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Loading robot arm control self-adaption blended learning mapping intelligent control method and system

A robot arm and hybrid learning technology, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve problems such as impracticality, neural network needs to be retrained, robot maintenance inconvenience, etc., to achieve the effect of improving stability and facilitating maintenance

Active Publication Date: 2017-10-17
CENT SOUTH UNIV
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AI Technical Summary

Problems solved by technology

However, the method of obtaining a large number of samples by solving the kinematic equation is not practical, and the replacement of the motor at the joint of the robot arm will make the previously constructed neural network need to be retrained
Therefore, there is a great inconvenience in the maintenance of the robot

Method used

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  • Loading robot arm control self-adaption blended learning mapping intelligent control method and system

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Embodiment Construction

[0106] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0107] The schematic diagram of the grabbing process of the carrier robot is as follows: figure 1 Shown; Application control method of the present invention controls the flow chart of robot arm grasping as Figure 4 shown.

[0108] An adaptive hybrid learning mapping intelligent control method and system for manipulating a carrier robot arm, comprising the following steps:

[0109] Step 1: Fix the starting point a of the grasping task where the carrying robot is located in the designated grasping area, use the remote server to control the arm of the carrying robot to repeat multiple grasping trainings, and obtain the grasping sample set;

[0110] Each grasping sample includes the process of the carrying robot moving from the fixed starting point a of the grasping task to the grasping end point b, passing through each moving point in turn, the control ...

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Abstract

The invention discloses a loading robot arm control self-adaption blended learning mapping intelligent control method and system. The method comprises the first step of using a remote server to control a loading robot arm to repeatedly conduct grabbing training multiple times to obtain a grabbing sample set; the second step of selecting an initial training sample according to an arm motion power consumption amount; the third step of conducting joint classification on the initial training sample according to the change amplitudes of all arm joint control values to obtain various joint sample sets; the fourth step of using the various joint sample sets as a final training set to construct a prediction model for the loading robot arm joint control values; the fifth step of inputting positions of all movable points to the prediction model in sequence to obtain the control values of all joints on each movable point, and then completing a grabbing task. According to the loading robot arm control self-adaption blended learning mapping intelligent control method and system, by constructing the map between the distance of a robot base and a grabbing platform and a robot arm posture, complicated construction of a kinematical equation is avoided; accurate joint control values are obtained, and the joints are flexibly controlled.

Description

technical field [0001] The invention belongs to the field of robot control, and in particular relates to an intelligent control method and system for manipulating an adaptive hybrid learning mapping of a carrying robot arm. Background technique [0002] In recent years, mobile robots have been widely used in indoor transportation, such as hospitals using mobile robots to transport medical equipment, service robots in supermarkets, and robots in factory manufacturing environments. The robot arm is an important part of the robot's mechanical system, and it is also the main carrier for the robot to realize its service functions. [0003] The control problem of the mechanical arm has always been a difficult problem in the industry. In the early days, most of the arms used PID control, which could achieve tracking at a speed below the middle level. However, in the case of high precision and fast speed, the traditional PID control could not meet the control requirements. In this...

Claims

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/1602B25J9/163
Inventor 刘辉李燕飞金楷荣
Owner CENT SOUTH UNIV
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