Robot Collisions Classification Based on Variational Mode Decomposition of Vibration Measurements

The future smart factory necessitates the identification of robot collisions, including the materials involved, the specific robot component impacted, and whether a human is involved. This article introduces a method for classifying robot collisions based on motor current and robot link vibration. First, it analyzes vibration modal features that can reveal the collision’s location and the contacting material. Thereafter, it employs variational mode decomposition (VMD) to analyze the vibration characteristics of collisions, with optimization of the decomposition parameter. To address computational complexity, an equivalent filter bank is proposed for extracting collision features instead of VMD. These vibration features are then utilized to construct and train a neural network for collision detection and classification. Finally, the proposed method is verified on a robot platform, successfully distinguishing and detecting collisions involving four different materials: human hand, cotton hammer, rubber hammer, and steel hammer. The accuracy of detection is evaluated using support vector machine (SVM), back propagation (BP), radial basis function (RBF), and convolutional neural network (CNN) through comprehensive experiments. The primary contribution of this study lies in the accurate differentiation between human-involved and nonhuman-involved collisions, facilitating real-time robot collision monitoring amid varying loads and offering insights into the collision’s location. Experimental results demonstrate a 90% accuracy in human collision detection and a 95% accuracy in collision link detection. Experiment data and code for training and testing of neural networks are available at
https://github.com/ldepn/Robot-collision-VMD.git
.