Aprèn - Cristina Barrado Muxí

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)
Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT)

SISTEMES OPERATIUS - 300028

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Towards autonomous vertiport operations: a deep reinforcement learning perspective

Treball Final de Grau

Martín Goushesh, Nayib

Tutor/a - Director/a: Barrado Muxí, Cristina; García Lasheras, Raquel

Data de publicació: 2024

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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This project explored the feasibility of using Deep Reinforcement Learning (DRL) to manage drone traffic at a Vertiport dedicated to automated package delivery. A DRL agent, utilizing the Proximal Policy Optimization (PPO) algorithm, was trained to coordinate multiple drones concurrently, aiming to achieve safe and efficient operations within a simulated urban environment. A realistic simulation environment was developed by integrating the AirSim simulator with a custom server, 'DroneManagerServer', built using the FastAPI framework. This environment enabled the creation of complex scenarios and provided a platform for training and evaluating the DRL agent's performance. The agent's learning process was guided by a meticulously designed reward function that penalized collisions and inefficient actions while incentivizing safe inter-drone separation and efficient completion of delivery missions. While demonstrating the potential of DRL for Vertiport management, the project also revealed challenges associated with applying DRL to real-world control problems. Limitations in simulator performance, the complexity of hyperparameter tuning, and the intricacies of reward function design influenced the agent's overall performance and limited the scope of evaluation to a single challenging scenario. The project's key contributions include: successfully demonstrating DRL's capability to learn complex drone coordination strategies; identifying specific challenges in applying DRL to drone traffic management (UTM); and developing a flexible DRL framework that can serve as a foundation for future research. Future research directions include: enhancing the simulation environment to accommodate higher drone densities and more realistic scenarios; exploring advanced DRL techniques to improve performance and scalability; and investigating methods to enhance the agent's robustness and generalization capabilities. Furthermore, integrating U-space regulations and exploring hybrid approaches that combine DRL with other AI techniques are identified as promising avenues for advancing DRL-based UTM. By addressing these future research directions, this project aims to contribute to the development of robust and reliable autonomous systems for managing drone traffic, ultimately facilitating the safe and efficient integration of drones into urban airspace and unlocking the full potential of Urban Air Mobility.

Este proyecto exploró la viabilidad de utilizar el Aprendizaje por Refuerzo Profundo (DRL, por sus siglas en inglés) para gestionar el tráfico de drones en un Vertipuerto dedicado a la entrega automatizada de paquetes. Un agente DRL, utilizando el algoritmo Proximal Policy Optimization (PPO), fue entrenado para coordinar múltiples drones simultáneamente, con el objetivo de lograr operaciones seguras y eficientes dentro de un entorno urbano simulado. Se desarrolló un entorno de simulación realista mediante la integración del simulador AirSim con un servidor personalizado, 'DroneManagerServer', construido utilizando el framework FastAPI. Este entorno permitió la creación de escenarios complejos y proporcionó una plataforma para entrenar y evaluar el rendimiento del agente DRL. El proceso de aprendizaje del agente fue guiado por una función de recompensa meticulosamente diseñada que penalizaba las colisiones y las acciones ineficientes, al tiempo que incentivaba la separación segura entre drones y la finalización eficiente de las misiones de entrega. Si bien se demostró el potencial del DRL para la gestión de Vertiports, el proyecto también reveló desafíos asociados con la aplicación del DRL a problemas de control del mundo real. Las limitaciones en el rendimiento del simulador, la complejidad del ajuste de hiperparámetros y las complejidades del diseño de la función de recompensa influyeron en el rendimiento general del agente y limitaron el alcance de la evaluación a un único escenario desafiante. Las principales contribuciones del proyecto incluyen: demostrar con éxito la capacidad del DRL para aprender estrategias complejas de coordinación de drones; identificar desafíos específicos en la aplicación del DRL a la gestión del tráfico de drones (UTM); y desarrollar un framework DRL flexible que puede servir como base para futuras investigaciones. Las futuras direcciones de investigación incluyen: mejorar el entorno de simulación para acomodar mayores densidades de drones y escenarios más realistas; explorar técnicas avanzadas de DRL para mejorar el rendimiento y la escalabilidad; e investigar métodos para mejorar la robustez y las capacidades de generalización del agente. Además, la integración de las regulaciones del U-space y la exploración de enfoques híbridos que combinan DRL con otras técnicas de IA se identifican como vías prometedoras para avanzar en la UTM basada en DRL. Al abordar estas futuras direcciones de investigación, este proyecto tiene como objetivo contribuir al desarrollo de sistemas autónomos robustos y confiables para la gestión del tráfico de drones, facilitando en última instancia la integración segura y eficiente de los drones en el espacio aéreo urbano y desbloqueando todo el potencial de la Movilidad Aérea Urbana.

