TY - JOUR
T1 - An adaptive bi-level task planning strategy for multi-USVs target visitation
AU - Sun, Siqing
AU - Song, Baowei
AU - Wang, Peng
AU - Dong, Huachao
AU - Chen, Xiao
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/1
Y1 - 2022/1
N2 - This paper considers a task planning problem, which dispatches multiple unmanned surface vehicles (USVs) to visit a set of targets located in ocean environments. The problem is modeled as a bi-level optimization to reduce the total and the individual navigation costs simultaneously. The upper-level allocates targets and schedules target visitation sequences, while the lower-level plans safe and economical paths between two targets under the current influence. Subsequently, a novel nested strategy is proposed to solve the bi-level problem, which modifies each level initialization process and can adaptively give the lower-level function evaluation number according to the problem complexity. Besides, the proposed strategy can adopt general metaheuristics as optimizers. Thus, two upper-level and five lower-level algorithms are employed in combination, which covers most kinds of metaheuristics. Finally, the ten combinations of algorithms are tested on three large-scale and complex cases, and the results verify the effectiveness of the proposed model and strategy.
AB - This paper considers a task planning problem, which dispatches multiple unmanned surface vehicles (USVs) to visit a set of targets located in ocean environments. The problem is modeled as a bi-level optimization to reduce the total and the individual navigation costs simultaneously. The upper-level allocates targets and schedules target visitation sequences, while the lower-level plans safe and economical paths between two targets under the current influence. Subsequently, a novel nested strategy is proposed to solve the bi-level problem, which modifies each level initialization process and can adaptively give the lower-level function evaluation number according to the problem complexity. Besides, the proposed strategy can adopt general metaheuristics as optimizers. Thus, two upper-level and five lower-level algorithms are employed in combination, which covers most kinds of metaheuristics. Finally, the ten combinations of algorithms are tested on three large-scale and complex cases, and the results verify the effectiveness of the proposed model and strategy.
KW - Adaptive nested strategy
KW - Bi-level optimization
KW - Large-scale and complex case
KW - Metaheuristic
KW - Multi-USVs task planning
UR - http://www.scopus.com/inward/record.url?scp=85120988107&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2021.108086
DO - 10.1016/j.asoc.2021.108086
M3 - 文章
AN - SCOPUS:85120988107
SN - 1568-4946
VL - 115
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 108086
ER -