Summary of Machine Learning-based Estimation Of Wave Direction For Unmanned Surface Vehicles, by Manele Ait Habouche et al.
Machine Learning-Based Estimation Of Wave Direction For Unmanned Surface Vehicles
by Manele Ait Habouche, Mickaël Kerboeuf, Goulven Guillou, Jean-Philippe Babau
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Signal Processing (eess.SP)
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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 A novel machine learning-based approach is proposed for accurately estimating wave direction using sensor data from Unmanned Surface Vehicles (USVs). The method leverages Long Short-Term Memory (LSTM) networks to predict wave direction, improving navigation and ensuring operational safety. Experimental results demonstrate the LSTM model’s ability to learn temporal dependencies and provide accurate predictions, outperforming simpler baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unmanned surface vehicles (USVs) help us explore oceans and monitor the environment. To navigate safely, we need to know which way the waves are going. But traditional methods can be expensive and only show a small part of the ocean at a time. This paper suggests using machine learning to predict wave direction from data collected by USVs. The method uses special networks called LSTM (Long Short-Term Memory) networks to make predictions. Tests show that this approach works well and is better than simpler methods. |
Keywords
» Artificial intelligence » Lstm » Machine learning
