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Autonomously traversing ship manholes using end-to-end vision-based control
Develop an end-to-end learning-based approach for autonomous drone navigation in ship ballast tank manholes, incorporating both real and simulated training data. The project aims to emphasize speed, a high success rate, and safety in flying through the confined spaces of ship interiors.
Navigating through ship manholes poses a significant challenge for industrial drones, requiring a combination of agility and adaptability to successfully traverse the confined spaces within ship interiors. Given the challenges of limited lighting, constrained spaces, and complex geometries, the proposed solution focuses on leveraging end-to-end learning techniques for drone navigation. This entails the development of end-to-end vision-based algorithms to facilitate agile maneuvers trained on diverse datasets of ship interiors collected from simulation and real-world experiments. Prospective students for this thesis project should possess a strong background in reinforcement learning, deep neural networks, and robot perception. Proficiency in PyTorch is essential, along with programming skills in both C++ and Python.
Navigating through ship manholes poses a significant challenge for industrial drones, requiring a combination of agility and adaptability to successfully traverse the confined spaces within ship interiors. Given the challenges of limited lighting, constrained spaces, and complex geometries, the proposed solution focuses on leveraging end-to-end learning techniques for drone navigation. This entails the development of end-to-end vision-based algorithms to facilitate agile maneuvers trained on diverse datasets of ship interiors collected from simulation and real-world experiments. Prospective students for this thesis project should possess a strong background in reinforcement learning, deep neural networks, and robot perception. Proficiency in PyTorch is essential, along with programming skills in both C++ and Python.
Develop an end-to-end learning-based approach for autonomous drone navigation in ship ballast tank manholes, incorporating both real and simulated training data. The project aims to emphasize speed, a high success rate, and safety in flying through the confined spaces of ship interiors. Eventually, the project outcome will demonstrate the efficiency and safety of the developed approach with real-world tests.
Develop an end-to-end learning-based approach for autonomous drone navigation in ship ballast tank manholes, incorporating both real and simulated training data. The project aims to emphasize speed, a high success rate, and safety in flying through the confined spaces of ship interiors. Eventually, the project outcome will demonstrate the efficiency and safety of the developed approach with real-world tests.