Designs a waypoint following controller for a fixed-wing unmanned aerial vehicle (UAV). The UAV Guidance Model and UAV Waypoint Follower blocks are the main components that simulate the UAV guidance model and generate commands for following waypoints.

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.

Evolve your UAV plant model continuously to stay in sync with the latest information available.

Generate a random 3-D occupancy map by automatically adding the desired number of obstacles of varying dimensions at random positions on the map. You can then use the generated map for UAV motion planning using algorithms like RRT, RRT* and Hybrid A*.

Plan the motion 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.

Implement a small multicopter simulation to takeoff, fly, and land in a city environment.

Demonstrates enabling and interfacing onboard computer path planning with PX4 Software-in-the-Loop (SITL).

Demonstrates how to receive sensor readings and autopilot status from a simulated UAV with PX4 autopilot, and send control commands to navigate the simulated UAV.

This model implements waypoint following along with obstacle avoidance on a UAV in a simulated scenario. The model takes a set of waypoints and uses the 3D VFH+ algorithm to provide an obstacle-free path.

Demonstrates how to avoid collision with moving obstacles based on the velocity obstacle concept [1].The uavScenario used in this example is based on the "Simulate Radar Sensor Mounted on UAV" example, which shows how to generate track detections of moving UAVs close to the ego vehicle.

Demonstrates how to use a UAV, equipped with a lidar sensor, to map an environment in a 3D occupancy map. That generated occupancy map can then be used to execute motion planning. The UAV follows a specified trajectory through the environment. Knowledge of the position of the UAV and the point clouds obtained at corresponding locations are used to build a map of the environment. The pose of the UAV is assumed to be completely known throughout the mapping flight; this is also known as mapping with known poses. The generated map is used for motion planning. The goal of the motion planning is to plan the path of a UAV in an apartment complex where it is used to drop off packages, delivered at the gate, to a desired rooftop location within the complex.

Tune PID Controllers used in the attitude and position control of a small quadcopter in only one simulation using the Closed-Loop PID Autotuner block.

Use Control System Tuner to tune the fixed-structure PID controllers of a multicopter for nominal flight conditions and fault conditions. Here, you use a gain-scheduled approach to recover from a single rotor failure and land the UAV.

Plan a minimum snap trajectory (minimum control effort) for a multirotor unmanned aerial vehicle (UAV) from a start pose to a goal pose on a 3D map by using the optimized rapidly exploring random tree (RRT*) path planner.

Tune the parameters of the controllerVFH3D System object™ to navigate a multirotor UAV in an obstacle environment using a lidar sensor. Use the uavScenario object to create the obstacle environment. Mount a lidar sensor on the multirotor UAV to get the positions of obstacles. Use the uavWaypointFollower System object to set the goal location for the controllerVFH3D object. Use a PID controller to move the UAV in the desired obstacle-free direction.

Plan and customize coverage path for a UAV to survey a geographical region.

Generate a minimum jerk trajectory using the Minimum Jerk Polynomial Trajectory block.

Generate a minimum snap trajectory using the Minimum Snap Polynomial Trajectory block.