An online human–robot collaborative grinding state recognition approach based on contact dynamics and LSTM

Collaborative state recognition is a critical issue for physical human–robot collaboration (PHRC). This paper proposes a contact dynamics-based state recognition method to identify the human–robot collaborative grinding state. The main idea of the proposed approach is to distinguish between the human–robot contact and the robot–environment contact. To achieve this, dynamic models of both these contacts are first established to identify the difference in dynamics between the human–robot contact and the robot–environment contact. Considering the reaction speed required for human–robot collaborative state recognition, feature selections based on Spearman's correlation and random forest recursive feature elimination are conducted to reduce data redundancy and computational burden. Long short-term memory (LSTM) is then used to construct a collaborative state classifier. Experimental results illustrate that the proposed method can achieve a recognition accuracy of 97% in a period of 5 ms and 99% in a period of 40 ms.