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

TL;DR

This paper introduces ARDDQN, a novel attention-based recurrent double deep Q-network that enhances UAV coverage path planning and data harvesting efficiency by integrating RNNs and attention mechanisms for scalable, energy-aware control.

Contribution

The paper presents ARDDQN, combining DDQN, RNNs, and attention to improve UAV path planning and data harvesting, with a focus on scalability and energy constraints.

Findings

ARDDQN outperforms baseline models in data collection and coverage ratios.

LSTM with attention mechanism yields the best results among RNN variants.

Structured environment maps enable efficient scaling to large environments.

Abstract

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.