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Multi-Agent airport surface taxiing path planning method based on historical data analysis

A technology based on historical data and airport scenes, applied in the field of airport surface path planning, can solve problems such as ignorance of planning and scarcity of aircraft taxi path planning, and achieve the effect of reducing the probability of conflicts

Active Publication Date: 2021-11-05
SICHUAN UNIV
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Problems solved by technology

[0004] At present, there are many researches in the field of airport scenes at home and abroad, but the research on aircraft taxiing path planning is very rare, and many researchers pay more attention to the resolution of conflicts, ignoring the planning

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  • Multi-Agent airport surface taxiing path planning method based on historical data analysis
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  • Multi-Agent airport surface taxiing path planning method based on historical data analysis

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Embodiment Construction

[0032] A multi-Agent taxiing path planning method for airport surfaces based on historical data analysis, characterized in that: including taxiing historical data analysis and processing, establishment of Agent model, Q value network construction, real-time path planning strategy;

[0033] based on figure 1 For the types of conflicts shown, the taxiing history data analysis and processing module mainly processes the aircraft taxiing records of the current airport in the past year, including aircraft taxiing trajectories, key node speed parameters and time stamp records, providing basic data for subsequent Agent learning.

[0034] Agent model establishment, including aircraft Agent and intersection Agent, such as figure 2 As shown, the aircraft Agent is mainly responsible for the shortest path search in the model, sorts the Q values ​​of adjacent nodes in the established Q value network, and then selects the node that satisfies both the current conflict and the largest Q value...

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Abstract

The invention discloses a multi-Agent airport surface taxiing path planning method based on historical data analysis, and the method carries out the real-time dynamic planning of a surface aircraft taxiing path based on the analysis of the historical surface taxiing data of an aircraft in combination with a Q learning algorithm in reinforcement learning. According to the method, the possibility of aircraft ground conflict can be greatly reduced, and the scene operation efficiency can be improved. According to the method, different dimensions, including time sequence data and scene resources, of historical sliding data are analyzed, conflict hot spot areas and conflict peak periods in the historical operation process are obtained, and then intelligent learning is carried out by setting corresponding Agents. A searching strategy based on the shortest path is adopted, the sliding path is shortest under the constraint conditions of the minimum safety interval, sliding speed constraint and the like, and a corresponding priority conflict avoiding method is adopted for solving the conflict when the conflict occurs.

Description

technical field [0001] The invention relates to a multi-agent airport surface taxiing path planning method based on historical data analysis, belonging to the field of airport surface path planning. Background technique [0002] In recent years, with the rapid development of the civil aviation management industry, people pay more and more attention to the punctuality of flights, and the demand for user experience is getting higher and higher, while the operation efficiency of the flight scene affects the progress of the flight to a certain extent. In order to meet this demand In the case of continuous growth and extremely limited hardware resources (such as runway resources, apron resources, etc.), it is necessary to develop an intelligent surface management system to achieve a maximum utilization of airport resources, optimization of surface operation efficiency, and maximum economic benefits. intelligent solution. [0003] The key to improving the efficiency of surface op...

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Application Information

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IPC IPC(8): G06Q10/04G06N5/00G06N20/00G08G5/06
CPCG06Q10/047G06N20/00G08G5/06G06N5/01Y02T10/40
Inventor 韩云祥张建伟何爱平
Owner SICHUAN UNIV
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