A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation

Joint Authors

Hussein, Ahmed
Khamis, Alaa M.
Badreldin, Mohamed

Source

Advances in Artificial Intelligence

Issue

Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2013-11-12

Country of Publication

Egypt

No. of Pages

11

Main Subjects

Information Technology and Computer Science
Science

Abstract EN

This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS).

The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks.

The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots.

Finally, a comparative study is implemented between the two approaches and the results show that the optimization-based approach outperforms the market-based approach in terms of optimal allocation and computational time.

American Psychological Association (APA)

Badreldin, Mohamed& Hussein, Ahmed& Khamis, Alaa M.. 2013. A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation. Advances in Artificial Intelligence،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-457876

Modern Language Association (MLA)

Badreldin, Mohamed…[et al.]. A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation. Advances in Artificial Intelligence No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-457876

American Medical Association (AMA)

Badreldin, Mohamed& Hussein, Ahmed& Khamis, Alaa M.. A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation. Advances in Artificial Intelligence. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-457876

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-457876