Summary of Navigation in a Simplified Urban Flow Through Deep Reinforcement Learning, by Federica Tonti et al.
Navigation in a simplified Urban Flow through Deep Reinforcement Learning
by Federica Tonti, Jean Rabault, Ricardo Vinuesa
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research aims to minimize the environmental impact of unmanned aerial vehicles (UAVs) in urban environments by developing novel prediction models and optimizing flight planning strategies. Specifically, the study employs deep reinforcement learning (DRL) algorithms capable of autonomously navigating UAVs in urban settings while reducing energy consumption and noise. The researchers utilize fluid-flow simulations representing the environment and train the UAV as an agent interacting with an urban domain. They validate their methodology by solving Zermelo’s problem, a fundamental navigation challenge. Compared to simple PPO and TD3 algorithms, the proposed PPO+LSTM cells exhibit improved success rates (98.7%) and crash rates (0.1%). This work paves the way for DRL strategies guiding UAVs in three-dimensional flow fields using real-time signals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unmanned aerial vehicles are becoming more common in urban areas, but they can be noisy and use a lot of energy. To make them more environmentally friendly, scientists want to find new ways to predict where they should go and how they should get there. They’re trying to develop special computer programs that can help guide the drones through cities while minimizing their impact on the environment. This is done by using computer simulations that mimic the urban environment and training the drone as if it’s a driver interacting with the city. The researchers tested this approach and found that it works better than other methods they tried. |
Keywords
» Artificial intelligence » Lstm » Reinforcement learning
