Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917418672062464 |
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| author | Yan, Jingtian Li, Zhifei Kang, William Zheng, Kevin Zhang, Yulun Chen, Zhe Zhang, Yue Harabor, Daniel Smith, Stephen F. Li, Jiaoyang |
| author_facet | Yan, Jingtian Li, Zhifei Kang, William Zheng, Kevin Zhang, Yulun Chen, Zhe Zhang, Yue Harabor, Daniel Smith, Stephen F. Li, Jiaoyang |
| contents | We present Scalable Multi-Agent Realistic Testbed (SMART), a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms. MAPF focuses on planning collision-free paths for a group of robots. While state-of-the-art MAPF planners can plan paths for hundreds of robots in seconds, they often rely on simplified robot models, making their real-world performance unclear. Researchers typically lack access to hundreds of physical robots in laboratory settings to evaluate the algorithms. Meanwhile, industrial professionals who lack expertise in MAPF require an easy-to-use simulator to efficiently test and understand the performance of MAPF planners in their specific settings. SMART fills this gap with several advantages: (1) SMART uses physics-engine-based simulators to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF planners and robot models, and (3) SMART scales to thousands of robots. The code is publicly available at https://github.com/smart-mapf/smart with an online service available at https://smart-mapf.github.io/demo/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART) Yan, Jingtian Li, Zhifei Kang, William Zheng, Kevin Zhang, Yulun Chen, Zhe Zhang, Yue Harabor, Daniel Smith, Stephen F. Li, Jiaoyang Robotics Artificial Intelligence We present Scalable Multi-Agent Realistic Testbed (SMART), a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms. MAPF focuses on planning collision-free paths for a group of robots. While state-of-the-art MAPF planners can plan paths for hundreds of robots in seconds, they often rely on simplified robot models, making their real-world performance unclear. Researchers typically lack access to hundreds of physical robots in laboratory settings to evaluate the algorithms. Meanwhile, industrial professionals who lack expertise in MAPF require an easy-to-use simulator to efficiently test and understand the performance of MAPF planners in their specific settings. SMART fills this gap with several advantages: (1) SMART uses physics-engine-based simulators to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF planners and robot models, and (3) SMART scales to thousands of robots. The code is publicly available at https://github.com/smart-mapf/smart with an online service available at https://smart-mapf.github.io/demo/. |
| title | Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART) |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2503.04798 |