Li, J., Surynek, P., Felner, A., Ma, H., Koenig, S.: Multi-agent path finding for large agents. In: AAAI. AAAI Press (2019)Li et al. 2019] Li, J.; Surynek, P.; Felner, A.; and Ma, H. 2019. Multi-agent path finding for large agents. In AAAI.
Large-scale conflict of paths is a reason which can hugely reduce the success rate for multi-agent path finding (MAPF) in dense scenarios. For this problem, a bargaining game based improving hierarchical cooperation A* (B-IHCA*) alg
Li, J., Surynek, P., Felner, A., Ma., H., Kumar, T.K.S., Koenig, S.: Multi-agent path finding for large agents. In: AAAI Conference on Artificial Intelligence (2019) Google Scholar Liu, M., Ma, H., Li, J., Koenig, S.: Task and path planning for multi-agent pickup an...
Kumar, S. Koenig, Multi-agent path finding for large agents, in: Proc. AAAI, 2019, pp. 7627–7634. Google Scholar [14] P. Surynek, Multi-agent path finding with continuous time and geometric agents viewed through satisfiability modulo theories (SMT), in: Proc. SOCS, 2019, pp. 200–...
具体来说,多子集架构采用了一个路径内注意力机制(Intra-path Attention Mechanism),该机制与3D卷积交替进行,能够有效捕捉路径级别的交互信息。该架构的工作流程如下: 路径内注意力机制: 路径内注意力使得信息可以在智能体的整个路径上流动,从而捕捉到路径级别的信息。
多智能体路径规划 (Multi-Agent Path Finding, MAPF) 研究多智能体的路径规划算法,为多机系统规划无冲突的最优路径. 本项目将多机路径规划算法(Multi-Agent Path Finding, MAPF)源码转换为ros实现.算法接口采用ros插件形式编写,利于扩展自己的多机规划方法. 本项目仓库: (如果对您有帮助的话给仓库右上角留下一...
Multi-agent path finding for large agents. Proc AAAI Conf Artif Intell. 2019;33(01):7627–34. https://doi.org/10.1609/aaai.v33i01.33017627. SN Computer Science 83 Page 20 of 20 SN Computer Science (2023) 4:83 14. Li J, Ran M, Xie L. ...
k: the number of agents t: the runtime limit suboptimality: the suboptimality factor w You can find more details and explanations for all parameters with: ./eecbs --help To test the code on more instances, you can download the MAPF instances from theMAPF benchmark. In particular, the ...
Theassignfunction returns a list ofAgent, seeagent.pyfor more details. Return: Anumpy.ndaarraywith shape(N, L, 2)withNbeing the number of agents (i.e. # of start coordinates) andLbeing the length of the path. To get the path of the first agent (i.e. the agent with the first ...
Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning has been introduced to solve the partially observable variant of MAPF by ...