UAV network design: a rapidly-exploring random graph and probabilistic roadmap by simulated annealing

Treball Final de Grau

Ubiergo Gómez, Maria

Tutor/a - Director/a: Barrado Muxí, Cristina; Delahaye, Daniel

Data de publicació: 2024

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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In this Bachelor's thesis, the main objective is to design an optimal network for Unmanned Aerial Vehicles (UAVs) in complex environments, such as urban areas with obstacles. The goal is to develop a network that optimally connects UAV bases and hospitals in the city of Madrid, while considering various constraints like distance, redundancy, and obstacle avoidance. To achieve this, the project proposes an algorithm for designing optimal networks. This involves designing the graph using either the Rapidly-exploring Random Graph (RRG) or the Probabilistic Roadmap (PRM). Simulated Annealing (SA) is then responsible for the optimization process. The project starts by contextualizing the current situation of drones in the delivery field. Due to the increasing integration of drones into the daily operations of such services, especially in urban logistics, the need for efficient airspace management and network design has arisen. Next, it explains the algorithms used (PRM, RRG and SA) in the literature review. This theoretical background is useful for comprehending the formalization of the final algorithm, which involves defining the problem, setting objectives, identifying variables and constraints, and designing the cost functions. All this leads to the methodology, explaining how the results are obtained. It describes the space in which the network is designed, the combined use of all the algorithms, and specific important processes needed to achieve the results. After understanding the rationale, the objectives, and how the simulations will be carried out, the results are presented. This section is divided into two parts: The first simulations are conducted in predefined scenarios to test the algorithm's performance. Once these tests are completed, the algorithm is applied to the airspace of Madrid. The information about Madrid is extracted from the MUSE Project. The results include not only the UAV network design but also a comparison between PRM and RRG, along with an analysis of the environmental impact of each approach. Finally, before the conclusions, there is also an analysis of the social impact this delivery method could have. The conclusions summarize the key findings and the algorithm's performance. Overall, this project provides a valuable approach to UAV network design, offering a foundation for further research and development in this field.

Potentials of the generative artificial intelligence in air traffic management

Treball Final de Grau

Hinno González, Ingrid

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2024

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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SISTEMES OPERATIUS (Examen parcial, 2n quadrimestre) Restricted access to the UPC academic community

Examen

Barrado Muxí, Cristina

Data de publicació: 2024

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

SISTEMES OPERATIUS - 300028

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SISTEMES OPERATIUS (Examen parcial, 2n quadrimestre) Restricted access to the UPC academic community

Examen

Barrado Muxí, Cristina

Data de publicació: 2024

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

SISTEMES OPERATIUS - 300028

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INFORMÀTICA 1 (Examen final, 2n quadrimestre) Restricted access to the UPC academic community

Examen

Barrado Muxí, Cristina; Hidalgo Carrillo, Rubén

Data de publicació: 2023

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

INFORMÀTICA I - 300207

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Assessing drone trajectory error and improving flightpath predictability

