Designs a waypoint following controller for a fixed-wing unmanned aerial vehicle (UAV) using the UAV Guidance Model and UAV Waypoint Follower blocks.

Simulation models often need different levels of fidelity during different development stages. During the rapid-prototyping stage, we would like to quickly experiment and tune parameters to test different autonomous algorithms. During the production development stage, we would like to validate our algorithms against models of increasing fidelities. In this example, we demonstrate a method to approximate a high-fidelity model with the UAV Guidance Model block and use it to prototype and tune a waypoint following navigation system. See Tuning Waypoint Follower for Fixed-Wing UAV. The same navigation system is tested against a high-fidelity model to verify its performance.

Demonstrates motion planning of a fixed-wing unmanned aerial vehicle (UAV) using the rapidly exploring random tree (RRT) algorithm given a start and goal pose on a 3-D map. A fixed-wing UAV is nonholonomic in nature, and must obey aerodynamic constraints like maximum roll angle, flight path angle, and airspeed when moving between waypoints.

Through incremental design iterations how to implement a small multicopter simulation to takeoff, fly, and land at a different location in a city environment.