article · 2008

An Optimized Multirobot Task Allocation

Bibhuti Bhusan Choudhury, Bibhuti Bhusan Biswal

Cited by 3Abstract and details only

Abstract

Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. One of the most important aspects in the design of MRS is the allocation of tasks among the robots in a productive and efficient manner. Optimal solutions to multirobot task allocation (MRTA) can be found through an exhaustive search. Since there are ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible. Task allocation methodologies must ensure that not only the global mission is achieved, but also the tasks are well distributed among the robots. This paper presents task allocation methodologies for MRS by considering their capability in terms of time and space. A two-phase solution methodology is used to solve the MRTA problem wherein the task capacity of the robots is determined during the first phase and the task allocation optimization is done during the second phase using linear programming (LP).

Cite this paper

APA 7

Choudhury, B. B., & Biswal, B. B. (2008). An Optimized Multirobot Task Allocation. https://doi.org/10.1109/icetet.2008.111

MLA 9

Choudhury, Bibhuti Bhusan, and Bibhuti Bhusan Biswal. “An Optimized Multirobot Task Allocation.” 2008, pp. 320–25, https://doi.org/10.1109/icetet.2008.111.

Chicago

Choudhury, Bibhuti Bhusan, and Bibhuti Bhusan Biswal. 2008. “An Optimized Multirobot Task Allocation.” https://doi.org/10.1109/icetet.2008.111.

Harvard

Choudhury, B.B. and Biswal, B.B. (2008) ‘An Optimized Multirobot Task Allocation’. Available at: https://doi.org/10.1109/icetet.2008.111.

BibTeX

@article{choudhury2008optimized,
  title = {{An Optimized Multirobot Task Allocation}},
  author = {Choudhury, Bibhuti Bhusan and Biswal, Bibhuti Bhusan},
  pages = {320--325},
  year = {2008},
  doi = {10.1109/icetet.2008.111}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.