Online route planning decision-making method of aircraft in complex environment

被引：0

作者：

Yang, Zhipeng ^{[1
]}

Chen, Zihao ^{[1
]}

Zeng, Chang ^{[1
]}

Lin, Song ^{[1
]}

Mao, Jindi ^{[1
]}

Zhang, Kai ^{[1
]}

机构：

[1] System Design Institute of Hubei Aerospace Technology Academy, Wuhan

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 09期

关键词：

autonomous decision-making; curriculum learning; deep reinforcement learning (DRL); online route planning; threat avoidance;

D O I：

10.12305/j.issn.1001-506X.2024.09.28

中图分类号：

学科分类号：

摘要：

Aiming at the problem of online route planning for aircraft, an online autonomous decisionmaking method for aircraft based on deep reinforcement learning (DRL) is proposed. Firstly, the maneuvering model and detection model of the aircraft are explained, and then the deep deterministic policy gradient (DDPG) algorithm of DRL is employed to construct the frame of the aircraft policy model. On this basis, a curriculum learning (CD-DDPG algorithm based on CL is proposed, which decomposes the online route planning task, guides the aircraft to learn the strategies of target approach, threat avoidance, and air route optimization. The corresponding Gaussian noises are set to help the aircraft explore and optimize the strategy. And, the adaptive learning and decision-making control of the aircraft in complex scenarios are realized. Simulation experiments show that the CL-DDPG algorithm can effectively improve the training efficiency of the model. The algorithm model has higher task success rate, excellent generalization and robustness, and can be better applied to online route planning tasks in complex dynamic environments. © 2024 Chinese Institute of Electronics. All rights reserved.

引用

页码：3166 / 3175

页数：9

共 32 条

[1] GUI X H, ZHANG J F, PENG Z H., Trajectory clustering for arrival aircraft via new trajectory representation, Journal of Systems Engineering and Electronics, 32, 2, pp. 473-486, (2021)
[2] NIKLAS G, TOBIAS B, DIRK N., Deep reinforcement learning with combinatorial actions spaces
[3] an application to prescriptive maintenance, Computers &. Industrial Engineering, 179, 1, (2023)
[4] WANG X Y, YANG Y P, WANG D, Et al., Mission-oriented cooperative 3D path planning for modular solar-powered aircraft with energy optimization, Chinese Journal of Aeronautics, 35, 1, pp. 98-109, (2022)
[5] LI B, YANG Z P, CHEN D Q, Et al., Maneuvering target tracking of UAV based on MN-DDPG and transfer learnmg[j], Defence Technology, 17, 2, pp. 457-466, (2021)
[6] LIU C S, ZHANG S J., Novel robust control framework for morphing aircraft, Journal of Systems Engineering and Electronics, 24, 2, pp. 281-287, (2013)
[7] OBAJEMU, MAHFOUF M, MAIYAR L M, Et al., Realtime four-dimensional trajectory generation based on gain-scheduling control and a high-fidelity aircraft model, Engineering, 7, 4, pp. 495-506, (2021)
[8] ZHAO Y, WU J F, GAO Y P., Information fusion method of hypersonic vehicle based on multi-agent navigation, Systems Engineering and Electronics, 42, 2, pp. 405-413, (2020)
[9] CHEN Z J, ZHANG R L, ZHANG P, Et al., Flight control: challenges and opportunities, Acta Automatica Sinica, 39, 6, pp. 703-710, (2013)
[10] DUCHON F, BABINEC A, KAJAN M, Et al., Path planning with modified a star algorithm for a mobile robot, Procedia Engineering, 96, 1, pp. 59-69, (2014)

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