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Target-Tracking-Simulator

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Urban Environment Simulator Code for Testing your Target Tracking Algorithms.

// repository documentation

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# Target Tracking Urban Environment Simulator

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This repository contains code for urban environment simulator that can be used to test Target Tracking algorithms. It has functionalities for dynamic target and cylindrical obstacles.

This work is a part of paper <a href="https://arxiv.org/abs/2007.10934">"UAV Target Tracking in Urban Environments using Deep Reinforcement Learning"</a> published at The 2020 International Conference on Unmanned Aircraft Systems, Athens, Greece. In case you find any of this useful consider citing our work.

```
@article{Bhagat2020UAVTT,
  title={UAV Target Tracking in Urban Environments Using Deep Reinforcement Learning},
  author={Sarthak Bhagat and P. B. Sujit},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.10934}
}
```

## Contents

- `animation_2d.py`: Simulate the environment in x-y plane.
- `animation_3d.py`: Simulate the environment in x-z plane.
- `create_map.py`: Create customisable map for the environment. 
- `args.py`: Arguments that are used by all other scripts.

## Testing your Algorithms using this Simulator

- Install all required libraries. For this simply run the following command.
```
pip install -r requirements.txt
```

- Add your target-tracking algorithm code in the same directory.

That file must read the current state of the target from the file `state_car.txt`, which could be used to determine the effectiveness of the choosen action by your algotithm. You may use the snippet given below for reading the file.

```
file = open("state_car.txt", "r")
car_state = file.read().split()
car_state = list(map(float, car_state))
```

It also must write the state of the drone obtained by performing the action suggested by your algorithm. You could use the following snippet for that. Here, `time_step_drone` is the step counter which is incremented whenever an action is taken by the agent.

```
file = open("state_drone.txt", "w")
file.write(str(state_x) + ' ' + str(state_y) + ' ' + str(state_z) + ' ' + str(time_step_drone))
file.close()
```

- Change the position, radius, height and number of obstacles based on your requirements. This can be done by changing line 44-46 in `animation_2d.py` and line 45-47 in `animation_3d.py`.

For example, for setting the position as p_x and p_y, radius as r and height as h for an obstacle, make the following additions to `animation_2d.py` and `animation_3d.py` respectively.

```
obstacle_name = patches.Circle((p_x, p_y), radius=r, fc='g')

obstacle_name = patches.Rectangle((p_x, 0), 2*r, h, fc='g') 
```

We could also change the field of view of the agent (FOV) by changing line 59 in `animation_2d.py`. Say, we want to make the radius of field of view as fov_radius, just make the following change.

```
fov_circle = patches.Circle((0, 0), radius=fov_radius, fc=None, ec='k', alpha=0.3)
```

- Add or change the arguments of your choice in `args.py` file. You may also want to change the plotting configurations placed immediately after the import statements in all animation files.

- Finally, for simulating an episode using actions suggested by your algorithm, run the following commands in seperate terminals.

```
python animation_2d.py
python animation_3d.py
python your_target_tracking_algorithm_script.py
```

## Screenshot of Environment

Below is a screenshot of the environment developed by the `animation_2d.py` script. Here, red circle depicts the agent, yellow circle depicts the target, blue circle depicts the FOV and green ones depict the obstacles.

![figure](https://raw.githubusercontent.com/sarthak268/Target-Tracking-Simulator/master/environment.png)

---
### Contact

If you face any problem in running this code, you can contact us at sarthak16189@iiitd.ac.in.

### License

For license information, see LICENSE or http://mit-license.org

Target-Tracking-Simulator

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