Combinatorial optimization for UAV swarm path planning and task assignment in multi-obstacle battlefield environment

被引：5

作者：

Guo, Cong ^{[1
]}

Huang, Lei ^{[1
]}

Tian, Kuo ^{[1
]}

机构：

[1] Dalian Univ Technol, Dept Engn Mech, State Key Lab Struct Anal Optimizat & CAE Software, Dalian 116024, Peoples R China

来源：

APPLIED SOFT COMPUTING | 2025年 / 171卷

关键词：

Path planning; Task assignment; Unmanned aerial vehicle; Rapid-exploring random tree; Artificial potential field; Particle swarm optimization; DIFFERENTIAL EVOLUTION; ALGORITHM; STRATEGY;

D O I：

10.1016/j.asoc.2025.112773

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Aiming at improving the autonomous control performance and comprehensive combat capability of Unmanned Aerial Vehicle (UAV), this paper presents a combinatorial optimization for UAV swarm path planning and task assignment, including two optimization problems. For path planning, Rapidly-exploring Random Tree*(RRT*) Artificial Potential Field (APF) algorithm is improved by introducing adaptive potential field adjustment and global search. The comparison simulations show that the optimization time by the improved RRT*-APF can be reduced by more than 80 % while achieving the shortest path. For task assignment, with the shortest path as input, the discrete PSO considering multiple constraints is employed for the optimal assignment scheme. Compared with other algorithms, the algorithm used can obtain the optimal solution. Finally, two-dimensional and three-dimensional cases are carried out. Case results show that the proposed method can not only effectively plan reasonable paths, but also obtain the optimal task assignment scheme.

引用

页数：19

共 43 条

[1] A new APF strategy for path planning in environments with obstacles [J].