Summary of Arddqn: Attention Recurrent Double Deep Q-network For Uav Coverage Path Planning and Data Harvesting, by Praveen Kumar et al.
ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting
by Praveen Kumar, Priyadarshni, Rajiv Misra
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed ARDDQN model integrates double deep Q-networks (DDQN) with recurrent neural networks (RNNs) and an attention mechanism to generate path coverage choices that maximize data collection from IoT devices. This approach learns a control scheme for the UAV that generalizes energy restrictions, allowing efficient scaling to large environments. The model outperforms other RNN variants such as LSTM, Bi-LSTM, GRU, and Bi-GRU in terms of evolution parameters like data collection, landing, and coverage ratios for both coverage path planning (CPP) and data harvesting scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new AI model called ARDDQN that helps drones collect more data from sensors on the ground. It’s like a super smart autopilot system! The model uses special techniques to decide where to go and what to do, so it can collect as much data as possible while also being energy-efficient. This is really important for big areas or areas with lots of obstacles. |
Keywords
» Artificial intelligence » Attention » Lstm » Rnn
