Aircraft optimal path determination method based on mixed probability A star and agent

Abstract

The invention discloses an aircraft optimal path determination method based on mixed probability A star and agent. The method is mainly used to solve problems of too long path length and too large thread value of obstacles to the aircraft. The method comprises steps of: (1) generating a topographical map of the obstacles; (2) arranging a start point SP and a stop point EP of the aircraft in the topographical map of the obstacles; (3) planning a global path of the aircraft by using a probability A star algorithm in accordance with the positions of the start point SP and the stop point EP of the aircraft; and (4) locally optimizing the planned path by using an agent algorithm. The method has advantages of short path length and low thread value, and can be used for determining the optimal path of the aircraft.

Description

technical field [0001] The invention belongs to the technical field of spatial data processing, and in particular relates to path optimization of aircraft. The method can be used for path planning in fields such as intelligent navigation and unmanned driving. Background technique [0002] The aircraft relies on terrain information and enemy information for path planning, that is, under certain constraints, the aircraft searches for the optimal flight path that can preferentially satisfy a specific purpose from the start point to the end point, where the flight path is represented by a series of track points . [0003] In the process of finding the optimal path, path planning needs to balance the path length and the threat value to the aircraft, so path planning plays an important role in artificial intelligence, overall planning, machine learning and spatial database technology. [0004] With the continuous development of military technology and actual war requirements, the...

AI Technical Summary

Problems solved by technology

Compared with the traditional algorithm, the robustness has been significantly improved, and the dependence on the idealization of obstacle objects is reduced. However, the calculation time of the intelligent optimization algorithm is generally too long, and the threat value of the obstacle to the aircraft obtained by the radar equation is relatively large. , and cannot adapt to the dynamic, variable map that is closer to the real scene for path planning

[0008] For other algorithms, such as the dynamic programming algorithm, due to the limitations of the algorithm itself, it can achieve the optimal local path and adapt to the dynamic map, but it cannot be guaranteed in the global planning, and the overall path may not be calculated, and the path result is unstable.

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