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- Title
An online human-robot collaborative grinding state recognition approach based on contact dynamics and LSTM.
- Authors
Shouyan Chen; Xinqi Sun; Zhijia Zhao; Meng Xiao; Tao Zou
- 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-termmemory (LSTM) is then used to construct a collaborative state classifier. Experimental results illustrate that the proposedmethod can achieve a recognition accuracy of 97% in a period of 5ms and 99% in a period of 40 ms.
- Subjects
ROBOT motion; RANDOM forest algorithms; FEATURE selection; NATURAL language processing; RANK correlation (Statistics); ROBOTS; DYNAMIC models
- Publication
Frontiers in Neurorobotics, 2022, Vol 16, p1
- ISSN
1662-5218
- Publication type
Article
- DOI
10.3389/fnbot.2022.971205