In many unmanned aerial vehicle (UAV) applications, flexible trajectory generation algorithms are required to enable high levels of autonomy for critical mission phases, such as take-off, area coverage, and landing. In this paper, we present a guidance approach which uses the improved intrinsic tau guidance theory to create 4-D trajectories for the desired time-to-contact (TTC) with a landing platform tracked by a visual sensor. This allows us to perform maneuvers with tunable trajectory profiles, while catering for static or non-static starting and terminating motion states. We validate our method using a rotary-wing UAV to land on a static platform.Results from simulations and outdoor experiments show that our approach achieves smooth, accurate landings with easily adjustable parameters.
Improved Tau-Guidance and Vision-aided Navigation for Robust Autonomous Landing of UAVs
VETRELLA, AMEDEO RODI;FASANO, GIANCARMINE;ACCARDO, DOMENICO;
2017
Abstract
In many unmanned aerial vehicle (UAV) applications, flexible trajectory generation algorithms are required to enable high levels of autonomy for critical mission phases, such as take-off, area coverage, and landing. In this paper, we present a guidance approach which uses the improved intrinsic tau guidance theory to create 4-D trajectories for the desired time-to-contact (TTC) with a landing platform tracked by a visual sensor. This allows us to perform maneuvers with tunable trajectory profiles, while catering for static or non-static starting and terminating motion states. We validate our method using a rotary-wing UAV to land on a static platform.Results from simulations and outdoor experiments show that our approach achieves smooth, accurate landings with easily adjustable parameters.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
