scholarly journals Multi-Level Partitioning and Distribution of the Assignment Problem for Large-Scale Multi-Robot Task Allocation

Author(s):  
Lantao Liu ◽  
Dylan Shell
2021 ◽  
Vol 12 (1) ◽  
pp. 272
Author(s):  
Bumjin Park ◽  
Cheongwoong Kang ◽  
Jaesik Choi

This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.


2006 ◽  
Vol 13 (5) ◽  
pp. 548-551 ◽  
Author(s):  
Ping-an Gao ◽  
Zi-xing Cai

2021 ◽  
Author(s):  
Ayan Dutta ◽  
Vladimir Ufimtsev ◽  
Tuffa Said ◽  
Inmo Jang ◽  
Roger Eggen

Sign in / Sign up

Export Citation Format

Share Document