計画と制御

Design and tune a waypoint following controller for a fixed-wing UAV by using the Guidance Model and Waypoint Follower blocks.

シミュレーションでは多くの場合、さまざまな開発段階で異なる忠実度が必要になります。ラピッドプロトタイピング段階では、パラメーターの実験と調整を迅速に行い、さまざまな自律アルゴリズムをテストすることが好まれます。製造開発段階では、忠実度の高いモデルに対するアルゴリズムの検証を行います。この例では、Guidance Model ブロックによって高忠実度モデルを近似し、それを使用してウェイポイント追従ナビゲーション システムのプロトタイプ作成と調整を行う方法について説明します。Tuning Waypoint Follower for Fixed-Wing UAVを参照してください。高忠実度モデルに対して同じナビゲーション システムをテストし、そのパフォーマンスを検証しています。

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 3D motion of a fixed-wing UAV using the rapidly exploring random tree (RRT) algorithm given a start and goal pose.

市街地環境で小型マルチコプターの離陸、飛行、および着陸のシミュレーションを実装する。

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

Receive sensor readings and autopilot status from PX4® autopilot and send control commands to navigate a simulated UAV.

Simulate waypoint following mission with obstacles using the 3D VFH+ algorithm to compute obstacle-free direction.

Avoid collision with moving obstacles based on the velocity obstacle concept by using radar detections.

Use a UAV with lidar sensor to create a 3D occupancy map for motion planning.

Tune quadcopter attitude and position PID controllers by 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 3D minimum snap trajectory for a multirotor UAV from a start to a goal pose by using the RRT* path planner.

Tune the controllerVFH3D System object™ to navigate a multirotor UAV in an obstacle environment using a lidar sensor.

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.