面向多目标侦察任务的无人机航线规划

针对无人机执行多目标侦察任务的航线规划问题,提出一种改进粒子群算法结合高斯伪谱法的分层航线规划方法.设计改进粒子群算法进行航线预规划,针对传统粒子群优化算法收敛速度慢、易陷入局部最优的问题,通过引入混沌映射初始化和自适应参数调整策略,加快算法收敛速度,提升解的最优性.在此基础上,结合最短路求解策略,完成对侦察任务的时序排列,得到可执行的最优侦察任务方案,进而为提升侦察航线的精确性和可控性,基于高斯伪谱法进行航线再规划.引入航线预规划结果作为初值猜测,进一步提升算法的效率和准确性.最后在复杂环境下进行无人机侦察航线仿真分析,结果表明所设计算法能够快速生成任务执行方案与高精度侦察航线,符合军事无人机执行侦察任务的工程应用.

UAV trajectory planning for multi-target reconnaissance missions

A hierarchical trajectory planning method based on improved particle swarm optimization(PSO) combined with Gauss pseudo-spectral method is proposed to solve the trajectory planning of multi-target reconnaissance missions for unmanned aerial vehicles (UAVs). Firstly, an improved PSO algorithm is designed for trajectory pre-planning. Aiming at the problem of slow convergence of traditional PSO algorithms, which is easy to fall into the local optimization problem, the chaos mapping initialization and adaptive parameter adjustment strateges are introduced to accelerate the algorithm convergence and improve the optimality of the solution. On this basis, combined with the shortest path solution strategy, the sequence of reconnaissance tasks is completed, and the executable reconnaissance task plan is obtained. Furthermore, in order to improve the accuracy and controllability of reconnaissance trajectories, trajectory re-planning based on Gauss pseudo-spectral method is proposed. By introducing the results of trajectory pre-planning as initial value guessing, the efficiency and accuracy of the algorithm are further improved. Finally, the simulation under complex environments shows that the designed algorithm can quickly generate mission execution plans and high-precision reconnaissance trajectories, which is consistent with the engineering applications of military UAVs performing reconnaissance tasks.