The coverage path planning (CPP) algorithms aim to cover the total area of interest with
minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path …

Coverage path planning consists of finding the route which covers every point of a certain
area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in …

Speed is essential in wildlife surveys due to the dynamic movement of animals throughout
their environment and potentially extreme changes in weather. In this work, we present a …

This paper presents a multi-objective coverage flight path planning algorithm that finds
minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in …

Due to the high deployment flexibility and strong maneuverability, unmanned aerial vehicles
(UAVs) have gained a significant attention in civilian and military applications. One of the …

Abstract The Deep Q-Network (DQN) is one of the key methods in the deep reinforcement
learning algorithm, which has a deep neural network structure used to estimate Q-values in …

Underwater gliders (UGs) are widely applied to regional exploration to find potential targets.
However, the complex marine environment and special movement patterns make it difficult …

Reducing the total mission time is essential in wildlife surveys owing to the dynamic
movement of animals throughout their migrating environment and potentially extreme …

Disaster management has always been a struggle due to unpredictable changing conditions
and chaotic occurrences that require real-time adaption. Highly optimized missions and …

Abstract The Deep Q-Network (DQN) is one of the deep reinforcement learning algorithms,
which uses deep neural network structure to estimate the Q-value in Q-learning. In the …