2022
DOI: 10.1155/2022/2056617
|View full text |Cite
|
Sign up to set email alerts
|

Multirobot Adaptive Task Allocation of Intelligent Warehouse Based on Evolutionary Strategy

Abstract: To solve the dynamic and real-time problem of multirobot task allocation in intelligent warehouse system under parts-to-picker mode, this paper presents a combined solution based on adaptive task pool strategy and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm. In the first stage of the solution, a variable task pool is used to store dynamically added tasks, which can dynamically divide continuous and large-scale task allocation problems into small-scale subproblems to solve them to meet… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 19 publications
0
1
0
Order By: Relevance
“…In [ 106 ], Yifan dealt with the topic of real-time task allocation in a smart warehouse system; a solution of the covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm and a group task strategy were presented. The first step was to store random tasks so that their systematization and allocation was dynamic, having the opportunity to branch a large task so that the task division was fair and solvable in a more efficient way.…”
Section: Carrymentioning
confidence: 99%
“…In [ 106 ], Yifan dealt with the topic of real-time task allocation in a smart warehouse system; a solution of the covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm and a group task strategy were presented. The first step was to store random tasks so that their systematization and allocation was dynamic, having the opportunity to branch a large task so that the task division was fair and solvable in a more efficient way.…”
Section: Carrymentioning
confidence: 99%