Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)

Feng Chen, Chenghe Wang, Fuxiang Zhang, Hao Ding, Qiaoyong Zhong, Shiliang Pu, Zongzhang Zhang

AAAI 2023 pp. 16182-16183

doi:10.1609/AAAI.V37I13.26951 /aaai/2023/chen2023aaai-deployment/

Abstract

Multi-agent pathfinding (MAPF) is essential to large-scale robotic coordination tasks. Planning-based algorithms show their advantages in collision avoidance while avoiding exponential growth in the number of agents. Reinforcement-learning (RL)-based algorithms can be deployed efficiently but cannot prevent collisions entirely due to the lack of hard constraints. This paper combines the merits of planning-based and RL-based MAPF methods to propose a deployment-efficient and collision-free MAPF algorithm. The experiments show the effectiveness of our approach.

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Cite

Text

Chen et al. "Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26951

Markdown

[Chen et al. "Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/chen2023aaai-deployment/) doi:10.1609/AAAI.V37I13.26951

BibTeX

@inproceedings{chen2023aaai-deployment,
  title     = {{Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)}},
  author    = {Chen, Feng and Wang, Chenghe and Zhang, Fuxiang and Ding, Hao and Zhong, Qiaoyong and Pu, Shiliang and Zhang, Zongzhang},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16182-16183},
  doi       = {10.1609/AAAI.V37I13.26951},
  url       = {https://mlanthology.org/aaai/2023/chen2023aaai-deployment/}
}