Analysis and implementation of multiagent deep reinforcement learning algorithms for natural disaster monitoring with swarms of drones

Baldazo Escriña, David (2019). Analysis and implementation of multiagent deep reinforcement learning algorithms for natural disaster monitoring with swarms of drones. Thesis (Master thesis), E.T.S.I. Telecomunicación (UPM).

Description

Title: Analysis and implementation of multiagent deep reinforcement learning algorithms for natural disaster monitoring with swarms of drones
Author/s:
  • Baldazo Escriña, David
Contributor/s:
  • Parras Moral, Juan
Item Type: Thesis (Master thesis)
Masters title: Ingeniería de Telecomunicación
Date: 2019
Subjects:
Freetext Keywords: Swarms, drones, machine learning, reinforcement learning, optimal control, navigation, surveillance, natural disasters, deep learning, distributed, multiagent, recurrent neural networks.
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Natural disasters are the cause of a great amount of deaths and economic loss every year. The rapid and efficient deployment of mitigation and relief work is of great value but can be hampered by the lack of information in real time about the situation. Technological progress has allowed for a gradual improvement of this process of information retrieval. Automatic detection of wildfires and floods from images is now a reality. However, the real-time update of natural disaster maps still requires the physical presence of sensors. A natural disaster surveillance strategy that is gaining traction in recent years uses swarms of drones to acquire images of the environment. The cost reduction of this architecture depends on the automation of swarm cooperative navigation. The goal of this Master’s thesis is to build upon the recent advancements in the use of Deep Reinforcement Learning techniques for swarm navigation to improve them and apply them to a wider variety of situations. In particular, we have developed a platform for the simulation and training of swarms of drones and a series of multiagent algorithms. Lastly, we have trained deep neural networks with and without memory in simulations of both wildfires and floods, following several different strategies with varying levels of training decentralization, and we have studied the potential benefits of each approach. The final practical outcome is a series of trained neural networks which can be loaded onboard the drones of the swarm to control their navigation in a decentralized manner.

More information

Item ID: 56866
DC Identifier: http://oa.upm.es/56866/
OAI Identifier: oai:oa.upm.es:56866
Deposited by: Biblioteca ETSI Telecomunicación
Deposited on: 14 Oct 2019 12:40
Last Modified: 14 Oct 2019 12:40
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