Treball Final de Grau

Terés I Teixiné, Pau

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2023

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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The rapid growth of the drone industry aims to develop applications and implement them in a wide range of areas. This includes busy urban areas for services such as surveillance, deliveries and monitoring. In this context, it is essential to have an excellent design of the airspace. This thesis focuses on the analysis of a dataset from the Very Large Demonstration project of CORUSXUAM, which contributes to the U-Space mission of developing a safe, sustainable, efficient and fully digitalized airspace for integrated Urban Air Mobility which does not interfere with current ATM operations. The dataset includes flight plans, telemetry, and U-space predictions for 72 drone flights. The analysis involves comparing intended trajectories with actual flight paths to identify factors contributing to deviations from the flight plan and computing relevant performance parameters to assess the adherence of the drones to the flight plan. This is done with the use of dynamic time warping algorithms in order to establish a link between the telemetry points and the flight plan, which sets the basis for the next section of the project. Having processed the data, during this project we develop machine learning models to predict telemetry parameters based on the input flight plan. Several models are tested and evaluated to find the most suitable one for our objective. The project also involves visualizing and interpreting the data to gain insights of the drone performance and adherence to the flight plan. Position prediction opens up a new area of research and in this project the approach is to use an alternative method to define the spacing and size of the airways that compose the flight plans so as to dictate safety areas to prevent any possible conflict that could appear in future flights in a busy area if the spacing were to be below the thresholds. The results of this study demonstrate a successful progression from raw data to a comprehensive analysis, offering valuable insights for evaluating drone performance and predicting flight times. The development of various data visualization functions enabled efficient and effective interpretation of the data. While the obtained results with the available dataset are remarkable, the potential for further improvement lies mainly in acquiring a larger dataset with more features and samples, which would enhance the performance of the machine learning models and yield even more accurate predictions.

Design and development of a students' performance predicting LMS utilizing Machine Learning based on mental stress level measured through a Bluetooth enabled smart watch

Projecte Final de Màster Oficial

Kazmi, Syed Haider Ali

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2023

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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INFORMÀTICA 1 (Examen parcial, 2n quadrimestre) Restricted access to the UPC academic community

Examen

Barrado Muxí, Cristina

Data de publicació: 2023

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

INFORMÀTICA I - 300207

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UAM Airspace Design

Treball Final de Grau

Martínez Aliaga, Ferran

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2023

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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INFORMÀTICA 1 (Examen final, 1r quadrimestre) Restricted access to the UPC academic community

Examen

Barrado Muxí, Cristina

Data de publicació: 2023

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

INFORMÀTICA I - 300207

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Aircraft-to-aircraft separation based on reinforcement learning

Treball Final de Grau

Prawda, Weronika

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2022

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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Prediction of 4D drone trajectories from demonstration data

Treball Final de Grau

Vázquez Husillos, Laura

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2022

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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Air traffic control using separation algorithm based on rules of the air

Treball Final de Grau

Fuentes Coll, Lidia

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2022

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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Crowd counting using density maps

Projecte Final de Màster Oficial

Trujillo Gomez, Juan Manuel

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2022

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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Law enforcement agents have to care about the number of people in public areas to ensure security. The problem they have is that they do not have tools to measure the number of people in a fast and precise way. This need has been especially important since 2020 COVID pandemic arrived to our society and the control of people is relevant to avoid spread of COVID. This Master Thesis is complementing other previous Master Thesis presented in 2021 where via an Android app connected to a drone the system was able to count people from the images captured in real time. This solution was only able to count individual people, as crowds of people are complex to measure following standard object detection algorithms as YOLO technology. In our Master Thesis we are adding a new functionality by being able not only to count individuals but also counting crowds of people. With this new functionality the app could provide to the police a more accurate tool to be able to count people in different scenarios as prides, sports events, demonstrations, concerts¿ where crowd is a normal situation. As main technology driver we are working with CNN (Convolutional Neural Networks). First, we have been implementing a CNN density map using the CSRNet technology that is able to count people by measuring the concentration of people. Therefore, an important part of this Master Thesis is to create a process to split the input images in 2 (segmentation process), one for YOLO (individual persons) and other for CSRNET (crowds of people). This process has been implemented using a second CNN called Region-based CNN (R-CNN), that we found it was the most suitable tool to train a model to detect a crowd. The solution has been developed in Google Colab platform and using Python as programming language. We have been working with images taken from drones from Castelldefels Police and UPC but also public datasets. The final solution has been able to detect crowds and calculate the number of people in that crowd with a maximum error of 20% considering Mean Average Percentage Error (MAPE) and 89 considering Mean Absolute Error (MAE).

