$ space with a top speed of 14m/s. Flight tests are performed on two quadrotors passing through 3 waypoints in a $4m\times 2m$ flight arena to demonstrate the feasibility of the proposed method in the real world. Both simulations and real-world flight tests show that the proposed method can generate the optimal aggressive trajectories for a swarm of autonomous racing drones. The method can also be easily transferred to other types of robot swarms.

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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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Developing a Window control Algorithm Based on Reinforcement Learning for Indoor PM2.5 Mitigation 5th

Developing a Window Control Algorithm Based on Reinforcement...

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5th International Conference on Building Energy and Environment, COBEE 2022

作者： An, Yuting Xia, Tongling You, Ruoyu Lai, Dayi Liu, Junjie Chen, Chun Department of Mechanical and Automation Engineering The Chinese University of Hong Kong China Department of Building Services Engineering The Hong Kong Polytechnic University China School of Design Shanghai Jiao Tong University Shanghai China Tianjin Key Lab of Indoor Air Environmental Quality Control School of Environmental Science and Engineering Tianjin University Tianjin China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9789811998218

Smart control of window is a means of effectively reducing concentrations of indoor PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) in naturally ventilated residential buildings without air cleaning devices. This study aimed to develop a reinforcement learning (RL) approach to automatically control window behavior in real time for mitigation of indoor PM2.5 pollution. The method trains the window controller using deep Q-network (DQN) in a specific apartment for a month. The trained controller can be employed to control window behavior to mitigate indoor PM2.5 in that apartment. The input data for the controller are the real-time indoor and outdoor PM2.5 concentrations with 1-min resolution. Simulation was conducted in a real apartment in Tianjin. The results show that, the RL algorithm reduced the average indoor PM2.5 concentration by 9.11% when compared with the I/O ratio algorithm and by 7.40% when compared with real window behavior. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Path Smoothing via Policy Gradient Parameter Optimization with Utilization to Quadrotor Tracking

Path Smoothing via Policy Gradient Parameter Optimization wi...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Renkai Yi Xiuhui Peng Yuanbo Xie Qishao Wang College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Department of Dynamics and Control Beihang University Beijing China

This paper proposes a novel global path smoothing approach based on policy gradient parameter optimization. Firstly, an algorithm is introduced to sample path points suitable for computer processing on grid maps, resulting in a discrete set of sampled points defining the path. Secondly, a path smoothing strategy is designed by resorting to differential iteration. The smoothing parameters are optimized and trained with a policy gradient which allows the coordinates of each path point to converge to a state where a smooth optimized trajectory can be generated. Finally, the simulation and experimental results indicate the smooth output of continuous achievable paths can be obtained on the basis of discrete paths of rasterized maps for the quadrotor UAV.

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Autonomy for Ferries and Harbour Buses: a Collision Avoidance Perspective

arXiv

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arXiv 2023年

作者： Enevoldsen, Thomas T. Blanke, Mogens Galeazzi, Roberto Automation and Control Group Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. LyngbyDK 2800 Denmark

This paper provides a collision avoidance perspective to maritime autonomy, in the shift towards Maritime Autonomous Surface Ships (MASS). In particular, the paper presents the developments related to the Greenhopper, Denmark’s first autonomous harbour bus. The collision and grounding avoidance scheme, called the Short Horizon Planner (SHP), is described and discussed in detail. Furthermore, the required autonomy stack for facilitating safe and rule-compliant collision avoidance is presented. The inherent difficulties related to adhering to the COLREGs are outlined, highlighting some of the operational constraints and challenges within the space of autonomous ferries and harbour buses. Finally, collision and grounding avoidance is demonstrated using a simulation of the whole Greenhopper autonomy stack. © 2023, CC BY-NC-ND.

关键词： Collision avoidance

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Fixed-time extended state observer-based trajectory tracking control for autonomous underwater vehicles

Fixed-time extended state observer-based trajectory tracking...

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作者： Zheng, Jiaqi Song, Lei Liu, Lingya Yu, Wenbin Zhu, Shanying Wang, Yiyin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

In this paper, we investigate the trajectory tracking control problem for autonomous underwater vehicles (AUVs) with time-varying external disturbances of currents and waves in the sea. To solve the problem, a fixed-time extended state observer (FTESO)-based tracking controller is proposed. Firstly, by using the extended state observer, the FTESO is proposed for estimating the external disturbances and the velocities of the AUV within the fixed convergence time. Based on the FTESO, the fixed-time controller is proposed for reducing the tracking errors and improving robustness performance. Theoretical analysis is given, which shows that the unknown external disturbances can be compensated and tracking error can converge to zero within the fixed time. Simulation results show that the proposed FTESO controller not only guarantees the fixed-time convergence but also reduces the trajectory tracking errors of the AUV. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Autonomous underwater vehicles

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ChatSTL: A Framework of Translation from Natural Language to Signal Temporal Logic Specifications for Autonomous Vehicle Navigation out of Blocked Scenarios

ChatSTL: A Framework of Translation from Natural Language to...

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International Conference on Computer and automation engineering, ICCAE

作者： Yujin Wang Zhaoyan Huang Shiying Dong Hongqing Chu Xiang Yin Bingzhao Gao School of Automotive Studies Tongji University Shanghai China Department of Control Science and Engineering Jilin University Changchun China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

This paper addresses the quandary that autonomous vehicles may encounter in blocked scenarios, and proposes a novel framework of translation from natural language to Signal Temporal Logic specifications based on Large Language Model, which helps autonomous vehicles navigate out of blocked scenarios through path planning. We utilize the profound capability of reasoning and planning of GPT-3.5, design a set of predicate functions and formulate the prompts in order to generate accurate Signal Temporal Logic specifications describing driving tasks. The feasibility of the proposed framework is demonstrated via a numerical experiment.

关键词： Navigation Computational modeling Natural languages Path planning Cognition Planning Numerical models

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