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Neural Network Vehicle Modelling for Underwater Vehicles

Neural Network Vehicle Modelling for Underwater Vehicles

引用

OCEANS conference

作者： Rozenbroek, Tijs Rojer, Jim Steenstra, Lukas Hildmann, Hanno Netherlands Org Appl Sci Res TNO Intelligent Autonomous Syst IAS The Hague Netherlands Netherlands Org Appl Sci Res TNO Heat Transfer & Fluid Dynam HTFD The Hague Netherlands Netherlands Org Appl Sci Res TNO Acoust & Sonar AS The Hague Netherlands

ISBN: (纸本)9798350332261

Underwater assets such as wind turbine foundations and other infrastructure require regular inspections. These inspections are currently performed by extensively trained divers or pilots of remotely operated underwater vehicles. Compact unmanned underwater vehicles could perform many of these tasks, and myriad others, with reduced risk and cost. However, navigation and position estimation is difficult under water, especially due to the absence of satellite navigation signals. Therefore, the fusion of information from sensors into a single position estimate is a common approach. These approaches often suffer from drift in the position estimate, which can accumulate during sub-surface operation. Furthermore, critical sensors can be unavailable in certain segments of the operation. In addition, information from the vehicle's actuators is often disregarded, while these signals could improve position estimation. In this work, the focus is therefore on the inclusion of actuator signals for modelling vehicle behaviour. In particular, the use of neural networks for modelling the vehicle dynamics is explored in a machine learning approach, using real-world data. It is shown that these neural network models are able to capture the relevant dynamics in these data. The feedforward neural network severely limited the effects of position drift when used as plant model in a Kalman filter together with IMU measurements. The presented approach is therefore a promising method for mitigating the error due to unavailability of critical sensors in a real-world setting.

关键词： Neural networks Vehicle Dynamics PNT Underwater Navigation sensor Fusion

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Integrating UAV and LoRaWAN in WSN for Intelligent Monitoring in Large-scale Rural Farms

Integrating UAV and LoRaWAN in WSN for Intelligent Monitorin...

引用

2023 IEEE International conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Tao, Xu Silvestri, Simone University of Kentucky Department of Computer Science United States

ISBN: (纸本)9781665453813

With the growth of challenges to crop productivity caused by climate changes, high-performance, and sustainable farming techniques are essential to satisfy the growing worldwide food demands. However, today's farming modes still extensively rely on farmers for monitoring crop growth status and making decisions on management strategies, leaving it far behind the desired intelligence and automaticity. This paper proposes a decentralized architecture to enable energy-efficient solutions for intelligent crop monitoring in large-scale rural farms. We employ unmanned aerial vehicle (UAV) as data mules for data collection with data inference techniques in Wireless sensor Network (WSN). We then facilitate Bluetooth Low Energy (BLE) and LoRaWAN technologies to provide hierarchical edge-fog-cloud network infrastructure, enabling seamless communications among ground sensors, UAVs, fog-layer computational devices, and cloud servers. © 2023 IEEE.

关键词： Wireless sensor networks

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E3-Net: Event-Guided Edge-Enhancement Network for UAV-based Crack Detection

E3-Net: Event-Guided Edge-Enhancement Network for UAV-based ...

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International conference on Advanced Robotics and Mechatronics (ICARM)

