Publication

An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field

Bai, X., Yan, W., Ge, S. S. & Cao, M., Jul-2018, In : Information Sciences. 453, p. 227-238 12 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Bai, X., Yan, W., Ge, S. S., & Cao, M. (2018). An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field. Information Sciences, 453, 227-238. https://doi.org/10.1016/j.ins.2018.04.044

Author

Bai, Xiaoshan ; Yan, Weisheng ; Ge, Shuzhi Sam ; Cao, Ming. / An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field. In: Information Sciences. 2018 ; Vol. 453. pp. 227-238.

Harvard

Bai, X, Yan, W, Ge, SS & Cao, M 2018, 'An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field', Information Sciences, vol. 453, pp. 227-238. https://doi.org/10.1016/j.ins.2018.04.044

Standard

An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field. / Bai, Xiaoshan; Yan, Weisheng; Ge, Shuzhi Sam; Cao, Ming.

In: Information Sciences, Vol. 453, 07.2018, p. 227-238.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Bai X, Yan W, Ge SS, Cao M. An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field. Information Sciences. 2018 Jul;453:227-238. https://doi.org/10.1016/j.ins.2018.04.044


BibTeX

@article{75e29a27427a475a81f2eac63e207abf,
title = "An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field",
abstract = "This paper investigates the task assignment problem for a team of autonomous aerial/marine vehicles driven by constant thrust and maneuvering in a planar lateral drift field. The aim is to minimize the total traveling time in order to guide the vehicles to deliver a number of customized sensors to a set of target points with different sensor demands in the drift field. To solve the problem, we consider together navigation strategies and target assignment algorithms; the former minimizes the traveling time between two given locations in the drift field and the latter allocates a sequence of target locations to each vehicle. We first consider the effect of the weight of the carried sensors on the speed of each vehicle, and construct a sufficient condition to guarantee that the whole operation environment is reachable for the vehicles. Then from optimal control principles, time-optimal path planning is carried out to navigate each vehicle from an initial position to its given target location. Most importantly, to assign the targets to the vehicles, we combine the virtual coding strategy, multiple offspring method, intermarriage crossover strategy, and the tabu search mechanism to obtain a co-evolutionary multi-population genetic algorithm, short-named CMGA. Simulations on sensor delivery scenarios in both fixed and time-varying drift fields are shown to highlight the satisfying performances of the proposed approach against popular greedy algorithms.",
author = "Xiaoshan Bai and Weisheng Yan and Ge, {Shuzhi Sam} and Ming Cao",
year = "2018",
month = jul,
doi = "10.1016/j.ins.2018.04.044",
language = "English",
volume = "453",
pages = "227--238",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field

AU - Bai, Xiaoshan

AU - Yan, Weisheng

AU - Ge, Shuzhi Sam

AU - Cao, Ming

PY - 2018/7

Y1 - 2018/7

N2 - This paper investigates the task assignment problem for a team of autonomous aerial/marine vehicles driven by constant thrust and maneuvering in a planar lateral drift field. The aim is to minimize the total traveling time in order to guide the vehicles to deliver a number of customized sensors to a set of target points with different sensor demands in the drift field. To solve the problem, we consider together navigation strategies and target assignment algorithms; the former minimizes the traveling time between two given locations in the drift field and the latter allocates a sequence of target locations to each vehicle. We first consider the effect of the weight of the carried sensors on the speed of each vehicle, and construct a sufficient condition to guarantee that the whole operation environment is reachable for the vehicles. Then from optimal control principles, time-optimal path planning is carried out to navigate each vehicle from an initial position to its given target location. Most importantly, to assign the targets to the vehicles, we combine the virtual coding strategy, multiple offspring method, intermarriage crossover strategy, and the tabu search mechanism to obtain a co-evolutionary multi-population genetic algorithm, short-named CMGA. Simulations on sensor delivery scenarios in both fixed and time-varying drift fields are shown to highlight the satisfying performances of the proposed approach against popular greedy algorithms.

AB - This paper investigates the task assignment problem for a team of autonomous aerial/marine vehicles driven by constant thrust and maneuvering in a planar lateral drift field. The aim is to minimize the total traveling time in order to guide the vehicles to deliver a number of customized sensors to a set of target points with different sensor demands in the drift field. To solve the problem, we consider together navigation strategies and target assignment algorithms; the former minimizes the traveling time between two given locations in the drift field and the latter allocates a sequence of target locations to each vehicle. We first consider the effect of the weight of the carried sensors on the speed of each vehicle, and construct a sufficient condition to guarantee that the whole operation environment is reachable for the vehicles. Then from optimal control principles, time-optimal path planning is carried out to navigate each vehicle from an initial position to its given target location. Most importantly, to assign the targets to the vehicles, we combine the virtual coding strategy, multiple offspring method, intermarriage crossover strategy, and the tabu search mechanism to obtain a co-evolutionary multi-population genetic algorithm, short-named CMGA. Simulations on sensor delivery scenarios in both fixed and time-varying drift fields are shown to highlight the satisfying performances of the proposed approach against popular greedy algorithms.

U2 - 10.1016/j.ins.2018.04.044

DO - 10.1016/j.ins.2018.04.044

M3 - Article

VL - 453

SP - 227

EP - 238

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -

ID: 56896931