Abstract
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.