Using Reinforcement Learning in the Path Planning of Swarms of UAVs for the Photographic Capture of Terrains
Title
Using Reinforcement Learning in the Path Planning of Swarms of UAVs for the Photographic Capture of TerrainsDate
2021Citation
Puente-Castro, A.; Rivero, D.; Pazos, A.; Fernandez-Blanco, E. Using Reinforcement Learning in the Path Planning of Swarms of UAVs for the Photographic Capture of Terrains. Eng. Proc. 2021, 7, 32. https://doi.org/10.3390/engproc2021007032
Abstract
[Abstract] The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better trajectories than using a single global network. There is no need to use additional map information other than the current state of the environment, like targets or distance maps.
Keywords
UAV swarm
Path planning
Reinforcement learning
Q-learning
Artificial neural network
Terrain
Path planning
Reinforcement learning
Q-learning
Artificial neural network
Terrain
Description
Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.
Editor version
Rights
Atribución 3.0 España