Control of an unmanned aerial vehicle using a deep reinforcement learning approach

Autor(es)

Alves, Adson Nogueira ; Colombini, Esther Luna ; Simoes, Alexandre da Silva

• Tipo de documento: Artigo Científico
• Fonte: 2025 BRAZILIAN CONFERENCE ON ROBOTICS, CROS; v. N/A, p. 6-pg., 2025-01-01.

Resumo

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in complex, dynamic environments, demanding advanced control systems for safe and efficient autonomous navigation. This work explores a deep reinforcement learning approach using the Soft Actor-Critic algorithm for model-free, mapless UAV navigation. Leveraging visual inputs compressed via an autoencoder alongside embedded sensor data, we construct multimodal state representations that enable robust low-level flight control. The proposed framework is evaluated across progressively challenging scenarios-from obstacle-free settings to environments with fixed and dynamic obstacles-demonstrating effective policy generalization and stable flight behavior. Experimental results indicate that SAC enables resilient UAV navigation through continuous policy improvement, even under unstructured and previously unseen conditions. (AU)

• Processo FAPESP: 20/09850-0 - Centro de Pesquisa Aplicada em Inteligência Artificial: impulsionando a transformação das indústrias rumo ao padrão 5.0
• Beneficiário: Jefferson de Oliveira Gomes
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada