  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.
