Study on 4D taxiing path planning of aircraft based on spatio-temporal network

Ningning Zhao; Shihao Cui; Ningning Zhao; Shihao Cui

doi:10.3934/mbe.2023213

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 3: 4592-4608. doi: 10.3934/mbe.2023213

Previous Article Next Article

Research article

Study on 4D taxiing path planning of aircraft based on spatio-temporal network

Ningning Zhao ^,,
Shihao Cui

School of Air Traffic Control, Civil Aviation University of China, Tianjin, China

Academic Editor: Ping Jiang

Received: 14 July 2022 Revised: 13 November 2022 Accepted: 16 November 2022 Published: 27 December 2022

In recent years, China vigorously develops energy conservation and emission reduction, in order to actively respond to the national call to make the aircraft operation process reduce unnecessary costs and strengthen the safety of the aircraft taxiing process. This paper studies the spatio-temporal network model and dynamic planning algorithm to plan the aircraft taxiing path. First, the relationship between the force, thrust and engine fuel consumption rate during aircraft taxiing is analyzed to determine the fuel consumption rate during aircraft taxiing. Then, a two-dimensional directed graph of airport network nodes is constructed. The state of the aircraft is recorded when considering the dynamic characteristics of the node sections, the taxiing path is determined for the aircraft using dijkstra's algorithm, and the overall taxiing path is discretized from node to node using dynamic planning to design a mathematical model with the shortest taxiing distance as the goal. At the same time, the optimal taxiing path is planned for the aircraft in the process of avoiding aircraft conflicts. Thus, a state-attribute-space-time field taxiing path network is established. Through example simulations, simulation data are finally obtained to plan conflict-free paths for six aircraft, the total fuel consumption for the six aircraft planning is 564.29 kg, and the total taxiing time is 1765s. This completed the validation of the dynamic planning algorithm of the spatio-temporal network model.

Keywords:

Citation: Ningning Zhao, Shihao Cui. Study on 4D taxiing path planning of aircraft based on spatio-temporal network[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4592-4608. doi: 10.3934/mbe.2023213

Related Papers:

[1]	Yuheng Wang, Yongquan Zhou, Qifang Luo . Parameter optimization of shared electric vehicle dispatching model using discrete Harris hawks optimization. Mathematical Biosciences and Engineering, 2022, 19(7): 7284-7313. doi: 10.3934/mbe.2022344
[2]	Dandan Fan, Dawei Li, Fangzheng Cheng, Guanghua Fu . Effects of congestion charging and subsidy policy on vehicle flow and revenue with user heterogeneity. Mathematical Biosciences and Engineering, 2023, 20(7): 12820-12842. doi: 10.3934/mbe.2023572
[3]	Xiangyang Ren, Shuai Chen, Liyuan Ren . Optimization of regional emergency supplies distribution vehicle route with dynamic real-time demand. Mathematical Biosciences and Engineering, 2023, 20(4): 7487-7518. doi: 10.3934/mbe.2023324
[4]	Fulin Dang, Chunxue Wu, Yan Wu, Rui Li, Sheng Zhang, Huang Jiaying, Zhigang Liu . Cost-based multi-parameter logistics routing path optimization algorithm. Mathematical Biosciences and Engineering, 2019, 16(6): 6975-6989. doi: 10.3934/mbe.2019350
[5]	Peng Zheng, Jingwei Gao . Damping force and energy recovery analysis of regenerative hydraulic electric suspension system under road excitation: modelling and numerical simulation. Mathematical Biosciences and Engineering, 2019, 16(6): 6298-6318. doi: 10.3934/mbe.2019314
[6]	Hamid Mofidi . New insights into the effects of small permanent charge on ionic flows: A higher order analysis. Mathematical Biosciences and Engineering, 2024, 21(5): 6042-6076. doi: 10.3934/mbe.2024266
[7]	Smita Shandilya, Ivan Izonin, Shishir Kumar Shandilya, Krishna Kant Singh . Mathematical modelling of bio-inspired frog leap optimization algorithm for transmission expansion planning. Mathematical Biosciences and Engineering, 2022, 19(7): 7232-7247. doi: 10.3934/mbe.2022341
[8]	Jana Zatloukalova, Kay Raum . High frequency ultrasound assesses transient changes in cartilage under osmotic loading. Mathematical Biosciences and Engineering, 2020, 17(5): 5190-5211. doi: 10.3934/mbe.2020281
[9]	Xiangyang Ren, Xinxin Jiang, Liyuan Ren, Lu Meng . A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction. Mathematical Biosciences and Engineering, 2023, 20(1): 683-706. doi: 10.3934/mbe.2023031
[10]	Massimo Fioranelli, O. Eze Aru, Maria Grazia Roccia, Aroonkumar Beesham, Dana Flavin . A model for analyzing evolutions of neurons by using EEG waves. Mathematical Biosciences and Engineering, 2022, 19(12): 12936-12949. doi: 10.3934/mbe.2022604

