Research on Positioning Accuracy Compensation of 6-DoF Robots Based on ISSA-LSSVR

doi:10.12068/j.issn.1005-3026.2025.20240136

Abstract

Abstract:

To enhance the positioning accuracy of six-degree-of-freedom （6-DoF） robots， a method for predicting and compensating for positioning errors of the 6-DoF robot was proposed. A hierarchical line-by-line sampling strategy in the high-frequency workspace of the robot was introduced， and a cumulative measurement error correction formula was established to improve measurement accuracy. Experimental results demonstrate that the robot’s working position significantly affects absolute errors. To address this issue， an error compensation model based on an improved sparrow search algorithm-optimized least squares support vector regression （ISSA-LSSVR） was developed to predict and correct the robot’s inherent positioning errors. The results indicate that， relative to the support vector regression （SVR） algorithm， least squares support vector regression （LSSVR） algorithm， and sparrow search algorithm-optimized LSSVR （SSA-LSSVR）， the ISSA-LSSVR algorithm achieves superior compensation performance. Specifically， the absolute error is reduced by 65.68%， and the maximum error is decreased by 68.95%.

Key words: 6-DoF robot, error measurement, cumulative measurement error, ISSA-LSSVR algorithm, error compensation

CLC Number:

TP 242.2

Hua-yu YU, Wen-fu ZHU, Bo XIN, Jun-feng SUN. Research on Positioning Accuracy Compensation of 6-DoF Robots Based on ISSA-LSSVR[J]. Journal of Northeastern University(Natural Science), 2025, 46(12): 48-56.

Figures/Tables 13

Fig.1 Robot error measurement system

Fig.2 Sampling point planning

Fig.3 Comparison of trajectory between theoretical sampling points and measured points

Fig.4 Generation and accumulation of measurement

Fig.5 Absolute error of measured point samples

Fig.6 Absolute error of measured point samples in the 4th and 6th layers

Fig.7 Distribution fitting results of error probability density

Fig.8 LSSVR algorithm structure

Fig.9 Experimental point position measurement

Fig.10 Experimental point error prediction results in all directions

Fig.11 Comparison of prediction accuracy of robot’s X-axis error training set

Fig.12 Error comparison after compensation

Table 1 Compensation effect of different SVR algorithms

算法	绝对误差最大值/mm	绝对误差平均值/mm	方差/mm²
未补偿	0.380 4	0.169 4	0.005 4
SVR	0.207 0	0.080 9	0.001 8
LSSVR	0.164 8	0.069 3	0.001 1
SSA-LSSVR	0.133 1	0.059 4	0.000 7
ISSA-LSSVR	0.118 4	0.057 5	0.000 6

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