CLC number: TP399
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-08-08
Cited: 0
Clicked: 5408
Weichao Si, Tao Sun, Chao Song, Jie Zhang. Design and verification of a transfer path optimization method for an aircraft on the aircraft carrier flight deck[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(9): 1221-1233.
@article{title="Design and verification of a transfer path optimization method for an aircraft on the aircraft carrier flight deck",
author="Weichao Si, Tao Sun, Chao Song, Jie Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="9",
pages="1221-1233",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000251"
}
%0 Journal Article
%T Design and verification of a transfer path optimization method for an aircraft on the aircraft carrier flight deck
%A Weichao Si
%A Tao Sun
%A Chao Song
%A Jie Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 9
%P 1221-1233
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000251
TY - JOUR
T1 - Design and verification of a transfer path optimization method for an aircraft on the aircraft carrier flight deck
A1 - Weichao Si
A1 - Tao Sun
A1 - Chao Song
A1 - Jie Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 9
SP - 1221
EP - 1233
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000251
Abstract: This paper studies the transfer path planning problem for safe transfer of an aircraft on the aircraft carrier flight deck under a poor visibility condition or at night. First, we analyze the transfer path planning problem for carrier-based aircraft on the flight deck, and define the objective to be optimized and the constraints to be met. Second, to solve this problem, the mathematical support models for the flight deck, carrier aircraft entity, entity extension, entity posture, entity conflict detection, and path smoothing are established, as they provide the necessary basis for transfer path planning of the aircraft on the aircraft carrier. Third, to enable automatic transfer path planning, we design a multi-habitat parallel chaos algorithm (called KCMPSO), and use it as the optimization method for transfer path planning. Finally, we take the Kuznetsov aircraft carrier as a verification example, and conduct simulations. The simulation results show that compared with particle swarm optimization, this method can solve the transfer path planning problem for an aircraft on the aircraft carrier flight deck better.
[1]Chen XY, 2018. The U.S. DoD Publish total aviation mishaps in recent years. Int Aviat, 6:25-28.
[2]Hao SQ, Cheng SW, Zhang YP, 2018. A multi-aircraft conflict detection and resolution method for 4-dimensional trajectory-based operation. Chin J Aeronaut, 31(7):1579-1593.
[3]He SH, Yan SW, Xu JW, 2019. Path designing for aircrafts’ taxiing on flight deck while launching. J Nav Aeronaut Astron Univ, 34(1):126-132 (in Chinese).
[4]Helbig M, Engelbrecht AP, 2014. Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems. Swarm Evol Comput, 14:31-47.
[5]Huang C, 2018. Study on Path Planning and Location of Mobile Robot based on Intelligent Optimization Algorithm. MS Thesis, Dalian Jiaotong University, Dalian, China (in Chinese).
[6]Jordehi AR, Jasni J, Wahab NA, et al., 2015. Enhanced leader PSO (ELPSO): a new algorithm for allocating distributed TCSC’s in power systems. Int J Electron Power Energy Syst, 64:771-784.
[7]Liang WJ, 2017. The Research and Implementation of MES based on Hybrid PSO Multi-objective Job Shop Scheduling. MS Thesis, Guangdong University of Technology, Guangzhou, China (in Chinese).
[8]Liu A, Liu K, 2017. Advances in carrier-based aircraft deck operation scheduling. Syst Eng Theory Pract, 37(1):49-60 (in Chinese).
[9]Lu W, 2019. Design and Application of Dynamic Multi-objective Particle Swarm Optimization Algorithm. MS Thesis, Beijing University of Technology, Beijing, China (in Chinese).
[10]Luan TT, 2019. Research on Model and Evaluation Method Capacity for Sortie Process of Carrier Aircraft. MS Thesis, Harbin Engineering University, Harbin, China (in Chinese).
[11]Luo J, Li Y, 2010. Artificial bee colony algorithm with chaotic-search strategy. Contr Dec, 25(12):1913-1916 (in Chinese).
[12]Ma JC, Yao DK, Zhao GH, et al., 2018. Research on airspace planning of tactical training based on discrete particle swarm optimization. Fire Contr Commun Contr, 43(12):94-98 (in Chinese).
[13]Osaba E, Yang XS, Diaz F, et al., 2016. An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng Appl Artif Intell, 48:59-71.
[14]Qi C, Wang D, 2016. Dynamic aircraft carrier flight deck task planning based on HTN. IFAC-PapersOnline, 49(12):1608-1613.
[15]Rosli NS, Ibrahim R, Ismail I, 2017. Intelligent prediction system for gas metering system using particle swarm optimization in training neural network. Proc Comput Sci, 105:165-169.
[16]Ryan JC, Banerjee AG, Cummings ML, et al., 2014. Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations. IEEE Trans Cybern, 44(6):761-773.
[17]Shao Q, 2017. Particle Swarm Optimization and its Application in Engineering. MS Thesis, Jilin University, Jilin, China (in Chinese).
[18]Si WC, Han W, Shi WW, 2012. Research on deck-disposed scheduling method of carrier planes based on PSO algorithm. Acta Aeron Astron Sin, 33(11):2048-2056 (in Chinese).
[19]Si WC, Qi YD, Han W, 2015. Hangar-exporting optimization schedule of multi-carrier plane based on NGA. Fire Contr Commun Contr, 40(11):13-19 (in Chinese).
[20]Song T, 2018. Design and Implementation of Aircraft Landing Information Processing and Display System. MS Thesis, Harbin Engineering University, Harbin, China (in Chinese).
[21]Tian DP, Shi ZZ, 2018. MPSO: modified particle swarm optimization and its applications. Swarm Evol Comput, 41:49-68.
[22]Trelea IC, 2003. The particle swarm optimization algorithm: convergence analysis and parameter selection. Inform Process Lett, 85(6):317-325.
[23]Wu Y, Jin YC, Liu XX, 2015. A directed search strategy for evolutionary dynamic multiobjective optimization. Soft Comput, 19(11):3221-3255.
[24]Yang DS, Wang KF, Chen DW, et al., 2018. Parallel carrier fleets: from digital architectures to smart formations. J Commun Contr, 4(2):101-110 (in Chinese).
[25]Zeng SY, 2017. Aircraft Carrier Deck Task Plan Repair and Replanning Research under Dynamic Environment. MS Thesis, Huazhong University of Science and Technology, Wuhan, China (in Chinese).
[26]Zhao Q, 2019. Analysis and Research on Driving Behavior of Carrier-based Aircraft Tractor. MS Thesis, Shenyang University of Technology, Shenyang, China (in Chinese).
Open peer comments: Debate/Discuss/Question/Opinion
<1>