planning and control -凯发k8网页登录

guidance models and navigation, trajectory and waypoint following

plan and execute uav flights using guidance motion models for fixed-wing and multirotor uavs. fly predefined missions using waypoint and trajectory-following algorithms. for an example using an rrt path planner that plans and simulates a flight in a city setting, see motion planning with rrt for fixed-wing uav.

functions

guidance and control

	guidance model for fixed-wing uavs
	guidance model for multirotor uavs
	control commands for uav
	time derivative of uav states
	environmental inputs for uav
	uav state vector

waypoint following

	follow waypoints for uav
	orbit location of interest using a uav

path management

	compute and execute a uav autonomous mission
	avoid obstacles using 3d vector field histogram
	dubins path connection for uav
	dubins path segment connecting two poses of uav
	connect poses with uav dubins connection path
	interpolate poses along uav dubins path segment

blocks

	orbit location of interest using uav
	follow waypoints for uav
	reduced-order model for uav
	animate uav flight path using translations and rotations
	compute and execute a uav autonomous mission
	integrate fourth- or sixth-order point mass equations of motion in coordinated flight
	compute obstacle-free direction using range sensor data and target position

featured examples

approximate high-fidelity uav model with uav guidance model block

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.

transition from low- to high-fidelity uav models in three stages

evolve your uav plant model continuously to stay in sync with the latest information available.

motion planning with rrt for fixed-wing uav

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.

uav package delivery

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