结合启发式与最优控制的舰载机甲板路径规划算法

doi:10.12305/j.issn.1001-506X.2023.04.18

摘要/Abstract

摘要：

舰载机甲板调运是机群全周期出动回收作业的关键环节, 为提高舰载机调运效率, 以得到满足运动学及终端位姿约束的最优路径为目标, 结合启发式与最优控制方法, 进行舰载机甲板路径规划研究。首先, 针对甲板复杂布列环境建立凸壳障碍膨胀模型。其次, 在A^*算法中引入动态衡量因子, 设计轨迹重搜索算法, 求解最短路径关键点。最后, 解算关键点运动状态, 结合最优控制算法, 对关键点间分段路径进行规划与整合。开展典型甲板环境下的路径规划仿真实验, 通过与最优控制算法仿真实验结果相比较, 验证所提算法的优越性。仿真实验结果表明, 所提算法有效解决了最优控制算法在复杂障碍环境下初值敏感性问题并有效提升了甲板调运的优化性能。

关键词: 舰载机, 飞行甲板, 路径规划, A^*算法, 最优控制

Abstract:

Carrier-based aircraft deck routing is a key step in the full-cycle dispatching and recovery operation of aircraft fleets. In order to improve the efficiency of carrier-based aircraft transfer, the optimal path satisfying the kinematic and terminal position constraints is obtained as the goal, and the heuristic and optimal control methods are combined to conduct the deck path planning research of carrier-based aircraft. Firstly, a convex hull obstacle expansion model is established for the complex deck layout environment. Secondly, a dynamic measurement factor is introduced into the A^* algorithm and the trajectory re-search algorithm is designed to solve the shortest path key point. Finally, the motion state of the key point is solved and combined with the optimal control algorithm to plan and integrate the segmented paths between the key points. The simulation experiments of path planning under typical deck environment are carried out to verify the superiority of the proposed algorithm by comparing with the simulation results of the optimal control algorithm. The simulation experimental results show that the proposed algorithm effectively solves the problem of initial value sensitivity of the optimal control algorithm in the complex obstacle environment, and effectively improves the optimization performance of the deck transfer.

Key words: carrier-based aircraft, flight deck, path planning, A^* algorithm, optimal control

中图分类号:

韩维, 刘子玄, 苏析超, 崔凯凯, 刘洁. 结合启发式与最优控制的舰载机甲板路径规划算法[J]. 系统工程与电子技术, 2023, 45(4): 1098-1110.

Wei HAN, Zixuan LIU, Xichao SU, Kaikai CUI, Jie LIU. Deck path planning algorithm of carrier-based aircraft based on heuristic and optimal control[J]. Systems Engineering and Electronics, 2023, 45(4): 1098-1110.

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算法名称	t/s	max (arctan(\|u₁\|))/(°)	u₂波动次数	v₂阶跃次数
最优控制算法	132	40	4	4
结合改进A^*算法与最优控制算法	121	22	2	2
优化结果	缩短8.3%	缩减45%且曲线平缓	波动次数减少	阶跃次数减少

算法名称	t/s	max (arctan(\|u₁\|))/(°)	u₂波动次数	v₂阶跃次数
最优控制算法	131	38	3	3
结合改进A^*算法与最优控制算法	130	37	3	3
优化结果	缩短0.8%	减小2.6%且曲线平缓	一致	一致

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