Riccardo Urban
Reinforcement Learning approach for autonomous UAVs path planning and exploration of critical environments.
Rel. Giorgio Guglieri, Simone Godio. Politecnico di Torino, Master of science program in Mechatronic Engineering, 2021
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial. Download (22MB) | Preview |
Abstract
Unmanned Aircraft Systems (UASs) have become an important and promising field of study in the aerospace industry. Their versatility and efficiency have led them to be used in a considerable number of different applications. Research in this field is constantly increasing their capabilities and with them, the number of tasks they can perform. For instance, recent developments in autonomous driving, often supported by artificial intelligence algorithms, have allowed them to work independently from human intervention. This advancement has greatly improved the possibility of using autonomous UASs in critical environments where it would be difficult or dangerous for a human to intervene.
One of the most challenging problems for UAS is the collaborative operation of multiple Unmanned Aerial Vehicles (UAVs) in the same environment to perform a common set of tasks
Relators
Publication type
URI
![]() |
Modify record (reserved for operators) |