作者： Ran Duan Bo Wu Hao Zhou Haobo Zuo Zhengshu He Chenxi Xiao Changhong Fu Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong China Department of Computer Science University of Hong Kong Hong Kong China MOE Key Laboratory of Transportation Tunnel Engineering Southwest Jiaotong University Chengdu China School of Information Science and Technology ShanghaiTech University Shanghai China School of Mechanical Engineering Tongji University Shanghai China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Buildings, bridges, and other exteriors of infrastructure are commonly susceptible to damages that may reduce safety and longevity. When left unattended, small cracks can cause catastrophic failures. However, crack inspection is a challenging process that requires significant human effort and can expose inspectors to risks. To address this challenge, the use of unmanned Aerial Vehicles (UAVs) has emerged as an approach to survey areas that are difficult to reach. However, the cracks are small and may blend into background textures, making them difficult to discriminate. Furthermore, the motion of quadrotors can potentially cause motion blur, increasing the difficulty of crack detection. To address these challenges, we propose a neural network designed to detect cracks robustly. Our neural network architecture includes two stages. First, a V2E (Video-to-Event) model transforms the image data into Dynamic Vision sensor (DVS) events. This procedure aims to make the output crack feature identifiable in the presence of motion blur. Then, an edge-enhancement net that combines UNet and non-local block net is designed to augment the crack feature and suppress the background texture concurrently. The background texture suppression is achieved by training on our annotated crack dataset using a triplet loss. Lastly, these proposed cracks can be highlighted by the YOLOv8n-seg network. Experimental results demonstrate that the proposed network can effectively detect small wall cracks in the presence of diverse background patterns.

关键词： Training Surveys Image edge detection Neural networks Transforms Inspection Vision sensors Safety Voltage control Vehicle dynamics

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Hybrid Wireless sensor networks: A Prototype 17th

Hybrid Wireless Sensor Networks: A Prototype

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17th IFIP TC13 International conference on Human-Computer Interaction (INTERACT)

作者： Khalifeh, Ala Bartolini, Novella Silvestri, Simone Bongiovanni, Giancarlo Al-Assaf, Anwar Alwardat, Radi Alhaj-Ali, Samer German Jordanina Univ Amman Jordan Sapienza Univ Rome Rome Italy Univ Kentucky Lexington KY USA Arab Robot & AI Org Amman Jordan King Abdullah II Design & Dev Bur KADDB Amman Jordan

ISBN: (纸本)9783030293901;9783030293895

Wireless sensor networks (WSNs) are widely used to assist monitoring of an area of interest whenever manual monitoring by skilled personnel is not convenient if not prohibitive. In this paper, we propose a step ahead with respect to the current status of art, with the design and implementation of a novel networked architecture which integrates a set of static ground nodes, an unmanned Ground Vehicle (UGV), and an unmanned Aerial Vehicle (UAV) in a unique monitoring system. All these devices are equipped with sensors, a microcontroller and a wireless communication module such that they are capable of covering an AoI in the terrestrial and aerial dimension. In the proposed architecture, the mobile robot can inspect areas which have no fixed ground sensors, whereas sensors installed on the UAV cover the ground areas from above, ensuring wider coverage. Once data is collected by the ground sensors, it is sent via the UAV to a remote processing center, which elaborates the gathered data for decision making.

关键词： Wireless sensor networks (WSN) unmanned Ground Vehicle (UGV) unmanned Aerial Vehicle (UAV) Remote monitoring Decision making

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Study of UGS Target Recognition Method based on Recurrent Neural Network and Peak Value Region Extraction

Study of UGS Target Recognition Method based on Recurrent Ne...

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IEEE International conference on unmanned Systems (ICUS)

作者： Wei Zhao Xiong Li Nan Wang Tonghua Xu Department of Information Technology Hunan Police Academy National University of Defense Technology Changsha China College of Intelligence Science and Technology National University of Defense Technology Changsha China

In order to improve the accuracy of unattended Ground sensors (UGS) for human activity recognition, this paper proposes a target recognition method based on Recurrent Neural Network (RNN) and peak value region extraction. The ground seismic signal is obtained from the seismic sensor, and the peak value region of the signal is extracted and trained by Recurrent Neural Network (RNN) to form the target recognition network model. Simulation results show that the proposed approach has an obvious improvement in the accuracy of human target recognition compared with the existing seismic target recognition methods.

关键词： Training Feature extraction sensors Data mining Target recognition Recurrent neural networks Legged locomotion

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Trajectory influence on data collection in Wireless sensor networks using an unmanned Aerial Vehicle 10

Trajectory influence on data collection in Wireless Sensor N...

