To be able to recover a fixed-wing unmanned aerial vehicle (UAV) on a small space like a boat deck or a glade in the forest, a steep and precise descent is needed. One way to reduce the speed of the UAV during landing is by performing a deep-stall landing manoeuvre, where the lift of the UAV is decreased until it is unable to keep the UAV level, at the same time as the drag is increased to minimize the speed of the UAV. However, this manoeuvre is highly non-linear and nontrivial to perform with high precision. To solve this, an on-line non-linear model predictive controller (NMPC) is implemented to guide the UAV in the landing phase, receiving inputs from the autopilot and guiding the UAV using pitch and throttle references. The UAV is guided along a custom path to a predefined deepstall landing start point and performs a guided deep-stall. The simulation results show that the NMPC guides the UAV in a deep-stall landing with good precision and low speed, and that the results depend on a correct prediction model for the controller.
Autonomous airdrop is a useful basic operation for a fixed-wing unmanned aerial system. Being able to deliver an object to a known target position extends operational range without risking human lives, but is still limited to known delivery locations. If the fixed-wing unmanned aerial vehicle delivering the object could also recognize its target, the system would take one step further in the direction of autonomy. This paper presents a closed-loop autonomous delivery system that uses machine vision to identify a target marked with a distinct colour, calculates the geographical coordinates of the target location and plans a path to a release point, where it delivers the object. Experimental results present a visual target estimator with a mean error distance of 3.4 m and objects delivered with a mean error distance of 5.5 m.
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