AAAI 2023technical0 citations
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
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.
BibTeX
@article{Chen_Wang_Zhang_Ding_Zhong_Pu_Zhang_2024, title={Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26951}, DOI={10.1609/aaai.v37i13.26951}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Feng and Wang, Chenghe and Zhang, Fuxiang and Ding, Hao and Zhong, Qiaoyong and Pu, Shiliang and Zhang, Zongzhang}, year={2024}, month={Jul.}, pages={16182-16183} }