Abstract

Creating this inaugural special issue on Engineering Applications of Artificial Intelligence (AI) is important due to the rapid technology advancement and the aim to reduce the manpower by incorporating Artificial Intelligence in various Industry 4.0 applications. As my research reflects the multi-disciplinarily of systems (consisting of mechanical, electrical, electronics, acoustical and marine engineering) from initial concepts to the modelling and AI simulation, creating graphical-user interface and their actual implementations and testing on sites. The special issue provides a good platform to share applied research results from different researchers around the world.

For example, the phase partition-based ensemble learning framework upon least squares supports vector regression (LSSVR) was used for soft sensor modeling to improve the prediction accuracy in chemical and biological processes. As a result, the robotic grasping based on improved Gaussian mixture model was also proposed using the virtual robot experimentation platform. The face image recognition algorithm based on two-dimensional (2D) Gabor wavelet transform and Local Binary Pattern (LBP) was presented. It provides a better classification performance in different scales and directions affected by illumination, gesture, expression, and other factor's variation. With more consciousness in cyber-security, the paper that used the Kalman filter-based attack detection model was proposed. The block withholding delay attack and the countermeasure were also proposed in a similar occasion. The well-known convolutional neural network (CNN) based approach was applied to detect the obstacle for the unmanned surface vehicle. Subsequently, an effective classifier based on the CNN and regularized extreme learning machine (ELM) was adopted to reduce the classification time in the training and testing.

In summary, this issue concluded with different engineering applications of AI. It is imperative that we continue to progress in our search for better engineering systems design and simulation using AI. The progress reported in this special issue suggests that achieving these aims is an attainable one. I hope that we can stay in contact and make this world a better place for a "deep" collaborative research.

