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Drones for search and rescue in disaster scenarios using RL
This project aims to develop a sophisticated Reinforcement Learning (RL) environment to train autonomous drones for efficient disaster response operations. By leveraging insights from drone racing research, the project will focus on creating a highly realistic 3D simulation environment.
Natural disasters pose significant challenges to timely and effective emergency response, often requiring rapid assessment of hazardous areas that are difficult or dangerous for humans to access. Autonomous drones have the potential to revolutionize disaster response by providing a fast, efficient, and adaptable means of navigating these environments. To realize this potential, advanced training environments are essential to prepare drones for the complex realities of disaster scenarios. This project leverages cutting-edge research in drone racing and reinforcement learning to create a highly realistic simulation environment, accurately modeling damaged buildings and potential survivor locations.
Natural disasters pose significant challenges to timely and effective emergency response, often requiring rapid assessment of hazardous areas that are difficult or dangerous for humans to access. Autonomous drones have the potential to revolutionize disaster response by providing a fast, efficient, and adaptable means of navigating these environments. To realize this potential, advanced training environments are essential to prepare drones for the complex realities of disaster scenarios. This project leverages cutting-edge research in drone racing and reinforcement learning to create a highly realistic simulation environment, accurately modeling damaged buildings and potential survivor locations.
This project aims to develop a sophisticated Reinforcement Learning (RL) environment to train autonomous drones for efficient disaster response operations. By leveraging insights from drone racing research, the project will focus on creating a highly realistic 3D simulation environment. The goal is to train drones to navigate these challenging environments, locate survivors swiftly, and make informed decisions in real-time.
This project aims to develop a sophisticated Reinforcement Learning (RL) environment to train autonomous drones for efficient disaster response operations. By leveraging insights from drone racing research, the project will focus on creating a highly realistic 3D simulation environment. The goal is to train drones to navigate these challenging environments, locate survivors swiftly, and make informed decisions in real-time.
Please send your CV and transcripts (bachelor and master), and any projects you have worked on that you find interesting to Angel Romero (roagui AT ifi DOT uzh DOT ch), Ismail Geles (geles AT ifi DOT uzh DOT ch), Jiaxu Xing (jixing AT ifi DOT uzh DOT ch), and Elie Aljalbout (aljalbout AT ifi DOT uzh DOT ch)
Please send your CV and transcripts (bachelor and master), and any projects you have worked on that you find interesting to Angel Romero (roagui AT ifi DOT uzh DOT ch), Ismail Geles (geles AT ifi DOT uzh DOT ch), Jiaxu Xing (jixing AT ifi DOT uzh DOT ch), and Elie Aljalbout (aljalbout AT ifi DOT uzh DOT ch)
