---
language:
- en
license: mit
pretty_name: ESARBench
task_categories:
- robotics
tags:
- uav
- embodied-ai
- search-and-rescue
- mllm
---

# ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue

[**Project Page**](https://4amgodvzx.github.io/ESAR.github.io) | [**Paper**](https://huggingface.co/papers/2605.01371) | [**GitHub**](https://github.com/4amGodvzx/ESAR)

**ESARBench** is a comprehensive benchmark designed to evaluate Multimodal Large Language Model (MLLM) driven UAV agents in highly realistic **Embodied Search and Rescue (ESAR)** scenarios. It requires aerial agents to autonomously explore complex environments, identify rescue clues, and reason about victim locations to execute informed decision-making.

## Key Features
- **High Fidelity**: 4 large-scale environments built with UE5 + AirSim using real-world GIS data.
- **Realistic Dynamics**: Integrated simulations of weather, lighting, and diverse rescue clues.
- **Task Diversity**: 600 tasks modeled after real-world rescue cases.
- **Evaluation**: A robust set of metrics to evaluate spatial memory, aerial adaptation, and search efficiency.

## Quick Start

### Installation

1. Create a Python 3.9 environment using Conda:
   ```bash
   conda create -n esar_env python=3.9
   conda activate esar_env
   ```
2. Install the required Python packages:
   ```bash
   pip install numpy msgpack-rpc-python pandas scipy dashscope opencv-python scikit-image scikit-fmm ultralytics matplotlib transformers pillow
   ```
3. Clone and install the AirSim-Colosseum client:
   ```bash
   git clone https://github.com/CodexLabsLLC/Colosseum.git
   cd Colosseum/PythonClient
   pip install -e . --no-build-isolation
   ```

### Running the Benchmark

The platform scripts load sample tasks by default. To run the benchmark, navigate to the `source` directory and run the platform script corresponding to your OS:

- **Linux**
```bash
cd source
python main_platform_linux.py
```
- **Windows**
```bash
cd source
python main_platform_win.py
```

### Analyzing Results
Each task writes a log file to `log/task_<task_id>_log.json`. You can analyze the results using:
```bash
cd source
python result_analysis.py
```

## Citation
If you use ESARBench in your research, please cite:
```bibtex
@article{esarbench2024,
  title={ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue},
  author={Authors list here},
  journal={arXiv preprint arXiv:2605.01371},
  year={2024}
}
```

## License
This project is licensed under the MIT License.