Improved Tau-Guidance and Vision-aided Navigation for Robust Autonomous Landing of UAVs

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.