Work

A platform for the navigation of Unmanned Aerial Vehiclesbased on Reinforcement Learning

Year: 2026

Type: article

Abstract: This article introduces a platform for developing use cases for the automated control of unmanned aerial vehicles (UAVs), utilising the AirSim simulator. This platform allows for the generation of realistic flight scenarios involving multiple UAVs.The proposed platform facilitates the construction of use cases for the development, validation, and verification of reinforcement learning (RL) and neural networks in critical real-time systems. These algorithms can learn to navigate dynamic environments without human intervention. The platform was validated using two UAVs with neural networks that were trained using the Soft Actor-Critical (SAC) algorithm.

Source: Jornadas de Automática

DOI: 10.17979/ja-cea.2026.47.13789

Authors: Daniel Álvarez Novoa, Ángel-Grover Pérez-Muñoz, Alejandro Alonso, María S. Pérez

Institution: Universidad Politécnica de Madrid

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Ministerio de Ciencia e Innovación

Award: G8572308697