基于CL-RRT与MPC的舰载机牵引系统路径规划

doi:10.12305/j.issn.1001-506X.2024.05.27

摘要/Abstract

摘要：

针对舰载机在甲板狭小、复杂环境下的调运过程, 结合闭环快速随机搜索树(close loop rapidly exploring random trees, CL-RRT)和模型预测控制(model predictive control, MPC)提出一种舰载机牵引系统的路径规划算法。首先, 在CL-RRT中采用纯追踪器与线性二次型(linear quadratic, LQ)控制器得到系统的控制输入并向前仿真得到规划路径。其次, 将已得路径进行等比缩放与插值作为MPC的初始解。最后, 设置MPC的目标函数等并解得最终路径。展开自定义三个场景下的仿真实验, 通过与CL-RRT算法的实验结果进行比较, 验证本文算法的优越性。实验结果表明, 所提算法可有效解决因采样随机性带来解质量不佳的问题, 提升舰载机在甲板上的调运效率与安全性。

关键词: 舰载机牵引系统, 闭环快速随机搜索树, 模型预测控制, 路径规划

Abstract:

The transport process of carrier aircraft is difficult because the environment of deck is narrow and complex. A path planning algorithm for the carrier aircraft traction system is proposed combining close loop rapidly exploring random trees (CL-RRT) and model predictive control (MPC). Firstly, the pure pursuit controller and linear quadratic (LQ) controller are used in CL-RRT to obtain the control input of the system, and the planned path is obtained by forward simulation. Secondly, the obtained path is scaled and interpolated as the initial solution of MPC. Finally, the objective function and so on of MPC is set and the final path is solved. The simulation experiment of three customized scenarios is carried out to verify the superiority of the proposed algorithm by comparing with the experimental results of CL-RRT algorithm. Experimental results show that the proposed algorithm can effectively solve the problem of poor solution quality caused by randomness of sampling, and improve the efficiency and safety of carrier aircraft on deck.

Key words: carrier aircraft traction system, close loop rapidly exploring random trees (CL-RRT), model predictive control (MPC), path planning

中图分类号:

孙家玮, 余明晖, 杨大鹏, 汤皓泉, 卞大鹏. 基于CL-RRT与MPC的舰载机牵引系统路径规划[J]. 系统工程与电子技术, 2024, 46(5): 1745-1755.

Jiawei SUN, Minghui YU, Dapeng YANG, Haoquan TANG, Dapeng BIAN. Path planning of carrier aircraft traction system based on CL-RRT and MPC[J]. Systems Engineering and Electronics, 2024, 46(5): 1745-1755.

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算法名称	路线长度/m	扫掠面积/m²	最大输入角/(°)	输入角极差/(°)	输入角震荡次数
RRT*	148.63	1 842.45	-	-	-
CL-RRT	160.01	1 244.00	45	90	5
CLRRT+MPC	143.75	1 005.62	10.28	15.83	0
对比结果	减少10.2%	减少19.2%	减少77.2%	减少82.4%	更平稳

算法名称	路线长度/m	扫掠面积/m²	最大输入角/(°)	输入角极差/(°)	输入角震荡次数
RRT*	136.03	1 565.52	-	-	-
CL-RRT	197.75	1 372.16	45	90	6
CL-RRT+MPC	158.01	1 098.43	26.30	52.60	2
对比结果	减少20.1%	减少20.0%	减少41.6%	减少41.6%	平滑

算法名称	路线长度/m	扫掠面积/m²	最大输入角/(°)	输入角极差/(°)	输入角震荡次数
RRT*	186.85	1 769.20	-	-	-
CL-RRT	184.18	1 321.41	45	90	10
CL-RRT+MPC	177.63	1 218.48	14.02	21.28	1
对比结果	减少3.6%	减少7.8%	减少68.8%	减少76.4%	平滑

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