Aircraft auto-route planning method with combination of searching and optimization

Abstract

The invention discloses an aircraft auto-route planning method with combination of searching and optimization. The method includes the steps of: 1) searching an available route by means of improved A* algorithm; 2) performing pretreatment for optimization of the available route; 3) optimizing the available route by means of particle swarm evolution algorithm; and 4) performing after-treatment of the optimized route. The method combines the searching capability of the improved A* algorithm and the optimization capability of the particle swarm evolution algorithm, wherein the improved A* algorithm is used for quickly searching an available route, thus ensuring that a feasible solution can be obtained within a short time by means of the planning method and increasing the robustness of the planning method; and then the particle swarm evolution algorithm optimizes the searched route to obtain a better optimized route, thereby improving optimization performance of the planning method.

Description

technical field [0001] The invention relates to the field of intelligent unmanned aircraft track planning, in particular to an autonomous aircraft track planning method combining search and optimization. Background technique [0002] With the deepening of intelligent algorithms, the research on applying intelligent algorithms to aircraft track trajectory planning technology has also been greatly developed. In the existing aircraft intelligent trajectory planning technology, the performance of the planning technology varies according to the different intelligent algorithms used. The main reason is that different intelligent algorithms have different emphasis on the two points of search and optimization: 1) A* algorithm and its improved algorithm, ant colony algorithm and other intelligent algorithms focus on search, and can quickly search to obtain a feasible path. Its advantage lies in the rapidity and robustness of the algorithm, but once a feasible path is searched, the pl...

AI Technical Summary

Problems solved by technology

However, since the algorithm itself is more focused on optimization, although the optimal path can be obtained, most of the algorithm execution time is often spent in the search process, which affects the efficiency of the algorithm, and may even fail to obtain the required track within the specified time.

The trajectory planning technology that uses these two types of intelligent algorithms respectively has performance deficiencies

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