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Autonomous Landing Simulation for Multirotor UAVs

Example Landing

Project Overview

This project involves the development of a simulation model for the autonomous landing of a multirotor unmanned aerial vehicle (UAV) on a moving landing platform. The simulation utilizes Aruco fractal marker pose estimation for precise landing.

Dependencies

  1. PX4 Autopilot (v1.13.3)
  2. Gazebo
  3. ROS1
  4. OpenCV
  5. Aruco Library

Repository Structure

Config

  • camera_calibration/ - Contains calibration files for the camera used in the simulation.
  • drone_fsm.yaml - Configuration for the drone finite state machine.
  • landing_controller.yaml - Configuration for the landing controller.
  • aruco_estimator.yaml - Configuration for Aruco marker estimation.
  • ugv_control.yaml - Configuration for the unmanned ground vehicle.

Launch

  • drone_fsm.launch - Launch file for the drone finite state machine.
  • landing_controller.launch - Launch file for the landing controller.
  • aruco_estimator.launch - Launch file for the Aruco marker estimator.
  • spawn_husky.launch - Launch file for spawning the Husky model.
  • spawn_iris.launch - Launch file for spawning the Iris model.
  • ugv_control.launch - Launch file for UGV control.
  • uav_landing.launch - Main launch file for the UAV landing simulation.

Scripts

Drone

  • drone_fsm.py - Script for the drone finite state machine.
  • landing_controller.py - Script for the landing controller.

Positioning

  • aruco_estimator.py - Script for Aruco marker estimation.

UGV

  • ugv_control.py - Script for UGV control.

Worlds

  • sim_windy_world/ - Gazebo world files for simulating windy conditions.
  • sim_world/ - General Gazebo world files.

Simulation Details

The simulation employs the following technologies for accurate landing:

  • Aruco Fractal Marker Pose Estimation: Utilizes Aruco fractal markers to determine the precise position and orientation of the landing platform.

References

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precise autonomous landing of a multirotor UAV on a moving platform using Aruco fractal markers.

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  • Python 82.2%
  • C++ 15.1%
  • CMake 2.7%