Four-dimension diversion and regression path planning method in hazardous weather conditions

doi:10.16265/j.cnki.issn1003-3033.2023.02.1355

Abstract

Abstract:

In order to solve problems due to diversion caused by hazardous weather under TBO,a 4-dimension diversion and regression path planning method was proposed. First, the airspace according to the performance limitation was rasterized. The ant colony algorithm and roulette method were used to generate the 3-dimension path for hazard weather avoidance. The concept of diversion and regression path was defined. Combining the path with the estimated time of arrival, the speed of the diversion path was calculated. Through aligning the time, the 4-dimension diversion path was obtained. Finally, taking a route in Midwest China as the operation scenario, selecting three different reroute 4-dimension regression points, the path of avoiding obstacles caused by hazardous weather was obtained. The effects of three different diversion schemes were evaluated through fuel consumption and emission costs. The result shows: when the latest recovery point is chosen, the diversion scheme 3 has 5.9 t fuel consumption and 26.3 t greenhouse gas emission, which are the least of the three schemes, but the number of conflict resolution is twice that of others. The diversion scheme 1 with the earliest recovery point does not conflict with other flights, but the fuel consumption and emission are increased by 0.1% and 0.2% respectively compared with scheme 3. The results above show this scheme can be used to select different recovery points in the 4-dimension diversion and regression path planning process.

Key words: hazardous weather, 4-dimension diversion, return trajectory, trajectory based operation (TBO), estimate arrive time (ETA), ant colony algorithm

WANG Yantao, LIU Kun. Four-dimension diversion and regression path planning method in hazardous weather conditions[J]. China Safety Science Journal, 2023, 33(2): 110-117.

Figures/Tables 6

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References 15

[1]	ICAO. Global air traffic management operational concept[R], 2005.
[2]	AMAB N, LAURENT E C, VU D. Algorithms for multi-aircraft routing under uncertainty[C]. The 2004 International Conference of Recherche Informatique Vietnam-Francophone, 2004:21-32.
[3]	KROZEL J, WEIDENER T, HHUNTER G, et al. Terminal area guidance incorporating heavy weather[C]. Guidance, Navigation, & Control Conference, 1997: DOI: 10.2514/6.1997-3541. doi: 10.2514/6.1997-3541
[4]	ERLANDSSON T. Comparing multi-objective approaches for air route planning in hostile environments[C]. The 12^th International Conference on Modeling Decisions for Artificial Intelligence (MDAI), 2015: 60-71.
[5]	李雄, 徐肖豪, 赵嶷飞, 等. 散点状分布危险天气区域下的航班改航路径规划[J]. 航空学报, 2009, 30(12):2342-2347.
	LI Xiong, XU Xiaohao, ZHAO Yifei, et al. Flight rerouting path planning in dispersedly distributed severe weather areas[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30 (12):2342-2 247.
[6]	王飞, 王红勇. 基于Maklink图和遗传算法的改航路径规划方法研究[J]. 交通运输系统工程与信息, 2014, 14(5):154-160.
	WANG Fei, WANG Hongyong. A re-routing path planning method based on Maklink graph and GA algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(5):154-160.
[7]	陈正茂, 林毅, 杨波. 基于历史数据的空中改航算法研究[J]. 工程科学与技术, 2018, 50(4):110-115.
	CHEN Zhengmao, LIN Yi, YANG Bo. Study on algorithm for rerouting based on historical data[J]. Advanced Engineering Sciences, 2018, 50(4):110-115.
[8]	向征, 张文奇, 张文军. 雷暴天气下基于多航空器冲突避让的路径规划[J]. 中国安全科学学报, 2019, 29(8):151-156. doi: 10.16265/j.cnki.issn1003-3033.2019.08.024
	XIANG Zheng, ZHANG Wenqi, ZHANG Wenjun. Route planning based on multi-aircraft conflict avoidance under thunderstorm weather[J]. China Safety Science Journal, 2019, 29(8):151-156. doi: 10.16265/j.cnki.issn1003-3033.2019.08.024
[9]	陈雨童, 胡明华, 杨磊, 等. 受限航路空域自主航迹规划与冲突管理技术[J]. 航空学报, 2020, 41(9):248-265.
	CHEN Yutong, HU Minghua, YANG Lei, et al. Autonomous trajectory planning and conflict management technology in restricted airspaces[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(9):248-265.
[10]	王岩韬, 刘锟, 赵嶷飞. 雷暴天气下的多航班备降动态优化方案[J/OL]. 工程科学学报:(2023-01-03).DOI:10.13374/j.issn2095-9389.2021.12.30.002. doi: 10.13374/j.issn2095-9389.2021.12.30.002
	WANG Yantao, LIU Kun, ZHAO Yifei. A flight alternate optimization scheme in dangerous weather based on multi expectation[J/OL]. Chinese Journal of Engineering:(2023-01-03).DOI:10.13374/j.issn2095-9389.2021.12.30.002. doi: 10.13374/j.issn2095-9389.2021.12.30.002
[11]	DORIGO M, MANIEZZO V, COLORNI A. Ant system: optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man,and Cybernetics,Part B(Cybernetics), 1996, 26(1): 29-41.
[12]	韩鹏, 张冰玉. 基于改进蚁群算法的无人机安全航路规划研究[J]. 中国安全科学学报, 2021, 31(1):24-29. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.004
	HAN Peng, ZHANG Bingyu. Safety route planning of UAV based on improved ant colony algorithm[J]. China Safety Science Journal, 2021, 31(1): 24-29. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.004
[13]	于飞, 马慧, 陈斐楠, 等. 三维海底栅格地形在潜器路径规划中的应用[J]. 计算机工程与应用, 2016, 52(5): 241-245.
	YU Fei, MA Hui, CHEN Feinan, et al. Application of 3D grid seabed terrain in submersible path planning[J]. Computer Engineering and Applications, 2016, 52(5): 241-245.
[14]	魏志强, 魏路欢. 基于排放影响的自由航路空域中飞行路线优化[J]. 安全与环境学报, 2022, 22(4):2140-2146.
	WEI Zhiqiang, WEI Luhuan. Flight route optimization in free route airspace based on emission effect[J]. Journal of Safety and Environment. 2022, 22(4):2140-2146.
[15]	EUROCONTROL, User manual for the base of aircraft data (BADA)[S]. 2009.

改航方案	航迹回归点	改航航迹距离/km	改航所需时间/min	改航段主用飞行速度/(km·h^-1)	燃油流量/ (kg·min^-1)
1	回归点1	233	19	735	81.9
2	回归点2	517	40	775	83.3
3	回归点3	874	73	718	81.6

改航方案	航班	有无冲突	燃油消耗/kg 与变化比/%	气体总排放量/kg 与变化比/%
改航方案1	FF1	无	6 000	26 520
	FF2	无	2 799	12 366
	FF3	无	3 370	14 889
	—	—	12 169	53 775
改航方案2	FF1	有	6 048/0.8	26 723/0.6
	FF2	有	2 812/0.46	12 432/0.5
	FF3	无	3 370/0	14 889/0
	冲突解脱	FF2因冲突变更航迹:先等待5 min, 等待后解脱速度为751 km/h	12 230/0.5	54 035/0.4
改航方案3	FF1	有	5 957/-0.7	26 325/-0.8
	FF2	有	2 806/0.25	12 397/0.3
	FF3	有	3 393/0.68	14 993/0.7
	冲突解脱	FF2、FF3因冲突变更航迹:FF2等待4 min,调速735 km/h; FF3等待7 min,调速776 km/h	12 156/-0.1	53 715/-0.2