Author(s)

Lei Wu, Fu Liu

Corresponding Author

Lei Wu

Abstract

This study aims to explore a collision position prediction method based on the end-effector of a multi-degree-of-freedom industrial robot for the problem of potential collision risk during the movement of industrial robots in complex environments. By collecting and analyzing a large amount of data on the motion trajectories and collision events of industrial robots in real production environments, combined with feature extraction and machine learning model development techniques, it aims to achieve accurate prediction of collision location and early warning. Relevant research status and methods in the field of industrial robot collision prediction are discussed in depth, which provide the theoretical basis and inspiration for this study. In the methodology section, the way and process of data collection, as well as the specific methods of feature extraction and machine learning model development are described in detail. The specific performance of the collision position prediction model in the experiment is demonstrated, and its performance is comprehensively evaluated and thoroughly discussed. The results of this study will provide important theoretical support and practical guidance for the safe operation of industrial robots in complex environments, and are expected to produce positive application value and promotion in the field of industrial automation.

Keywords

industrial robot, end-effector, collision position prediction, multiple degrees of freedom, data collection, feature extraction, model development