Framework and Evaluation Methodology for Autonomous Drone Racing

Abstract

Autonomous drone racing presents demanding challenges in perception, localization, navigation, and high-speed control. This work proposes a complete architecture and evaluation methodology based on the open-source Aerostack framework. The system and its individual algorithms are evaluated through both simulated experiments and real flights on an indoor racing circuit using different configurations.

Javier Rodríguez Vázquez

Principal Researcher

Principal Researcher in Artificial Intelligence working on generalizable autonomy, embodied AI, adaptive agents, planning, memory, and robot learning.