Descripción del proyecto
Hacia una navegación autónoma de los drones similar a la de los pilotos humanos
Los drones todavía no son capaces de navegar por entornos complejos de la misma forma que los pilotos humanos. Para lograr robots más ágiles, se necesitan sensores más rápidos y un procesamiento de baja latencia. El proyecto AGILEFLIGHT, financiado con fondos europeos, pretende desarrollar métodos científicos innovadores para demostrar la navegación autónoma, basada en la visión y ágil de los cuadricópteros en entornos desconocidos, sin GPS y abarrotados con posibles obstáculos en movimiento. El objetivo es lograr una navegación tan eficaz en términos de maniobrabilidad y agilidad como la de los pilotos profesionales de drones. Para ello, el proyecto desarrollará algoritmos que combinarán las ventajas de las cámaras estándar y los sensores visión dinámica (DVS, por sus siglas en inglés). AGILEFLIGHT también desarrollará métodos novedosos que permitan maniobras ágiles por entornos abarrotados, desconocidos y dinámicos. Este proyecto podría ser beneficioso para trabajos de reacción en caso de catástrofe, entrega aérea y de inspección.
Objetivo
Drones are disrupting industries, such as agriculture, package delivery, inspection, and search and rescue. However, they are still either controlled by a human pilot or heavily rely on GPS for navigating autonomously. The alternative to GPS are onboard sensors, such as cameras: from the raw data, a local 3D map of the environment is built, which is then used to plan a safe trajectory to the goal. While the underlying algorithms are well understood, we are still far from having autonomous drones that can navigate through complex environments as good as human pilots. State-of-the-art perception and control algorithms are mature but not robust: coping with unreliable state estimation, low-latency perception, real-time planning in dynamic environments, and tight coupling of perception and action under severe resource constraints are all still unsolved research problems. Another issue is that, because battery energy density is increasing at a very slow rate, drones need to navigate faster in order to accomplish more within their limited flight time. To obtain more agile robots, we need faster sensors and low-latency processing.
The goal of this project is to develop novel scientific methods that would allow me to demonstrate autonomous, vision-based, agile quadrotor navigation in unknown, GPS-denied, and cluttered environments with possibly moving obstacles, which can be as effective in terms of maneuverability and agility as those of professional drone pilots. The outcome would not only be beneficial for disaster response scenarios, but also for other scenarios, such as aerial delivery or inspection. To achieve this ambitious goal, I will first develop robust, low-latency, multimodal perception algorithms that combine the advantages of standard cameras with event cameras. Then, I will develop novel methods that unify perception and state estimation together with planning and control to enable agile maneuvers through cluttered, unknown, and dynamic environments.
Ámbito científico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsautonomous robotsdrones
- agricultural sciencesagriculture, forestry, and fisheriesagriculture
Palabras clave
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
8006 Zurich
Suiza