Goal-oriented obstacle avoidance for multirotor UAVs (Master Thesis project)
This is a reactive, mapless obstacle avoidance algorithm for multirotor Unmanned Aerial Vehicles (UAVs).
The algorithm applies image processing techniques and qualitative evaluations on a depth map acquired by the UAV in order to assess the reachability of the destination, and establishes an alternative path whenever obstacles are detected.
The algorithm is not a local path planner in the strict sense of the word, since we do not prove the optimality of the resulting path.
The current implementation is a set of Python3 scripts which targets a
V-REP simulation.
Example scenarios are stored in the vrep_scenes directory.
The scripts require opencv-python. On most *NIX systems, a simple
pip3 install opencv-python
should be enough. Please refer to the opencv-python documentation for detailed setup instructions for Lunux/MacOS and Windows.
After cloning the repository, the user should put the relevant V-REP API libraries in its main directory (follow the instructions here).
V-REP must also be allowed to communicate on a TCP port (default 11111).
Then:
- Start the simulation inside V-REP;
- Start the
main.pyscript withpython3 main.py.
At the moment the implementation does not support the "wall-following" scenario, i.e. a situation where most of the field of view is obstructed by an obstacle. This situation requires a different algorithm, such as the one proposed in [2].
[1] S. Hrabar, “Reactive obstacle avoidance for rotorcraft UAVs,” IEEE Int. Conf. Intell. Robot. Syst., no. August, pp. 4967–4974, 2011.
[2] T. Merz and F. Kendoul, “Beyond visual range obstacle avoidance and infrastructure inspection by an autonomous helicopter,” IEEE Int. Conf. Intell. Robot. Syst., no. August 2016, pp. 4953–4960, 2011.