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Vision-based End-to-End Flight with Obstacle Avoidance
Recent progress in drone racing enables end-to-end vision-based drone racing, directly from images to control commands without explicit state estimation. In this project, we address the challenge of unforeseen obstacles and changes to the racing environment. The goal is to develop a control policy that can race through a predefined track but is robust to minor track layout changes and gate placement changes. Additionally, the policy should avoid obstacles that are placed on the racetrack, mimicking real-world applications where unforeseen obstacles can be present at any time.
Recent progress in drone racing enables end-to-end vision-based drone racing, directly from images to control commands _without explicit state estimation_. In this project, we address the challenge of unforeseen obstacles and changes to the racing environment. The goal is to develop a control policy that can race through a predefined track but is robust to minor track layout changes and gate placement changes. Additionally, the policy should avoid obstacles that are placed on the racetrack, mimicking real-world applications where unforeseen obstacles can be present at any time.
Requirements:
- Machine learning experience (PyTorch)
- Excellent programming skills in C++ and Python
Recent progress in drone racing enables end-to-end vision-based drone racing, directly from images to control commands _without explicit state estimation_. In this project, we address the challenge of unforeseen obstacles and changes to the racing environment. The goal is to develop a control policy that can race through a predefined track but is robust to minor track layout changes and gate placement changes. Additionally, the policy should avoid obstacles that are placed on the racetrack, mimicking real-world applications where unforeseen obstacles can be present at any time.
Requirements: - Machine learning experience (PyTorch) - Excellent programming skills in C++ and Python
Not specified
Leonard Bauersfeld (bauersfeld@ifi.uzh.ch), Ismail Geles (geles@ifi.uzh.ch)
Leonard Bauersfeld (bauersfeld@ifi.uzh.ch), Ismail Geles (geles@ifi.uzh.ch)
