Keynotes

Towards the end of the 20th century, a Japanese TV show featured electric helicopters flying through complex mazes, hand-controlled by human pilots for a big prize. Watching it, I wondered: what if UAVs could do this autonomously? At that time, drones were bulky—typical drones in those days were the size of a motorbike—and onboard SLAM or lightweight perception was still far in the future. By the mid-2010s, the landscape had shifted: small multirotors had emerged, and GPUs became compact enough to fly. In 2016, I co-founded what is recognized as the first autonomous drone racing competition, held at IROS in Daejeon. Since then, we have witnessed a surge of autonomous drone challenges, from the DARPA Subterranean Challenge to Lockheed Martin’s AlphaPilot and, most recently, the UAE’s A2RL DCL. In this talk, I will reflect on this trajectory—from the point when quadrotors came to symbolize drones, through the breakthroughs enabled by global competitions, to the lessons learned from my own first-hand experiences. I will also discuss the challenges ahead in building more capable, trustworthy, and socially beneficial drone technologies.

Captain Gómez graduated as an electrical engineer from the Escuela de Ingenieros of the Mexican Navy. He has a dual master’s degree in Master of Sciences of Electrical Engineering and Master of Sciences in Engineering Acoustics from the United States Naval Postgraduate School. He has held several appointments in the Mexican Navy such as the Embedded Systems designer for UAV SPARTAAMv44, SPARTAAMv88 and, SPARTAAMv200. LCDR Gómez has been awarded with several distinctions, including Medals of Perseverance, Mexican Navy Research, and Development distinctive, and Honorable Mention for the excellence in the design of UAV. Currently, LCDR Gómez is the head of the Embedded Systems Lab in the SPARTAAM UAS Program at the Mexican Navy.

Lieutenant Guerrero, Electronics and Naval Communications Engineer, graduated from the Naval Engineers School of Mexico (2006–2011) and completed a specialisation in Operational Logistics at CESNAV in 2021. He has received technical training from Microchip, a PCB Design Course for military, aerospace, and extreme environments, and IPC/WHMA-A-620 certification from the Institute of Printed Circuits. He has served in various positions at INIDETAM and UNINDETEC, and is currently Head of the Integration and Technical Support Laboratory for Unmanned Aircraft. His work includes avionics engineering in the design, construction, and testing of avionics subsystems, simulation models, UAV prototype integration, as well as operation and technical support for UAV systems such as SPARTAAM-44, SPARTAAM-88, SPARTAAM-VTOL, and SPARTAAM-Satellite. For his contributions, he was awarded the Technological Research and Development Distinction.

Small Unmanned Aerial Vehicles (UAV) or drones are providing big amounts of data. Often, new classes of drones are deployed on a regular basis to help in different tasks. The purpose of this talk is to present a technique of drone detection using acoustic means. Although there are several techniques of drone detection, such as electromagnetic signal detection, radar location, electro-optic, and infrared visual detection. It is feasible to detect the unique acoustic signature of a drone class using modern techniques of signal processing. Using acoustic sensors, a drone can be detected, and with machine learning techniques, its path can be tracked. Furthermore, there are several other data that can be extracted using deep learning techniques based on neural networks such as the class of the drone, which can help in the decision process of the main function of the drone. At the end of the talk is presented a small routine used to detect and classify drones using its acoustic signature.