Descrizione del progetto
Verso una navigazione autonoma dei droni paragonabile a quella dei piloti umani
I droni non sono ancora in grado di navigare in ambienti complessi, come invece fanno i piloti umani. La realizzazione di robot più agili richiede sensori più veloci e un’elaborazione a bassa latenza. Il progetto AGILEFLIGHT, finanziato dall’UE, intende sviluppare metodi scientifici innovativi per dimostrare una navigazione del quadricottero agile, autonoma, basata sulla visione in ambienti sconosciuti, senza segnale GPS e affollati, con possibili ostacoli in movimento. L’obiettivo è quello di ottenere una navigazione altrettanto efficace in termini di manovrabilità e agilità, analoga a quella dei piloti di droni professionisti. A tal fine, il progetto svilupperà algoritmi che combinano i vantaggi delle telecamere convenzionali con quelli delle telecamere per eventi. AGILEFLIGHT svilupperà anche nuovi metodi che consentono manovre agili attraverso ambienti affollati, sconosciuti e dinamici. Questo lavoro potrebbe favorire la risposta alle catastrofi, la consegna aerea e il lavoro di ispezione.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-COG - Consolidator GrantIstituzione ospitante
8006 Zurich
Svizzera