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Foundation Model for Drone Navigation in Confined Spaces
This master thesis project centers on the development of a foundation model for drone navigation within confined spaces such as ballast tanks of ships.
This master thesis project centers on the development of a foundation model for drone navigation within confined spaces such as ballast tanks of ships. A specific emphasis is on efficiently fine-tuning the model using data collected from drone flights for fast domain adaptation. Recent advancements have showcased the capability of foundation models to help autonomously navigate ground robots, particularly in planar environments. This project will involve extending a foundation navigation model capable of directly generating drone control commands from input images, focusing on navigating challenging environments with narrow passages and varying lighting conditions. Applicants should have a good understanding of mobile robot navigation, machine learning experience (PyTorch), and programming experience in C++ and Python.
This master thesis project centers on the development of a foundation model for drone navigation within confined spaces such as ballast tanks of ships. A specific emphasis is on efficiently fine-tuning the model using data collected from drone flights for fast domain adaptation. Recent advancements have showcased the capability of foundation models to help autonomously navigate ground robots, particularly in planar environments. This project will involve extending a foundation navigation model capable of directly generating drone control commands from input images, focusing on navigating challenging environments with narrow passages and varying lighting conditions. Applicants should have a good understanding of mobile robot navigation, machine learning experience (PyTorch), and programming experience in C++ and Python.
The primary objective of this master thesis project is to develop and fine-tune a foundation navigation model for drones in confined spaces, leveraging data from real and simulated drone flights. We aim to demonstrate the efficacy of the navigation model through real-world deployment using a commercial drone platform.
The primary objective of this master thesis project is to develop and fine-tune a foundation navigation model for drones in confined spaces, leveraging data from real and simulated drone flights. We aim to demonstrate the efficacy of the navigation model through real-world deployment using a commercial drone platform.
Contact Details: Harmish Khambhaita (harmish@ifi.uzh.ch), Christian Sprecher (sprecher@ifi.uzh.ch)
Contact Details: Harmish Khambhaita (harmish@ifi.uzh.ch), Christian Sprecher (sprecher@ifi.uzh.ch)