Projektbeschreibung
Autonome Drohnennavigation, die menschlichen Piloten das Wasser reichen kann
Drohnen sind bisher noch weit davon entfernt, auf gleiche Weise durch komplexe Umgebungen zu navigieren wie menschliche Pilotinnen und Piloten. Um agilere Roboter zu schaffen, sind schnellere Sensoren und eine Verarbeitung mit geringer Latenz erforderlich. Das EU-finanzierte Projekt AGILEFLIGHT will innovative wissenschaftliche Methoden entwickeln, um die Navigation autonomer, sichtgestützter, agiler Quadrokopter in unbekannten und unübersichtlichen Umgebungen ohne GPS-Empfang und mit sich möglicherweise bewegenden Hindernissen zu demonstrieren. Ziel ist eine Navigation, die hinsichtlich Manövrierbarkeit und Wendigkeit genauso effektiv ist wie die von professionellen Drohnenpilotinnen und -piloten. Zu diesem Zweck werden im Rahmen des Projekts Algorithmen erstellt, welche die Vorteile von Standard- und Ereigniskameras kombinieren. Darüber hinaus sollen unter AGILEFLIGHT auch neuartige Methoden erarbeitet werden, die schwierige Manöver in unübersichtlichen, unbekannten und dynamischen Umgebungen ermöglichen. Diese Forschung könnte beispielsweise der Katastrophenhilfe, der Luftversorgung und Inspektionsarbeiten zugutekommen.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-COG - Consolidator GrantGastgebende Einrichtung
8006 Zurich
Schweiz