Warehouse logistics AGV path planning algorithm based on ant colony algorithm and improved genetic algorithm

Abstract

The invention discloses a warehouse logistics AGV path planning algorithm based on an ant colony algorithm and an improved genetic algorithm, relates to the ant colony algorithm and the genetic algorithm, and overcomes the defects that a traditional method is time-consuming in calculation, easy to premature and converge and easy to fall into local optimum. The invention comprises the following steps: 1, establishing gridding division and coding for a site; 2, generating an initial AGV path for genetic evolution by using an improved ant colony algorithm based on obstacle information; 3, iteratively selecting an AGV optimal path based on a three-stage genetic algorithm; 4, carrying out tail end intersection on different AGV paths with overlapping points; 5, performing favorable variation on the AGV path; and 6, recalculating the AGV path fitness, judging whether iteration is terminated or not, and carrying out trajectory smoothing processing on the terminated AGV path. The basic idea of the invention is that the improved ant colony algorithm and the improved genetic algorithm are combined, the iterative convergence speed is increased, the AGV path with higher operation efficiency is obtained, and the engineering applicability is high.

Description

technical field [0001] The present invention relates to automated logistics warehousing technology, in particular to warehousing logistics Automated Guided Vehicle (Automated Guided Vehicle, AGV) route planning and trajectory optimization technology, in particular to a storage AGV route planning technology based on the combination of ant colony algorithm and genetic algorithm. Background technique [0002] The path planning problem of warehousing and logistics AGV needs to be solved by more advanced algorithms, especially the intelligent planning technology based on genetic algorithm and ant colony algorithm is continuously attracting the attention of researchers. More and more scholars and experts hope to improve the traditional genetic algorithm and ant colony algorithm, and apply them to the path planning algorithm of warehousing AGV to improve the operation efficiency of warehousing logistics. Also based on this technical background, the present invention makes a certain...

AI Technical Summary

Problems solved by technology

The use of ant colony algorithm alone in the path planning of warehouse AGV often has the problem of slow convergence and easy to fall into local optimum; while the use of traditional genetic algorithm alone also has its own defects, mainly manifested in that it is prone to premature convergence (the planned path is far from When the global optimal path is reached, the iteration termination condition set by the algorithm is satisfied, resulting in unsatisfactory path planning effect), parameter setting is too dependent on experience, etc.

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