AGV RAD: AGV positioning system for ports using microwave doppler radar

Automation and intelligence have become an inevitable trend in the development of container terminals. The AGV (Automated Guided Vehicle) positioning is a primary problem to build the automated ports. Although the existing Ultra-High Frequency(UHF) RFID technology has good measurement accuracy and stability in the port AGV positioning, the exposed magnetic tags are easy to damage under the common heavy load, and its construction and maintenance cost is unbearable to most ports. Among the candidate technologies for the AGV positioning, microwave Doppler radar has a strong penetrating ability, and can work well in a complex environment (day and night, foggy, rainy). Therefore, the microwave Doppler radar-based AGV positioning system has attracted a lot of attention. In this thesis, a test system using the above technique was established, together with a NI myRIO real-time Wi-Fi compatible computation platform. Several computation algorithms were implemented to extract the accurate values of range and velocity. Wavelet denoising with the adapted threshold function was considered to filter noise contained in radar signals. In the frequency domain analysis, FFT and Chirp-Z Transform (CZT) joint algorithm was proposed to suppress the influence of fence effects and also improves real-time performance. In addition, 2D-FFT is used to calculate velocity of AGV. According to the port-like environment, the suitable AGV positioning algorithm and communication method based on microwave Doppler radars and NI myRIO-1900s also be proposed. The effectiveness of the proposed system was experimentally tested and several results are included in this thesis.Automação e inteligência artifical tornaram-se uma tendência inevitável no desenvolvimento dos terminais dos contentores. O posicionamento do VAG (Veículo Autónomo Guiado) é um dos problemas principais para construir as portas automatizadas. Embora a tecnologia RFID de frequência ultra-alta (UHF) existente tenha uma boa precisão e estabilidade de medição no posicionamento VAG dos portos, as etiquetas magnéticas expostas são fáceis de danificar sob a comum carga pesada e o seu habitual custo de construção e manutenção é insuportável para a maioria das portos. Entre as tecnologias para o posicionamento VAG, o radar Doppler de microondas possui uma forte capacidade de penetração e pode funcionar bem em ambientes complexos (dia, noite, nevoeiro e chuva). Portanto, o sistema de posicionamento VAG baseado em radar Doppler de microondas atraiu muita atenção. Nesta tese, foi estabelecido um sistema de teste usando a técnica acima mencionada, juntamente com uma plataforma de computação em tempo real, NI myRIO compatível com Wi-Fi. Vários algoritmos de computação foram envolvidos para extrair os valores precisos de distancia e velocidade. O “denoising” de wavelets com a função de limiar adaptado foi utilizado para filtrar o ruído nos sinais de radar. Na análise do domínio da frequência, o algoritmo conjunto FFT e Chirp-Z Transform (CZT) foi proposto para suprimir a influência dos efeitos de resolução e também melhorar o desempenho em tempo real. Além disso, o algoritmo 2D-FFT é usado para calcular a velocidade do VAG. De acordo com o ambiente dos portos, o algoritmo de posicionamento VAG e o método de comunicação adequado baseados em radares Doppler de microondas e NI myRIO-1900s também serão propostos. A eficiência do sistema proposto foi testada experimentalmente e vários resultados estão descritos nesta dissertação

Autonomous mobile robot using myRIO

Current research show that autonomous mobile robot is being widely researched to reduce human work and ease the job. In the research, mobile robot very useful for human because hazards and human limitations have the main reason for the need for more versatile and powerful mobile robots, especially in machining industry and military bases. Besides that, stable design for mobile rohot can easy to move in any environmentaL Moreover, the navigation and obstacle the object also famous used in mobile robot. In this project, the research study is conducted to develop an autonomous mobile robot algorithm with implementation of dynamic modelling using Newton-Euler approach. The rigid body of the mobile robot is equipped with two wheels and a castor for the purpose of simple control and stable balancing. In addition, Kinect motion sensor will be applied for the image detection and video recording while ultrasonic sensor obstacle avoidance of the mobile robot. On the other hand, the simulation of the control mechanism is realized through Lab VIEW software package where the development of the mobile robot environment is carried out and transferred to National Instrument myRIO hardware. Results found that the autonomous mobile robot successfully obstacle and record video while using both sensors. It is believed that this mobile robot can used in hazards environment to record all the important data that need those environment

Statistical analysis of range of motion and surface electromyography data for a knee rehabilitation device

This work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that real-time signals and related statistical data on the knee’s performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users

Mobile Robotics

The book is a collection of ten scholarly articles and reports of experiences and perceptions concerning pedagogical practices with mobile robotics.“This work is funded by CIEd – Research Centre on Education, project UID/CED/01661/2019, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT.

The design and safety analysis of an accessible low-cost differential drive robot under LIDAR-Based obstacle avoidance

A low-cost mobile robot, OpenDani, is re-designed and built to serve as a research platform capable of autonomous tasks. A self-contained, open-source design is supported by the single-board computer mounted atop the robot. Low-cost sensors are integrated into the robot communication network to achieve online obstacle avoidance (OA). The mobile system is compared to its previous, non-autonomous design to highlight an elevated level of autonomy. An experimental analysis is performed to define a relationship between components of the OpenDani design and the safe operation of the vehicle under an obstacle avoidance task.Mechanical Engineerin

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)

Development of an Intelligent Robotic Rein for Haptic Interaction with Mobile Machines

The haptic sense is often placed secondary when compared to vision and hearing in allowing navigation and autonomy. However, in certain conditions such as low or no visibility, the haptic senses can play a crucial role in aiding navigation and obtaining spatial knowledge of the environment and object properties. For example, search and rescue operations are often undertaken in complex and hazardous situations where factors such as limited or no visibility, noise and time constraints impede progress into an unknown environment. In these situations, the fire-fighter/rescue personnel could be aided by a machine (a mobile robot) that can sense and give the follower tactile and haptic information in a similar manner to the interaction between the visually impaired person and a guide dog. The visually impaired person follows the dog through the signs being transmitted to his hand and interprets them into information about the environment and how to navigate the route. The aim of this research is to investigate and build a prototype robotic rein to emulate the natural and adaptable relationship observed between a guide dog and human when traversing an unknown path. From the previous work, an investigation and evaluation of the design of the SHU prototype have been undertaken and its outcomes are used in this research to improve the new system prototypes, especially in the area of designing the feedback and adaptive control. The new system has divided into four prototypes, each prototype has separated test to determine it is suitability. In the prototype (I) a set of sensors set in the front edge of the existing rein to know follower /robot location. The prototype (II), will implement to moving rein by installing an actuator. Prototype (III) has some sensors in the grip of the rein to calculate the strength of tensile which occur between the follower hand and the long axis of rein. In the prototype (IV), all previous prototypes will be connected to an integrated system which can move the follower to follow the robot path by sending messages from the robot to the follower in the form of rein movements The resultant system is an intelligent robotic rein which continuously interacts with the user to optimize the guidance in terms of comfort, following accuracy and safety. The improved system has been tested, analysed and evaluated against previous designs and compared against the aspiration of the human - guide dog relationship. A novel idea has been established in the use of the sensors to raise the level of the robot human interaction and achieving automatous robot/ follower safe navigation