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Projecte llegit

Títol: UAV Network Design: A Rapidly-exploring Random Graph and Probabilistic Roadmap by Simulated Annealing

Estudiants que han llegit aquest projecte:

Director/a: BARRADO MUXÍ, CRISTINA

Departament: DAC

Títol: UAV Network Design: A Rapidly-exploring Random Graph and Probabilistic Roadmap by Simulated Annealing

Data inici oferta: 07-05-2024     Data finalització oferta: 07-05-2024


Estudis d'assignació del projecte:
    DG ENG AERO/TELEMÀT
Tipus: Individual
 
Lloc de realització: ERASMUS
 
        Supervisor/a extern: Daniel Delahaye
 
Paraules clau:
UAV, Drones, Optimization, Airspace design, Rapidly-exploring Random Graph, Probabilistic Roadmap, Simulated Annealing
 
Descripció del contingut i pla d'activitats:
The primary objective of this TFG is to design optimal networks for UAVs operating in environments with obstacles, such as urban areas. The parameters to be optimizes include the distance between drone stations and clients. To achieve this goal we will compare two different approaches for the graph generation: the augmented rapidly-exploring random graph and the probabilistic roadmap. Both approaches will be integrated with Simulated Annealing for control and implementation in Java. Additionally, it should be noted that this TFG is being conducted concurrently with a research paper.
This work will be developed as part of the Erasmus+ programme at the École Nationale de l'Aviation Civile (France)
 
Overview (resum en anglès):
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.

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