A Bayesian Framework Based on Gaussian Mixture Model and Hidden Markov Process for Collision Detection in Cobots

Abstract

In this letter, we propose the B-GHF framework, an end-to-end collision state inference method based on a Bayesian framework that does not rely on external force/torque (F/T) sensors in the human-robot collaboration (HRC) environment. This method integrates GMM for probabilistic object position and error analysis, HMP for temporal collision state evolution, and BNN for observational uncertainties. Dynamic collision state assessment and decision uses multi-joint state-weighted integration and recursive Bayesian updates. The experimental results show that B-GHF achieves a detection success rate of 98.36% and an average detection time of 8.34 ms, significantly outperforming both a state-of-the-art (SOTA) learning-based method (MCD-CNN) and a classic model-based approach (MO-ID) in terms of accuracy, speed, and robustness.