Method for determining optimal path of flight vehicle based on self-adaptive A star algorithm

Abstract

The invention discloses a method for determining an optimal path of a flight vehicle based on a self-adaptive A star algorithm, which is mainly used for solving the problems of excessively long length of the divided path, excessively large threat value of an obstacle to the flight vehicle and excessively large number of waypoints of the prior art. The method comprises the following implementation steps: (1) setting a start point SP and an end point EP of the flight vehicle in a map; (2) generating an obstacle region; (3) performing grid division on the obstacle region to obtain a grid map; (4) setting a grid where the start point SP of the flight vehicle is located as SG and setting the grid where the end point EP is located as EG in the grid map; (5) performing path planning according to the grid SG where the start point of the flight vehicle is located and the grid EG where the end point is located. The method disclosed by the invention has the advantages of short path length, low threat value and a small number of waypoints, and can be used for determining the optimal path of the flight vehicle.

Description

technical field [0001] The invention belongs to the technical field of spatial data processing, and in particular relates to path planning of an 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 starting point to the ending point, where the flight path is represented by a series of waypoints. [0003] With the continuous development of military technology and actual war requirements, the path planning modeling of aircraft has developed from a single system to a complex and changeable system. The path planning problem of aircraft presents complex characteristics such as real-time, multi-dimensionality, uncertainty and nonlinearity. The models established un...

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 has been reduced. However, the calculation time of the intelligent optimization algorithm is generally too long to adapt to the dynamic and variable map that is closer to the real scene. route plan

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

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