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---
license: mit
Modalities: Pickle
Formats: pkl
Size: <500MB
libraries: Datasets
---
# UAV Path Planning Benchmark Suite
This dataset provides a collection of 56 benchmark instances for testing and evaluating global optimization algorithms in the context of Unmanned Aerial Vehicle (UAV) path planning. It is based on the benchmark proposed in the paper:
> **Benchmarking Global Optimization Techniques for Unmanned Aerial Vehicle Path Planning**
> [arXiv:2501.14503](https://arxiv.org/abs/2501.14503)
The original implementation in MATLAB is available at:
👉 [Zenodo Record (MATLAB Code)](https://zenodo.org/records/12793991)
We reimplemented the benchmark in Python using `matlab.engine`, converting the original `Model56.mat` file into a Python-friendly `.pkl` format for easy loading and experimentation.
---
## 📦 Dataset Overview
### Problem Suite Composition
- A total of **56 benchmark instances** are included.
- These instances are selected from a pool of **5,000 automatically generated terrains**, with **56 diverse terrains** manually curated for their challenging features and realistic complexity.
- Each terrain may include:
- Flat plains
- Rolling hills
- Steep slopes
- Deep valleys
- Two **obstacle density configurations** are used:
- **Sparse scenario:** 15 cylindrical threats
- **Dense scenario:** 30 cylindrical threats
- Obstacles are modeled as **cylinders with varying heights and radii**, simulating real-world structures such as radar towers or missile defense units.
- The UAV must navigate from a **start point** to a **goal point**, each placed at opposite corners of the map.
- Both the start and goal are located in safe, threat-free regions to ensure feasible solution spaces.
### Instance Composition
- Each of the **28 selected terrains** is evaluated under **2 obstacle configurations**, resulting in:
```
28 terrains × 2 threat settings = 56 total benchmark instances
```
---
## 🔧 Format & Usage
- Each instance is stored in a Python pickle (`.pkl`) format, containing:
- Terrain elevation grid
- Obstacle locations and sizes
- Start and goal coordinates
- The dataset is suitable for benchmarking global optimization, black-box optimization (BBO), and reinforcement learning approaches to 3D path planning under constraints.
---
## 🖼️ Example Visualization: Terrain53
Below is an example of one benchmark instance (`Terrain53`) under the sparse threat scenario.
<p align="center">
<img src="img/terrain53.png" alt="Terrain53 3D View" width="45%" style="display: inline-block; margin-right: 10px;">
<img src="img/terrain53_view.png" alt="Terrain53 2D Projection" width="45%" style="display: inline-block;">
</p>
- **Left:** 3D terrain surface with elevation and cylindrical threats (obstacles).
- **Right:** 2D top-down projection with start (bule) and goal (yellow) points visualized.
---
## 📄 Citation
If you use this dataset in your research, please cite the original paper:
```
@article{shehadeh2025benchmarking,
title={Benchmarking global optimization techniques for unmanned aerial vehicle path planning},
author={Shehadeh, Mhd Ali and Kudela, Jakub},
journal={arXiv preprint arXiv:2501.14503},
year={2025}
}
``` |