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

A technology of intelligent environment and static path planning, applied in the direction of vehicle position/route/height control, non-electric variable control, control/regulation system, etc., can solve the problem of being unable to cope with unknown changes in intelligent and complex environments, and robots taking too long Calculate and calculate disasters and other issues to achieve the effect of improving obstacle avoidance processing efficiency, shortening obstacle avoidance time, and simple learning rules
CN107436604AActive Publication Date: 2017-12-05CENT SOUTH UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Current Assignee / Owner
CENT SOUTH UNIV
Publication Date
2017-12-05

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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.
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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...

Claims

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