基于双向长短期记忆模型的起重机智能操控方法

doi:10.13229/j.cnki.jdxbgxb20190231

Abstract

Abstract:

To solve the problem that it is difficult for commander on the ground and driver to operate cranes cooperatively, an intelligent control method of the crane based on BiLSTM model was proposed, which realizes single-person control of crane, thus, reducing manpower cost. Firstly, Kinect sensors were used to collect the coordinates of human joints from the commander's instructions. Then, the vectors of human joints were constructed using the coordinates, and two kinds of characteristic matrices that distinguish different instructions were constructed by calculating the angles and modulus ratios between the vectors. After that, the feature matrix of angle was input into the instruction recognition network based on BiLSTM model, and the recognition results were compared with those of support vector machine (SVM) and back propagation (BP) neural networks. Finally, the feature matrices of angle and modulus ratio were fused to improve the accuracy. The experimental results show that the instruction recognition network designed in this paper achieves the highest accuracy rate and the proposed fusion recognition method utilizes the information of multiple features effectively. For the training set, the recognition rate can reach 99.13%, and for the test set, this rate can reach 96.75%.

Key words: intelligent operation of crane, identification of lifting instructions, characteristic matrix, BiLSTM model, multi-features fusion recognition

CLC Number:

TH218

Tao NI,Hai-qiang LIU,Lin-lin WANG,Shao-yuan ZOU,Hong-yan ZHANG,Ling-tao HUANG. Intelligent manipulation method of crane based on BiLSTM model[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 445-453.

Figures/Tables 18

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Table 1

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Table 2

Features of modulus ratios"

简称	模比值	简称	模比值
$r 1$	$r b, s l$	$r 7$	$r b, s r$
$r 2$	$r b, e l$	$r 8$	$r b, e r$
$r 3$	$r b, w l$	$r 9$	$r b, w r$
$r 4$	$r b, k l$	$r 10$	$r b, k r$
$r 5$	$r b, a l$	$r 11$	$r b, a r$
$r 6$	$r b, h$

Table 2

Table 3

Features of angles"

简称	夹角	简称	夹角
$θ 1$	$l s r, e r, l s s, s r$	$θ 9$	$l s r, e r, l s s, s r$
$θ 2$	$l b, s l, l s, s l$	$θ 10$	$l b, s r, l s, s r$
$θ 3$	$l s l, e l, l e l, w l$	$θ 11$	$l s r, e r, l e r, w r$
$θ 4$	$l b, e l, l s l, e l$	$θ 12$	$l b, e r, l s r, e r$
$θ 5$	$l b, h l, l h l, k l$	$θ 13$	$l b, h r, l h r, k r$
$θ 6$	$l h l, k l, l k l, a l$	$θ 14$	$l h r, k r, l k r, a r$
$θ 7$	$l b, k l, l h l, k l$	$θ 15$	$l b, k r, l h r, k r$
$θ 8$	$l b, s s, l s s, h$	$θ 16$	$l b s, s r, l s s, h$

Table 3

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Fig.8

Fig.9

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Fig.11

Table 4

Fig.12

Table 5

Table 6

References 20

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关节点	简称	关节点	简称
左手腕	wl	右手腕	wr
左肘	el	右肘	er
左肩	sl	右肩	sr
左脚踝	al	右脚踝	ar
左膝	kl	右膝	kr
左髋	hl	右髋	hr
肩椎	ss	基节点	b
头	h

识别模型	训练集/%	测试集/%
SVM	80.75	77.50
BP神经网络	89.13	85.25
图8网络	93.38	92.25

识别模型	训练集%	测试集/%
图8网络（输入特征矩阵Θ）	93.38	92.25
图8网络（输入特征矩阵R）	92.38	89.75
图9(a)网络（拼接R和Θ融合）	90.13	85.75
图9(b)网络（加权融合）	99.13	96.75

	预备	要主钩	要副钩	吊钩升	吊钩降	升臂	降臂	结束
预备	0.96	0	0	0.04	0	0	0	0
要主钩	0	0.94	0	0.06	0	0	0	0
要副钩	0	0	1.00	0	0	0	0	0
吊钩升	0.04	0.04	0	0.92	0	0	0	0
吊钩降	0.04	0	0	0	0.96	0	0	0
升臂	0	0	0	0	0	0.98	0.02	0
降臂	0	0	0	0	0	0.02	0.98	0
结束	0	0	0	0	0	0	0	1.00

Intelligent manipulation method of crane based on BiLSTM model

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