Genetic ant algorithm-based unmanned aerial vehicle global path planning method

Abstract

The invention discloses a genetic ant algorithm-based unmanned aerial vehicle global path planning method. The method includes the following steps that: modeling is performed for an environment through an improved grid method; a part of optimal solutions obtained by an MMAS (Max-Min Ant System) algorithm are transformed into the initial solutions of an EGA (Elitist Genetic Algorithm); the MMAS algorithm and the EGA are utilized to simultaneously perform path optimization; and an improved mutation operator and the MMAS algorithm are utilized to perform further optimization, and an optimal path is obtained. According to the genetic ant algorithm-based unmanned aerial vehicle global path planning method of the invention, a new sub-region division method is adopted, and therefore, the expression forms of the solutions are simplified, and the computation quantity and storage quantity of data are decreased; according to the characteristics of the convergence rates of the MMAS algorithm and the EGA, a method according to which iteration optimal solutions and optimal solutions obtained through EGA optimization are adopted each time to jointly update pheromones is adopted, and therefore, the search efficiency of the optimal solution can be improved; and when the algorithm is stagnant, the number of ants is increased, and the EGA mutation operator is improved, and therefore, the search efficiency of the optimal path can be further improved.

Description

technical field [0001] The invention mainly relates to the technical field of intelligent algorithms for unmanned autonomous underwater vehicles (AUVs), in particular to an AUV path planning method based on a genetic ant algorithm. Background technique [0002] Unmanned autonomous underwater vehicle (AUV) is a kind of underwater robot, which relies on its own autonomy and control to complete the assigned mission. At present, the strategic position of marine and marine science has risen sharply, with two major development trends of global change and deep sea, gradually forming a new pattern of expansion from nearshore to pelagic, and from shallow water to deep sea. AUV has small targets and strong underwater concealment. It has immeasurable potential application value in detailed ocean survey, early warning search and reconnaissance, anti-submarine, anti-mine, defense, and combat support. Path planning is an essential and important function to ensure the safety of AUV naviga...

AI Technical Summary

Problems solved by technology

[0013] Generally speaking, in the existing path planning methods, the environmental modeling part has improved greatly in terms of data storage and operation speed through the improvement of the traditional grid method; but the hybrid algorithm does not make full use of path planning. The characteristics of the fusion strategy need to be further improved, and the search efficiency and search accuracy of the optimal solution need to be further improved.

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