Using Reinforcement Learning in the Path Planning of Swarms of UAVs for the Photographic Capture of Terrains

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.

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

“…Moreover, in the realm of wheeled vehicles, such as Ackermann robots, can be combined with Bezier curves to generate smooth trajectories (Zhang, 2023). Furthermore, has been successfully applied to the autonomous control of Unmanned Aerial Vehicle (UAV) swarms, thereby enabling the identification of optimal flight paths and complete coverage of areas featuring obstacles (Puente-Castro et al, 2024). Finally, QL also enables UAV navigation that takes into account factors such as path length, safety, and energy consumption (Carvalho, Hiago Batista, Fagundes-Júnior, & Brandão, 2023).…”