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SPACE Simulator Documentation
=============================
Welcome to the official documentation for **SPACE** (Swarm Planning And Control Evaluation) Simulator.
This simulator is designed to facilitate research and development in swarm robotics and decentralized decision-making. Below are the key features of the SPACE simulator:
Key Features
------------
- **Swarm Robotics Focus**: Optimized for swarm robotics research, supporting large-scale simulations with a lightweight design.
- **Behavior Trees**: Uses behavior trees to define and manage agent behaviors, allowing for flexible and structured decision-making.
- **Groot2 Integration**: Compatible with `Groot2 `_ for visualizing and editing the agents' behavior trees, enhancing ease of use and analysis.
- **Flexible Configuration**: Easily configurable via a YAML file, enabling customization without modifying the simulator’s source code.
- **Custom Plugins**: Integrates custom decision-making algorithms as plugins, allowing for tailored testing and experimentation.
- **Local Communication and Awareness**: Agents communicate locally within specified radii and maintain situational awareness based on their situational awareness ranges.
- **Dynamic Task Generation**: Supports the creation of tasks dynamically during the simulation, adapting to evolving scenarios.
- **Algorithm Comparison**: Facilitates the comparison of different decision-making algorithms within a consistent simulation environment and supports Monte Carlo tests for statistical analysis.
For detailed installation instructions, please refer to the :doc:`Installation Guide `.
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* - .. figure:: assets/GRAPE_20_agents_200_tasks.gif
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**Figure 1:** GRAPE with 20 agents and 200 tasks.
- .. figure:: assets/GRAPE_500_agents_10_tasks.gif
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**Figure 2:** GRAPE with 500 agents and 10 tasks.
Citations
------------
Please cite this work in your papers!
- `Inmo Jang, "SPACE: A Python-based Simulator for Evaluating Decentralized Multi-Robot Task Allocation Algorithms", arXiv:2409.04230 [cs.RO], 2024 `_
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