| Name | autonav JSON |
| Version |
1.0.3
JSON |
| download |
| home_page | None |
| Summary | Research software for navigating a UAV in indoor environments |
| upload_time | 2024-05-18 15:03:03 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
uav
navigation
indoor
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://codecov.io/gh/ricardo-s-santos/AutoNAV)
[](https://ricardo-s-santos.github.io/AutoNAV/)
[](https://pypi.org/project/autonav/)
<p align="center">
<img src="https://github.com/ricardo-s-santos/AutoNAV/blob/main/docs/docs/figures/icon.png?raw=true" alt="image" width="200" height="auto">
</p>
A Python package for simulating UAV Navigation in Satellite-Less Environments. The package contains two algorithms the GTRS <a href="https://ieeexplore.ieee.org/document/9456863">[1]</a> and WLS <a href="https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/wss2.12041">[2]</a> whose goal is to estimate and navigate a UAV.
## Installation
Install from PyPI:
```sh
pip install --upgrade pip
pip install autonav
```
## First Steps
After installing the package one can import the algorithms and necessary dependencies as follows:
```python
import matplotlib.pyplot as plt
from autonav import gtrs, wls
from autonav.file_handlers import readpathfile
from autonav.plots import plot_trajectories
from numpy import array
```
Afterwards, one can create the necessary values to run the algorithms:
```python
# Area border
b = 200
# Number of anchors
n = 8
# Position of the anchors
a_i = array(
[
[0, 0, 0],
[0, b, 0],
[b / 2, 0, 0],
[b / 2, b, 0],
[0, 0, b / 8],
[0, b, b / 8],
[b / 2, 0, b / 8],
[b / 2, b, b / 8],]
).T
# Number of measurement samples
k = 50
# Maximum velocity allowed to the UAV
v_max = b / 100
# Distance threshold
tau = b / 50
# Smoothing factor
gamma = b / 100
# Initial position of the UAV
initial_uav_position = [10, 10, 5]
# File containing the waypoints
destinations = readpathfile("docs/docs/examples/Path.csv")
# Noise seed
noise_seed = 1
# Noise Distribution
noise_distribution = "normal"
# Noise distribution parameters
mean = 0
std = 1
noise_distribution_parameters = [mean, std]
```
Finally, run the GTRS or WLS algorithm and plot the trajectories:
```python
# Estimate the positions and trajectory using the GTRS algorithm
[estimated_positions, true_trajectory] = gtrs(
a_i,
n,
k,
destinations,
initial_uav_position,
v_max,
tau,
gamma,
noise_seed,
noise_distribution,
noise_distribution_parameters,
)
# Plot the trajectory that the UAV followed
plot_trajectories(destinations, [true_trajectory], a_i, ['GTRS'])
plt.show()
```
<p align="center">
<img src="https://github.com/ricardo-s-santos/AutoNAV/blob/main/docs/docs/figures/trajectories_plot.png?raw=true" alt="image" width="auto" height="auto">
</p>
## References
[1] J. P. Matos-Carvalho, R. Santos, S. Tomic and M. Beko, "GTRS-Based Algorithm for UAV Navigation in Indoor Environments Employing Range Measurements and Odometry," in IEEE Access, vol. 9, pp. 89120-89132, 2021, doi: 10.1109/ACCESS.2021.3089900. https://ieeexplore.ieee.org/document/9456863
[2] R. Santos, J. P. Matos-Carvalho, S. Tomic and M. Beko, "WLS algorithm for UAV navigation in satellite‐less environments," in IET Wireless Sensor Systems, 2022, 12, (3-4), p. 93-102, DOI: 10.1049/wss2.12041
IET Digital Library, https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/wss2.12041
## License
[MIT License](LICENSE.txt)
Raw data
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