Robust Human Upper-Limbs Trajectory Prediction Based on Gaussian Mixture Prediction

Abstract

Accurate prediction of human motion trajectory can improve the security of human-robot cooperation. Due to the unstructured nature of collaborative workspace and the uncertainty of sensor sensing data, the trajectory prediction accuracy of traditional prediction algorithms is low, and the uncertainty is difficult to estimate. Aiming at the complex characteristics of human upper limb movement patterns, this paper proposes a robust upper limb end trajectory prediction algorithm. The robust Gaussian mixture model was used to model the trajectory of human upper limb movement, and the statistical values of the future trajectory were obtained by combining Gaussian mixture regression. The advantage of this algorithm is that the prediction result is not only the predicted value of the position, but also the probability distribution of all possible future motion trajectories of the upper limb. The position prediction information in a specific motion mode can be obtained by using probability and statistical distribution characteristics. The algorithm is tested on both public and private datasets. Experimental results show that this method can predict human trajectories well.