Adaptive switching route planning method of automatic conveying trolley

Abstract

The invention discloses an adaptive switching route planning method of an automatic conveying trolley and belongs to the technical field of route planning. The method includes the steps of adopting agrid method for creating a two-dimensional work environment model of the automatic conveying trolley in a warehouse; on the basis of the two-dimensional work environment model, utilizing a detection device of the automatic conveying trolley to identify the current work environment; according to the current work environment, adaptively switching a genetic algorithm and a particle swarm algorithm for route planning. According to the scheme, the genetic algorithm is combined with the particle swarm algorithm, the method has not only has the excellent global searching capability of the genetic algorithm but also the excellent local searching capability and high convergence speed of the particle swarm algorithm, the work efficiency is improved, and requirements for an optimal route are met.

Description

technical field [0001] The invention relates to the technical field of path planning, in particular to a path planning method for an automatic transport trolley based on adaptive switching between a genetic algorithm and a particle swarm algorithm. Background technique [0002] Path planning is one of the key technologies of the automatic handling trolley. The purpose is to determine the optimal path from the starting position to the destination position. To a certain extent, it marks the level of intelligence of the automatic handling trolley and reflects the efficiency of the automatic handling trolley. and reliability. [0003] At present, the commonly used path planning methods include fuzzy reasoning method, heuristic graph search method, artificial potential field method, A* algorithm, etc. These algorithms have their own advantages and disadvantages: for example, the biggest advantage of the fuzzy reasoning method is that it has very good real-time performance, but t...

AI Technical Summary

Problems solved by technology

These algorithms have their own advantages and disadvantages: for example, the biggest advantage of the fuzzy reasoning method is that its real-time performance is very good, but the design of fuzzy membership functions and the formulation of fuzzy control rules mainly rely on human experience.

Therefore, none of the above-mentioned path planning methods can be applied to the path planning of automatic handling vehicles working in complex environments.

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