Continuous Wavelet Network for Efficient and Transferable Collision Detection in Collaborative Robots

Abstract

This article addresses the crucial aspect of safety in collaborative robotics by introducing a new continuous wavelet transform-convolutional neural network (CWT-CNN) for efficient robot collision detection. Unlike conventional methods, CWT-CNN exhibits superior data efficiency, requiring minimal collision data for robust training without relying on a dynamic model. The network’s adaptability extends to varying internal stiffness levels, offering robustness to changes in robotic system characteristics. Through comprehensive experimental studies, we investigate the impact of input signal types, wavelet types, wavelet scale ranges, and time-moving window sizes on collision detection performance, offering critical insights for optimal CWT parameter selection. Additionally, our transferability analysis demonstrates that the CWT-CNN can seamlessly adapt from one joint to another, requiring only minimal free-motion data from the new joint. This adaptability is validated through extensive experiments on an industrial robot and the robot equipped with variable stiffness actuators. In conclusion, the CWT-CNN is highly generalizable and data-efficient, making it a reliable solution for real-time collision detection in human-robot interactions, addressing a key aspect of safety in collaborative environments.