In this article, we discuss the calibration of a robot arm using regularization techniques. The goal is to improve the accuracy of the robot’s movements by adding a norm-based regularization term to the cost function. We explore the use of different degrees of polynomials and regularization parameters to achieve optimal results.
Calibration Algorithm
The proposed algorithm rewrites the affine model for each element of y in R6 as a polynomial of degree np, where np is the number of outputs. The polynomial includes terms up to np-1, including the offset term. To improve stability and performance, feature normalization is applied to have all features on a similar scale.
Experiments
The article presents experiments with higher-degree models (np > 1) and explores the effect of regularization on the calibration process. A group of 4th-degree polynomial models was built using the algorithm in III-A, but with different values of λ in each case. The selected values of λ are shown in Table III.
Results
The results show that regularization improves the performances and stability of the model. The validation curve shows a similar shape for all models, indicating a good tradeoff between accuracy and complexity. The best-performing model is obtained with np = 3 and λ = 0.1.
Conclusion
In conclusion, this article demonstrates the effectiveness of regularization techniques in improving the calibration of a robot arm. By adding a norm-based regularization term to the cost function, we can reduce overfitting and improve the accuracy of the robot’s movements. The optimal values of λ depend on the degree of the polynomial and the complexity of the model, and the proposed algorithm provides a systematic way to find these values. The results show that regularization is an essential tool in robotics, enabling the development of more accurate and robust control systems.
