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Deep learning control planning method of movement routes of robot in intelligent environment

A technology of robot movement and deep learning, applied in the direction of vehicle position/route/height control, non-electric variable control, control/regulation system, etc., can solve the problem of fast search speed

Active Publication Date: 2017-11-21
CENT SOUTH UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

A* algorithm is the most effective direct search method in robot path planning, with fast search speed and high efficiency, but it is easy to fall into local optimum

Method used

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  • Deep learning control planning method of movement routes of robot in intelligent environment
  • Deep learning control planning method of movement routes of robot in intelligent environment
  • Deep learning control planning method of movement routes of robot in intelligent environment

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Embodiment Construction

[0082] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0083] Such as figure 1 As shown, a robot movement path deep learning control planning method in an intelligent environment includes the following steps:

[0084] Step 1: Construct a three-dimensional coordinate system of the global map for the carrying area of ​​the carrier robot, and obtain the coordinates of the walkable area under the three-dimensional coordinate system of the global map;

[0085] The ground center point of the carrying area is the origin, the due east direction is the X axis, the true north direction is the Y axis, and the vertical ground direction is the Z axis;

[0086] The carrying area of ​​the carrying robot is all the floor connecting areas in a building, and the walkable area refers to the area where obstacles in the building are deleted from all the floor connecting areas;

[0087] In the three-dimensional coordinate syst...

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Abstract

The invention discloses a deep learning control planning method of movement routes of a robot in an intelligent environment. The method comprises steps of step 1, constructing a global map three-dimensional coordinate system for a carrying region of a carrying robot, and acquiring a walkable region coordinate under the global map three-dimensional coordinate system; step 2, acquiring a training sample set; step 3, constructing a global static route planning model of the carrying robot; and step 4, inputting a start point and destination coordinate in a transmission task into the global static route planning model based on a fuzzy neural network and acquiring an optimal planed route corresponding to the carrying robot. According to the invention, by establishing the global static route planning module and a global dynamic obstacle avoiding planning model, and using quite strong non-linear fitting characteristics of the deep learning, the global optimal path can be quickly found and a problem is solve that the normal route planning often falls into the local optimization.

Description

technical field [0001] The invention belongs to the field of robot path planning, and in particular relates to a deep learning control planning method for a robot motion path in an intelligent environment. Background technique [0002] With the trend of industry 4.0 in the world, 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 for physical labor, and greatly Increase the level of automation. Among them, path planning, as the key technology of mobile robot navigation, directly determines the quality of the robot's completion of transportation tasks. [0003] Current typical path planning methods include: traditional grid method, artificial potential field method, Dijkstra algorithm, A* algorithm, Voronoi diagram, etc. Intelligent algorithms include fuzzy rule method, neural network algorithm, genetic alg...

Claims

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0221
Inventor 刘辉李燕飞黄家豪段超王孝楠
Owner CENT SOUTH UNIV
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