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Vision-Based Autonomous Drone Recovery Using Reinforcement Learning
This project is focused on developing a vision-only flight recovery system for autonomous drones. A critical capability for autonomous drones is to recover safely from any unstable state. This project explores the potential of using reinforcement learning to enable a drone to transition from an unstable to a stable state, using only vision sensors. The challenge lies in creating a system that not only stabilizes the drone but also ensures it can safely land in various unforeseen scenarios.
This project is focused on developing a vision-only flight recovery system for autonomous drones. A critical capability for autonomous drones is to recover safely from any unstable state. This project explores the potential of using reinforcement learning to enable a drone to transition from an unstable to a stable state, using only vision sensors. The challenge lies in creating a system that not only stabilizes the drone but also ensures it can safely land in various unforeseen scenarios.
This project is focused on developing a vision-only flight recovery system for autonomous drones. A critical capability for autonomous drones is to recover safely from any unstable state. This project explores the potential of using reinforcement learning to enable a drone to transition from an unstable to a stable state, using only vision sensors. The challenge lies in creating a system that not only stabilizes the drone but also ensures it can safely land in various unforeseen scenarios.
- Develop a Reinforcement Learning Policy: Create a learning algorithm capable of recovering the drone from any dangerous, unstable state to a stable state, utilizing only vision-based inputs.
- Testing the system in simulation and hardware in the loop environment
- Testing the system in the real world platform
Requirements:
- Strong background in machine learning and reinforcement learning
- Proficient in programming in C++ and python
- Solid understanding of nonlinear dynamic systems.
- Comfortable in a hands-on, experimental environment.
- Develop a Reinforcement Learning Policy: Create a learning algorithm capable of recovering the drone from any dangerous, unstable state to a stable state, utilizing only vision-based inputs.
- Testing the system in simulation and hardware in the loop environment
- Testing the system in the real world platform
Requirements:
- Strong background in machine learning and reinforcement learning
- Proficient in programming in C++ and python
- Solid understanding of nonlinear dynamic systems.
- Comfortable in a hands-on, experimental environment.
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), Jiaxu Xing (xing AT ifi DOT uzh DOT ch) and Ismail Geles (geles 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), Jiaxu Xing (xing AT ifi DOT uzh DOT ch) and Ismail Geles (geles AT ifi DOT uzh DOT ch)