# Spatial Error Prediction and Compensation of Industrial Robots Based on Extended Joints and BO-XGBoost

## TL;DR

This paper improves the positioning accuracy of industrial robots by predicting and compensating for spatial errors using a novel joint angle feature and an optimized machine learning model.

## Contribution

The paper introduces extended joint angles and a BO-XGBoost model for high-accuracy spatial error prediction in industrial robots.

## Key findings

- The proposed method reduced mean position error by 90.62% after compensation.
- Maximum error decreased by 85.88%, and standard deviation by 84.54%.
- BO-XGBoost outperformed Decision Tree, K-Nearest Neighbors, and Random Forest models.

## Abstract

Robotic positioning accuracy is paramount in complex tasks. This accuracy is influenced by both geometric and non-geometric factors, making error prediction a significant challenge. To address this, this paper introduces two key contributions. First, we propose a novel input feature, the robot’s “extended joint angles,” which incorporates joint reversal information to better capture non-geometric errors like gear backlash. Second, we develop a high-accuracy spatial error prediction model by combining the Extreme Gradient Boosting (XGBoost) algorithm with Bayesian Optimization (BO) for hyperparameter tuning. The BO-XGBoost model establishes a direct non-linear mapping from the extended joint angles to the positioning error. Experimental results demonstrate that after compensation, the mean position error was reduced from 1.0751 mm to 0.1008 mm (a 90.62% decrease), the maximum error from 3.3884 mm to 0.4782 mm (an 85.88% decrease), and the standard deviation from 0.5383 mm to 0.0832 mm (an 84.54% decrease). A comparative analysis against Decision Tree, K-Nearest Neighbors, and Random Forest models further validates the superiority of the proposed method in reducing robot position error.