Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

Li Tan; Jiaqi Shi; Jing Gao; Haoyu Wang; Hongtao Zhang; Yu Zhang

doi:10.1017/S0263574722001680

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

Li Tan, Jiaqi Shi, Jing Gao^*, Haoyu Wang, Hongtao Zhang, Yu Zhang

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Path planning is a key research issue in the field of unmanned aerial vehicle (UAV) applications. In practical applications, multi-objective path planning is usually required for multi-UAVs, so this paper proposes the improved balanced artificial bee colony (IB-ABC) algorithm to optimize multi-objective path planning. The algorithm adopts the ABC algorithm that emphasizes the global search capability, which is based on iterative feedback information. It uses single-element points, multi-element points, and iteration constraints to optimize the strategies of employed bees, follower bees, and scout bees, respectively. In terms of time and priority, simulation experiments prove that the IB-ABC algorithm can balance local and global search capabilities, accelerate the speed of convergence, and realize multi-UAV path planning in complex mountain environments.

Original language	English
Pages (from-to)	1244-1257
Number of pages	14
Journal	Robotica
Volume	41
Issue number	4
DOIs	https://doi.org/10.1017/S0263574722001680
Publication status	Published - 12 Apr 2023
Externally published	Yes

Keywords

ABC algorithm
balance strategy
element point control
feedback
multi-UAV path planning

Access to Document

10.1017/S0263574722001680

Cite this

@article{8d8ef04205e74741b6d8bbf154cd5a88,

title = "Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence",

abstract = "Path planning is a key research issue in the field of unmanned aerial vehicle (UAV) applications. In practical applications, multi-objective path planning is usually required for multi-UAVs, so this paper proposes the improved balanced artificial bee colony (IB-ABC) algorithm to optimize multi-objective path planning. The algorithm adopts the ABC algorithm that emphasizes the global search capability, which is based on iterative feedback information. It uses single-element points, multi-element points, and iteration constraints to optimize the strategies of employed bees, follower bees, and scout bees, respectively. In terms of time and priority, simulation experiments prove that the IB-ABC algorithm can balance local and global search capabilities, accelerate the speed of convergence, and realize multi-UAV path planning in complex mountain environments.",

keywords = "ABC algorithm, balance strategy, element point control, feedback, multi-UAV path planning",

author = "Li Tan and Jiaqi Shi and Jing Gao and Haoyu Wang and Hongtao Zhang and Yu Zhang",

note = "Publisher Copyright: {\textcopyright} Beijing Technology and Business University, 2022. Published by Cambridge University Press.",

year = "2023",

month = apr,

day = "12",

doi = "10.1017/S0263574722001680",

language = "English",

volume = "41",

pages = "1244--1257",

journal = "Robotica",

issn = "0263-5747",

publisher = "Cambridge University Press",

number = "4",

}

TY - JOUR

T1 - Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

AU - Tan, Li

AU - Shi, Jiaqi

AU - Gao, Jing

AU - Wang, Haoyu

AU - Zhang, Hongtao

AU - Zhang, Yu

N1 - Publisher Copyright: © Beijing Technology and Business University, 2022. Published by Cambridge University Press.

PY - 2023/4/12

Y1 - 2023/4/12

N2 - Path planning is a key research issue in the field of unmanned aerial vehicle (UAV) applications. In practical applications, multi-objective path planning is usually required for multi-UAVs, so this paper proposes the improved balanced artificial bee colony (IB-ABC) algorithm to optimize multi-objective path planning. The algorithm adopts the ABC algorithm that emphasizes the global search capability, which is based on iterative feedback information. It uses single-element points, multi-element points, and iteration constraints to optimize the strategies of employed bees, follower bees, and scout bees, respectively. In terms of time and priority, simulation experiments prove that the IB-ABC algorithm can balance local and global search capabilities, accelerate the speed of convergence, and realize multi-UAV path planning in complex mountain environments.

AB - Path planning is a key research issue in the field of unmanned aerial vehicle (UAV) applications. In practical applications, multi-objective path planning is usually required for multi-UAVs, so this paper proposes the improved balanced artificial bee colony (IB-ABC) algorithm to optimize multi-objective path planning. The algorithm adopts the ABC algorithm that emphasizes the global search capability, which is based on iterative feedback information. It uses single-element points, multi-element points, and iteration constraints to optimize the strategies of employed bees, follower bees, and scout bees, respectively. In terms of time and priority, simulation experiments prove that the IB-ABC algorithm can balance local and global search capabilities, accelerate the speed of convergence, and realize multi-UAV path planning in complex mountain environments.

KW - ABC algorithm

KW - balance strategy

KW - element point control

KW - feedback

KW - multi-UAV path planning

UR - http://www.scopus.com/inward/record.url?scp=85150015668&partnerID=8YFLogxK

U2 - 10.1017/S0263574722001680

DO - 10.1017/S0263574722001680

M3 - Article

AN - SCOPUS:85150015668

SN - 0263-5747

VL - 41

SP - 1244

EP - 1257

JO - Robotica

JF - Robotica

IS - 4

ER -

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this