飞控与探测2026，Vol.9Issue(4)：124-135,12.DOI:10.20249/j.cnki.2096-5974.2026.04.011
基于控制变量参数化的无人机集群协同打击轨迹规划
Collaborative Strike Trajectory Planning of Unmanned Aerial Vehicle Swarm Based on Control Variable Parameterization
摘要
Abstract
This article focuses on the trajectory planning problem of UAV swarm collaborative ground strike missions in complex environments with dynamic obstacles.Considering multiple UAV arriving at the target position at a specified angle at the same time,an optimal control prob-lem is established within the performance requirements of the UAV,combined with the safety dis-tance constraint of multiple UAV.The control variable parameterization method is used to trans-form the optimal control problem into a nonlinear programming problem,and the optimal control parameters are solved.At the same time,to accelerate the solving speed,the non-convex con-straints of obstacle avoidance and collision avoidance are extended to the objective function using an accurate penalty function,reducing the problem of long solving time caused by hard constraints.Finally,a distributed framework combined with rolling optimization is used to solve the local finite time domain optimization problem online.By designing simulation experiments to verify the effec-tiveness of the algorithm proposed in this paper,the simulation results show that the proposed al-gorithm can effectively complete collaborative strike tasks in complex environments and has good temporal and spatial collaboration performance.At the same time,the introduction of precise pen-alty functions can effectively accelerate the optimization problem solving speed,and the algorithm can achieve online decision-making.关键词
无人机集群/轨迹规划/最优控制/控制变量参数化/精确罚函数/滚动优化Key words
unmanned aerial vehicle swarm/trajectory planning/optimal control/control variable parameterization/exact penalty function/rolling optimization分类
航空航天引用本文复制引用
杨俊聪,文泽凌,程雨丽,周赢,钟苏川,宁召柯..基于控制变量参数化的无人机集群协同打击轨迹规划[J].飞控与探测,2026,9(4):124-135,12.基金项目
空基信息感知与融合全国重点实验室开放课题(202449) （202449）
四川省科技计划(2024NSFSC1485) （2024NSFSC1485）
