Intelligent decomposition control planning method for path of carrying robot in intelligent environment

Abstract

The invention discloses an intelligent decomposition control planning method for paths of a carrying robot in an intelligent environment. The method includes the following steps: step one, constructing a global map three-dimensional coordinate system for carrying areas of a carrying robot, and obtaining a walkable area coordinate in the global map three-dimensional coordinate system; step two, obtaining a training sample set; step three, constructing a global static path planning model of the carrying robot; and step four, obtaining an optical path in real time, and then finishing a transportation task. A path planning model is established through construction of a kernel extreme learning machine optimized by a wolf pack algorithm, a global optimal solution can be quickly found in an intelligent environment, and the problem of local optimum of conventional path planning is avoided.

Description

technical field [0001] The invention belongs to the field of robot path planning, in particular to an intelligent decomposition control planning method for a path of a carrying robot in an intelligent environment. Background technique [0002] With the advent of the era of intelligent manufacturing, delivery robots are more and more widely used in laboratories, factories, and medical intelligent environments to perform tasks such as transporting various parts, test raw materials, and medical items, replacing workers in physical labor, and greatly Increase the level of automation. Among them, the path planning level of the delivery robot determines the operating efficiency of the robot in the actual task, and the time-consuming of the transportation task determines the production and test cycle. How to improve the level of path planning and reduce the time of the transportation task has always been a difficulty in this field. [0003] With the continuous development of artif...

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Problems solved by technology

[0003] With the continuous development of artificial intelligence technology in recent years, intelligent bionic path planning algorithms such as neural network algorithm, genetic algorithm, particle swarm algorithm and artificial fish swarm algorithm have been applied, and a series of research results have been obtained, forming a series of typical planning algorithms, such as Chinese patent CN106094834A discloses a mobile robot path planning method based on a known environment, which mainly relies on the Floyd algorithm, but in actual situations, using the Floyd algorithm in a large space, under the situation of a large number of grid points, calculation disasters will occur, When encountering obstacles such as chairs and trash cans that are easy to change with the environment, the robot will take too long to calculate, which is not conducive to the operation in the actual environment

In the journal "Mobile Robot Dynamic Path Planning Method Based on Improved Ant Colony Algorithm", a path planning algorithm based on improved ant colony algorithm was proposed, which overcomes the local optimal problem, but cannot cope with unknown changes in intelligent complex environments. , in the face of new obstacles, it is impossible to avoid

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