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Local ant system for allocating robot swarms to time-constrained tasks

Yara Khaluf (UGent) , Seppe Vanhee and Pieter Simoens (UGent)
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Abstract
We propose a novel application of the Ant Colony Optimization algorithm to efficiently allocate a swarm of homogeneous robots to a set of tasks that need to be accomplished by specific deadlines. We exploit the local communication between robots to periodically evaluate the quality of the allocation solutions, and agents select independently among the high-quality alternatives. The evaluation is performed using pheromone trails to favor allocations which minimize the execution time of the tasks. Our approach is validated in both static and dynamic environments (i.e. the task availability changes over time) using different sets of physics-based simulations. (C) 2018 Elsevier B.V. All rights reserved.
Keywords
OPTIMIZATION, ALGORITHM, SEARCH, Swarm robotics, Ant Colony Optimization, Task allocation

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Citation

Please use this url to cite or link to this publication:

MLA
Khaluf, Yara, Seppe Vanhee, and Pieter Simoens. “Local Ant System for Allocating Robot Swarms to Time-constrained Tasks.” JOURNAL OF COMPUTATIONAL SCIENCE 31 (2019): 33–44. Print.
APA
Khaluf, Y., Vanhee, S., & Simoens, P. (2019). Local ant system for allocating robot swarms to time-constrained tasks. JOURNAL OF COMPUTATIONAL SCIENCE, 31, 33–44.
Chicago author-date
Khaluf, Yara, Seppe Vanhee, and Pieter Simoens. 2019. “Local Ant System for Allocating Robot Swarms to Time-constrained Tasks.” Journal of Computational Science 31: 33–44.
Chicago author-date (all authors)
Khaluf, Yara, Seppe Vanhee, and Pieter Simoens. 2019. “Local Ant System for Allocating Robot Swarms to Time-constrained Tasks.” Journal of Computational Science 31: 33–44.
Vancouver
1.
Khaluf Y, Vanhee S, Simoens P. Local ant system for allocating robot swarms to time-constrained tasks. JOURNAL OF COMPUTATIONAL SCIENCE. Amsterdam: Elsevier Science ; 2019;31:33–44.
IEEE
[1]
Y. Khaluf, S. Vanhee, and P. Simoens, “Local ant system for allocating robot swarms to time-constrained tasks,” JOURNAL OF COMPUTATIONAL SCIENCE, vol. 31, pp. 33–44, 2019.
@article{8612873,
  abstract     = {We propose a novel application of the Ant Colony Optimization algorithm to efficiently allocate a swarm of homogeneous robots to a set of tasks that need to be accomplished by specific deadlines. We exploit the local communication between robots to periodically evaluate the quality of the allocation solutions, and agents select independently among the high-quality alternatives. The evaluation is performed using pheromone trails to favor allocations which minimize the execution time of the tasks. Our approach is validated in both static and dynamic environments (i.e. the task availability changes over time) using different sets of physics-based simulations. (C) 2018 Elsevier B.V. All rights reserved.},
  author       = {Khaluf, Yara and Vanhee, Seppe and Simoens, Pieter},
  issn         = {1877-7503},
  journal      = {JOURNAL OF COMPUTATIONAL SCIENCE},
  keywords     = {OPTIMIZATION,ALGORITHM,SEARCH,Swarm robotics,Ant Colony Optimization,Task allocation},
  language     = {eng},
  pages        = {33--44},
  publisher    = {Elsevier Science },
  title        = {Local ant system for allocating robot swarms to time-constrained tasks},
  url          = {http://dx.doi.org/10.1016/j.jocs.2018.12.012},
  volume       = {31},
  year         = {2019},
}

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