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10th International conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Vladuta, Valentin-Alexandru Ghimes, Ana-Maria Grumazescu, Constantin Patriciu, Victor-Valeriu Mil Tech Acad Comp Sci Dept Bucharest Romania

ISBN: (纸本)9781538649015

Data collection is one of the most important parts in the communication process of wireless sensors. Without a proper mechanism to gather data, any further action is inefficient. Data aggregation, processing and decision making are only possible after data is collected. For this reason, this paper studies the impact of trajectories on data collection in an environment where a mobile unmanned aerial vehicle is deployed to gather information from ground sensors. Different mobility patterns have been taken into consideration to observe how collection efficiency is affected. Simulations were deployed in Network Simulator 2, version 2.34 and results are revealed in Section iv.

关键词： wireless sensor network unmanned aerial vehicle data collection trajectory

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Trajectory influence on data collection in Wireless sensor networks using an unmanned Aerial Vehicle

Trajectory influence on data collection in Wireless Sensor N...

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International conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Valentin-Alexandru Vladuta Ana-Maria Ghimes Constantin Grumazescu Victor-Valeriu Patriciu Computer Science Department Military Technical Academy Bucharest Romania

ISBN: (纸本)9781538649022;9781538649015

关键词： sensors Trajectory Data collection unmanned aerial vehicles Wireless sensor networks Protocols IEEE 802.11 Standard

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unmanned/unattended sensors and sensor networks XII 12

Unmanned/Unattended sensors and sensor networks XII

引用

unmanned/unattended sensors and sensor networks XII

作者： Carapezza, Edward M. Datskos, Panos G. Tsamis, Christos

This PDF file contains the front matter associated with SPIE Proceedings Volume 9986 including the Title Page, Copyright information, Table of Contents, Introduction, and conference Committee listing. © 2016 SPIE.

ISBN: (纸本)9781510603769

关键词： sensor networks

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Power for sensors - sensors for power 12

Power for sensors - Sensors for power

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conference on unmanned/unattended sensors and sensor networks XII

作者： Siegel, Mel Carnegie Mellon Univ Sch Comp Sci Inst Robot Pittsburgh PA 15213 USA

ISBN: (数字)9781510603776 s

ISBN: (纸本)9781510603769;9781510603776

As sensors are increasingly deployed in locations removed from mains power and increasingly expected to operate for times that are long compared to battery lifetimes we look to means for "harvesting" or "scavenging" energy from the sensors' operating environments. Whereas many sensors are "parametric" - their interaction with the environment causes a change in one or more of their electrical parameters - many other are true transducers - they perform their sensing function by extracting energy from their environment. These kinds of sensors can thus serve - under suitable operating conditions - both as measuring devices and as power supplies. In this paper we review this background, review the fundamental restrictions on our ability to extract energy from the environment, enumerate and summarize sensing principles that are promising candidates to double as power supplies, and provide several examples that span the range from already off-the-shelf at low cost to in laboratory prototype stage to sufficiently speculative that there might be reasonable doubt regarding whether they can actually work even in principle. Possibilities examined across this spectrum include thermal noise, ambient RF scavenging (briefly), thermoelectricity, piezoelectricity, pyroelectricity, and electrochemistry, especially including electrochemistry facilitated by microorganisms.

关键词： sensing sensors power energy transduction harvesting scavenging noise thermodynamics

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Data Fusion for Target Tracking and Classification with Wireless sensor Network 12

Data Fusion for Target Tracking and Classification with Wire...

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conference on unmanned/unattended sensors and sensor networks XII

作者： Pannetier, Benjamin Doumerc, Robin Moras, Julian Dezert, Jean Canevet, Loic Off Natl Etud & Rech Aerosp French Aerosp Lab Informat Proc Dept F-91761 Palaiseau France Rocade Est Echangeur Guerry DGA Tech Terrestres F-18021 Bourges France

ISBN: (数字)9781510603776 s

ISBN: (纸本)9781510603769;9781510603776

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

关键词： unattended wireless sensor network ground target tracking track classification map information classification fusion

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