References

[1]	F. Liu, X. Zhang, G. Ma, L. Liu, Research on A* algorithm-based dynamic programming method for airport ground movement, J. air force Eng. Univ. (Nat. Sci. Ed.), 18 (2017), 19–23.
[2]	J. Yu, L. Chen, Z. Liu, Dynamic taxing path planning based on time-space network, Aeronaut. Comput. Tech., 50 (2020), 25–28.
[3]	N. Li, Y. Sun, Q. Jiao, Z. Gao, Aircraft taxiing path planning based on multi-target speed profile, Flight Dyn., 38 (2020), 87–94. https://doi.org/10.13645/j.cnki.f.d.20200622.002 doi: 10.13645/j.cnki.f.d.20200622.002
[4]	Z. Zhang, Z. Yu, Taxiing route optimization algorithm based on situation awareness, Sci. Technol. Eng., 22 (2022), 1693–1698.
[5]	X. Wang, Research on Taxiing Route Planning for Aircraft Based on Surface Hotspots, Master thesis, Civil Aviation Flight University of China in Guanghan, 2015.
[6]	N. Li, Q. Zhao, X. Xu, Research on taxing optimization for aircraft based on improved A* algorithm, Comput. Simul., 29 (2012), 88–92. https://doi.org/10.3969/j.issn.1006-9348.2012.07.021 doi: 10.3969/j.issn.1006-9348.2012.07.021
[7]	A. E. I. Brownlee, J. R. Woodward, M. Weiszer, J. Chen, A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing, in GECCO'18: Proceedings of the Genetic and Evolutionary Computation Conference, (2018), 1207–1213. https://doi.org/10.1145/3205455.3205558
[8]	S. J. Landry, X. W. Chen, S. Y. Nof, A decision support methodology for dynamic taxiway and runway conflict prevention, Decis. Support Syst., 55 (2013), 165–174. https://doi.org/10.1016/j.dss.2013.01.016 doi: 10.1016/j.dss.2013.01.016
[9]	S. Ravizza, J. A. D. Atkin, E. K. Burke, A more realistic approach for airport ground movement optimisation with stand holding, J. Scheduling, 17 (2014), 507–520. https://doi.org/10.1007/s10951-013-0323-3 doi: 10.1007/s10951-013-0323-3
[10]	N. Li, Y. Sun, J. Yu, J. Li, H. Zhang, S. Tsai, An empirical study on low emission taxiing path optimization of aircrafts on airport surfaces from the perspective of reducing carbon emissions, Energies, 12 (2019), 1649. https://doi.org/10.3390/en12091649 doi: 10.3390/en12091649
[11]	J. Chen, M. Weiszer, G. Locatelli, S. Ravizza, J. A. Atkin, P. Stewart, et al., Towards a more realistic, cost effective and greener ground movement through active routing: a multi-objective shortest path approach, IEEE Trans. Intell. Transp. Sys., 17 (2016), 3524–3540. https://doi.org/10.1109/TITS.2016.2587619 doi: 10.1109/TITS.2016.2587619
[12]	N. K. Wickramasinghe, M. Brown, S. Fukushima, Y. Fukuda, Optimization-based performance assessment on 4D trajectory-based operations with track data, in Air Traffic Management and Systems Ⅱ, Springer, 420 (2017), 113–135. https://doi.org/10.1007/978-4-431-56423-2_6
[13]	T. Zhang, M. Ding, H. Zuo, J. Chen, M. Weiszer, X. Qian, et al, An online speed profile generation approach for efficient airport ground movement, Transp. Res. Part C: Emerging Technol., 93 (2018), 256–272. https://doi.org/10.1016/j.trc.2018.05.030 doi: 10.1016/j.trc.2018.05.030
[14]	Y. Gao, Short-term and long-term Hybrid Igorithm for 4D Trajectory Prediction, Master thesis, Civil Aviation University of China in Tianjin, 2016.
[15]	F. Zhang, Y. Huang, L. Li, J. Qing, C. Liu, Freight rope way path planning method based on Dijkstra algorithm, J. Shandong Univ. (Eng. Sci.), 2022.
[16]	M. Das, A. Roy, S. Maity, S. Kar, S. Sengupta, Solving fuzzy dynamic ship routing and scheduling problem through new genetic algorithm, Decis. Making: Appl. Manage. Eng., 5 (2022), 329–361. https://doi.org/10.31181/dmame181221030d doi: 10.31181/dmame181221030d
[17]	M. Yao, Research on Key Techniques of Aircraft Sueface Trajectory Prediction and Path Planning in Airport, Master thesis, University of Electronic Science and Technology of China in Xian, 2018.
[18]	H. Chen, Research on Optimization Techniques of Airport Aircraft Taxi Path, Nanjing University of Aeronautics and Astronautics in Nanjing, 2015.
[19]	Y. Sun, Research on Optimization of Aircraft Taxiing at the Airport Surface Based on 4D Trajectory, Civil Aviation University of China in Tianjin, 2020.
[20]	J. Xin, L. Wei, D. Wang, H. Xuan, Receding horizon path planning of automated guided vehicles using a time‐space network model. Optim. Contr. Appl. Met., 6 (2020), 1889–1903. https://doi.org/10.1002/oca.2654 doi: 10.1002/oca.2654
[21]	J. Xin, C. Meng, F. Schulte, J. Peng, Y. Liu, R. R. Negenborn, A time-space network model for collision-free routing of planar motions in a multi-robot station, IEEE Trans. Ind. Inf., 10 (2020), 6413–6422. https://doi.org/10.1109/TII.2020.2968099 doi: 10.1109/TII.2020.2968099

This article has been cited by:

Xu Hao, Deyu Zhou, Ruiheng Zhong, Shunxi Li, Xianming Meng, Bo Liu, Electrification pathways for light-duty logistics vehicles based on perceived cost of ownership in Northern China, 2024, 3, 2831-932X, 10.20517/cf.2024.24

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)