Improvements in sectorization optimizer within the dynamic airspace configuration concept

Treball Final de Grau

Ruano Ferrer, Sara

Tutor/a - Director/a: Barrado Muxí, Cristina; Bracero, Adrian Fabio

Data de publicació: 2022

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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The Dynamic Airspace Configuration (DAC) concept goal is to create more flexible airspace designs and configurations as a way to improve airspace use and absorb the increasing traffic demand. This modifies the conception of an airspace comprised of fixed volumes to one using a bigger quantity of building blocks and/or flexible boundaries. This translates into an increase in the number of possible configurations, which results in the creation of a large-scale optimization problem. To tackle this problem, CRIDA developed a sectorization optimizer ¿SECTORIA¿ which ranks all the possible configurations for a defined time and airspace in terms of occupancy and selects the optimal one. The aim of this thesis is to progress the tool in order to bring it closer to an operational environment. In particular, an improvement in its capacity constraint is added in order to obtain optimal solutions in all cases. A post-processing tool is also developed to refine the optimizer results and obtain more accurate configuration plans. The validation of these improvements is then carried out in two use cases: current sectorization in Madrid ACC Route 1 and a DAC sectorization of the same airspace, considering the addition of vertical cuts. The samples of traffic used are corresponding to the dates June 22nd, July 3rd, August 3rd and February 13th, 2019. The first feature is validated comparing the results obtained with and without the improvement, whereas the post-processing tool results are compared with the configuration plan opened with the current airspace design on the days of study and with a blind test performed on a Subject Matter Expert (SME). Results from the first validation show a better handling of inevitable overload, leading SECTORIA to provide more feasible solutions. On the other hand, the post-processing tool shows promising results, showing improvements in ATC hours which can reach up to 30\%. Conclusions and future work are then described following the analysis of the results.

Emerging technologies [drones, air taxis, smart phones, nuclear energy] & liberla acceptance

Projecte Final de Màster Oficial

Noni, Millen Ndemiso

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2022

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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Analysis of operational data to identify emerging safety risks

Treball Final de Grau

Hernández Pintó, Abel

Tutor/a - Director/a: Barrado Muxí, Cristina

Data de publicació: 2021

Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC)

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In view of the paramount importance of safety in the Air Traffic Management system, EUROCONTROL has been developing risk models for ATM to quantify and characterize how safe the different airspaces inside the ECAC region are. The risk models mainly characterize accidents by means of the quantification of barriers that avoid precursors becoming accidents due to their good functioning. While barriers are defined as probabilities of failure, and are quantified thanks to Fault Tree Analysis, where every element is a contributor to failure, precursors are quantified as frequencies of occurrence, and are based upon experience. This provides a static view of the models, and does not take into consideration more dynamic aspects such as the traffic flow characteristics, sector complexity, and many other features not used in the risk models because they would not allow generalization, and could only refer to specific airspaces as small as simple sectors. In view of the previous statements, this project is born with the objective to find a machine learning model capable of predicting air traffic control interventions for potential conflict avoidance inside an en-route sector from the FIR Península in Spain. The idea is to build a model explainable enough to provide sufficient information about complexity and traffic flow features in an en-route sector inducing tactical conflicts (precursors in the EUROCONTROL en-route Mid-Air Collision risk model), and offer a complementary vision of the given model. To develop the model, an analysis of operational data from the Spanish ANSP ENAIRE is made, accompanied by a study to find the losses of separation and potential tactical conflicts (mid-air collision accident precursors) during a 29-day period with a conflict detector tool. The last step is the training and testing of two tree-based supervised learning models. The outcome of the supervised learning model allows the identification of the flow and complexity features that induce air traffic control interventions to avoid potential tactical conflicts and LoS with a 99.79% of certainty in conflict prediction in a single en-route sector. Future evolutions of the model will consider the tactical ATCO support, conflict resolution with reinforcement learning, and finally the automation of ATCO tasks.