ifrunistuttgart/RL_CrossCountrySoaring
This repository includes a reinforcement learning framework for solving the tactical decision-making problem subject to cross-country soaring (by the example of the competition task of GPS Triangle racing).
This framework helps design autonomous systems for cross-country soaring, specifically for tasks like GPS Triangle racing. It takes environmental data (wind, updrafts) and aircraft dynamics to output optimal tactical flight decisions. This is for aerospace engineers, researchers, and anyone developing autonomous gliders or uncrewed aerial vehicles (UAVs) for long-distance flight.
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Use this if you are developing autonomous gliders that need to make smart, real-time decisions about when to cover distance, exploit updrafts, or map the environment during cross-country flights.
Not ideal if you are looking for a plug-and-play solution for general drone navigation or short-range flight control.
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Oct 25, 2022
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