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This project involves designing an intelligent landing system for an unmanned aerial vehicle (UAV) that can land on a slow-moving target. It involves detecting a slow moving landing marker using vision based systems. CoppeliaSim and Python have been used in this project.

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Quadcopter-Autonomous-Landing

This is a simulation of autonomous landing system for a UAV on a slow-moving target, handling target search, heading angle estimation, target state estimation, and velocity control without human intervention or communication with the landing marker. The simulation was developed using CoppeliaSim and Python. The UAV and landing marker operate independently. This approach ensures the UAV can land accurately even on a moving target.

In order to run the simulation, install CoppeliaSim EDU. CoppeliaSim is a robot simulator software developed by Coppelia Robotics. It is freely available for academic use.

Open your terminal and install the following libraries :

pip install cbor2 numpy opencv-python scikit-image pykalman

Ensure that both the python code folder and the CoppeliaSim folder are in the SAME folder.
Now, open the test.py file and the UAV landing model.ttt file. Run the test.py file. The simulation should start running automatically.

Pictures from the simulation -

Simulation Env

Simulation Environment

Quadcopter

Quadcopter with Home position

Landing Marker Detected

Landing marker detected using opencv

Coordinate Transformation

Coordinate transformation from image plane to world frame

Approach

Approaching landing marker

About

This project involves designing an intelligent landing system for an unmanned aerial vehicle (UAV) that can land on a slow-moving target. It involves detecting a slow moving landing marker using vision based systems. CoppeliaSim and Python have been used in this project.

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