# ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting

arXiv: https://arxiv.org/abs/2405.11013 · HTML: https://mlsanity.com/paper/2405.11013/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 11 of 11)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12461/) (2020)
- [UAV Assisted Data Collection for Internet of Things: A Survey](https://mlsanity.com/paper/2211.09555/) (2022)
- [Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires](https://mlsanity.com/paper/2006.07969/) (2020)
- [UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2007.00544/) (2020)
- [Long Short-Term Memory Neural Network for Financial Time Series](https://mlsanity.com/paper/2201.08218/) (2022)
- [UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.06917/) (2020)

## Built on by (most cited first, 1 of 1)

- [Meta-Adaptive Beam Search Planning for Transformer-Based Reinforcement Learning Control of UAVs with Overhead Manipulators under Flight Disturbances](https://mlsanity.com/paper/2603.26612/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) have gained popularity in data harvesting (DH) and coverage path planning (CPP) to survey a given area efficiently and collect data from aerial perspectives, while data harvesting aims to gather information from various Internet of Things (IoT) sensor devices, coverage path planning guarantees that every location within the designated area is visited with minimal redundancy and maximum efficiency. We propose the ARDDQN (Attention-based Recurrent Double Deep Q Network), which 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 and to learn a control scheme for the UAV that generalizes energy restrictions. We employ a structured environment map comprising a compressed global environment map and a local map showing the UAV agent's locate efficiently scaling to large environments. We have compared Long short-term memory (LSTM), Bi-directional long short-term memory (Bi-LSTM), Gated recurrent unit (GRU) and Bidirectional gated recurrent unit (Bi-GRU) as recurrent neural networks (RNN) to the result without RNN We propose integrating the LSTM with the Attention mechanism to the existing DDQN model, which works best on evolution parameters, i.e., data collection, landing, and coverage ratios for the CPP and data harvesting scenarios.