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Direct method of finding first integral of two-dimensional autonomous systems in polar coordinates

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Chinese Physics B 2006年第5期15卷 895-898页

作者：楼智美汪文珑 Department of Physics Shaoxing College of Arts and Sciences Shaoxing 312000 China Department of Mathematics Shaoxing College of Arts and Sciences Shaoxing 312000 China

A direct method to find the first integral for two-dimensional autonomous system in polar coordinates is suggested. It is shown that if the equation of motion expressed by differential 1-forms for a given autonomous Hamiltonian system is multiplied by a set of multiplicative functions, then the general expression of the first integral can be obtained, An example is given to illustrate the application of the results.

关键词： direct method autonomous systems first integral polar coordinates

Sampled-data Observer Design for Nonlinear autonomous systems

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Sampled-data Observer Design for Nonlinear Autonomous System...

A Practical Study of Intelligent Image-Based Mobile Robot for Tracking Colored Objects

2009中国控制与决策会议

作者： JIN Huiyu~1,YIN Baoqun~1,LING Qiang~1,KANG Yu~1 1.Department of Automation,University of Science and Technology of China,Hefei,230027

＜正＞Sampled-data observers are designed for nonlinear autonomous *** design is decomposed into two ***,a feedback term,which measures the state estimation error,is designed based on the Euler approximations of the ***,a more accurate system model is used to estimate state with the obtained feedback *** conditions are provided to guarantee the proposed observers are semi-globally practically *** with earlier results,these conditions relax the assumption in the literature that a globally Lipschitz Lyapunov function is given.

关键词： sampled-data observer nonlinear autonomous systems Euler approximation Lyapunov function

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Computers, Materials & Continua 2024年第8期80卷 2181-2197页

作者： Mofadal Alymani Mohamed Esmail Karar Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Faculty of Electronic Engineering Menoufia UniversityMenouf32952Egypt Faculty of Engineering Zagazig UniversityZagazig44519Egypt

Object tracking is one of the major tasks for mobile robots in many real-world ***,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot *** contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental ***,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance *** design of developed controllers implies some motion rules to mimic the knowledge of experienced *** scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot *** PID control failed to handle some tracking scenarios in this *** proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,*** promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.

关键词： Mobile robot autonomous systems fuzzy logic control real-time image processing

autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

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Computers, Materials & Continua 2022年第10期73卷 899-915页

作者： Ashit Kumar Dutta Yasser Albagory Abdul Rahaman Wahab Sait Ismail Mohamed Keshta Department of Computer Science and Information systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,*** the interesting applications of autonomous systems,their applicability in agricultural sector becomes *** unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop *** spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the *** recently developed deep learning(DL)models have exhibited effective performance in several data classification *** this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)*** proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target ***,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing ***,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of *** the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is *** design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

关键词： autonomous systems object detection precision agriculture unmanned aerial vehicles deep learning parameter tuning

A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

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能源与人工智能(英文) 2022年第2期8卷 177-212页

作者： Daniel Mitchell Jamie Blanche Sam Harper Theodore Lim Ranjeetkumar Gupta Osama Zaki Wenshuo Tang Valentin Robu Simon Watson David Flynn Smart systems Group Institute of SensorsSignals and SystemsSchool of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghEH144ASUnited Kingdom Centre for Mathematics and Computer Science Intelligent and Autonomous Systems GroupCWIAmsterdam1098 XG Netherlands Algorithms Group Faculty of Electrical EngineeringMathematics and Computer Science(EEMCS)Delft University of Technology(TU Delft)Delft2628 XE Netherlands The University of Manchester Department of Electrical and Electronic EngineeringOxford RoadManchesterM139PL United Kingdom James Watt School of Engineering University of GlasgowG128QQUnited Kingdom

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

关键词： Artificial intelligence autonomous systems Digitalization Offshore renewable energy Offshore wind farms Robotics

WARA-PS:a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

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autonomous Intelligent systems 2021年第1期1卷 104-134页

作者： Olov Andersson Patrick Doherty Mårten Lager Jens-Olof Lindh Linnea Persson Elin A.Topp Jesper Tordenlid Bo Wahlberg Department of Computer and Information Science Linköping UniversityLinköpingSweden Saab Kockums MalmöSweden Division of Decision and Control systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Department of Computer Science Lund UniversityLundSweden Saab Combitech AB LinköpingSweden Division of Automatic Control Department of Electrical EngineeringLinköping UniversityLinköpingSweden

A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is *** objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal *** enabler is a computational infrastructure with a core system architecture for industrial and academic *** includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial *** motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also *** first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface *** second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine *** research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test *** would be most difficult to do experiments on this large scale without the WARA-PS research ***,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.

关键词： autonomous systems Intelligent system architectures Research demonstration arena autonomous drones autonomous marine vessels Public safety and security Collaborative robotics

Monocular depth estimation based on deep learning: An overview

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Science China(Technological Sciences) 2020年第9期63卷 1612-1627页

作者： ZHAO ChaoQiang SUN QiYu ZHANG ChongZhen TANG Yang QIAN Feng Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints. Meanwhile, the predicted depth maps are sparse. Inferring depth information from a single image(monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. Meanwhile, dense depth maps are estimated from single images by deep neural networks in an end-to-end manner. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed subsequently. Therefore, we survey the current monocular depth estimation methods based on deep learning in this review. Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. Finally, we discuss the challenges and provide some ideas for future researches in monocular depth estimation.

关键词： autonomous systems monocular depth estimation deep learning unsupervised learning

Update Chain-based Approach for Checking Route Oscillation of BGP

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Chinese Journal of Aeronautics 2011年第2期24卷 202-209页

作者： ZHANG Jun School of Electronics and Information Engineering Beihang University Beijing 100191 China National Key Laboratory of CNS/ATM Beijing 100191 China

This paper presents a novel approach for checking route oscillation of border gateway protocol（BGP） quickly,by which the privacy of routing policies of autonomous system（AS） can be ***,route update chain tag（RUCT） is constructed to track the forwarding of update report,and local routing library is made to record the changing history of update *** route oscillation can be identified by analyzing correlative state of RUCT and local routing *** characteristic of this approach is that an arbitrary AS can check route oscillation alone only by sharing its network ID,which greatly respects the pri-vacy of routing policies for each *** paper shows that both looping in RUCT and consecutive positive-negative report in local record are sufficient conditions for route *** experiments demonstrate the availability and efficiency of the proposed approach.

关键词： routing protocols gateways border gateway protocol autonomous systems interdomain routing oscillations

Machine-to-Machine Collaboration Utilizing Internet of Things and Machine Learning

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Advances in Internet of Things 2023年第4期13卷 144-169页

作者： Mohammed Misbahuddin Abul Kashem Mohammed Azad Veysel Demir College College of Engineering and Engineering Technology Northern Illinois University DeKalb USA

Machine-to-Machine (M2M) collaboration opens new opportunities where systems can collaborate without any human intervention and solve engineering problems efficiently and effectively. M2M is widely used for various application areas. Through this reported project authors developed a M2M system where a drone and two ground vehicles collaborate through a base station to implement a system that can be utilized for an indoor search and rescue operation. The model training for drone flight paths achieves almost 100% accuracy. It was also observed that the accuracy of the model increased with more training samples. Both the drone flight path and ground vehicle navigation are controlled from the base station. Machine learning is utilized for modelling of drone’s flight path as well as for ground vehicle navigation through obstacles. The developed system was implemented on a field trial within a corridor of a building, and it was demonstrated successfully.

关键词： Search and Rescue Image Processing Navigation systems autonomous systems and Object Detection