4.5 Article

Data-driven gait model for bipedal locomotion over continuous changing speeds and inclines

Journal

AUTONOMOUS ROBOTS
Volume 47, Issue 6, Pages 753-769

Publisher

SPRINGER
DOI: 10.1007/s10514-023-10108-6

Keywords

Data-driven model; Kinematic trajectory; Gait model; Loss function; Wire-frame

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To tackle the complexity of trajectory generation for biped robots on uneven terrain, a data-driven Gait model is proposed in this paper. Deep learning methods are employed to develop seven different data-driven models, with LSTM+GRU-based model showing the best performance. Experimental results demonstrate the superiority of the proposed Gait models over traditional finite state machine and Basis models in terms of error summary statistics.
Trajectory generation for biped robots is very complex due to the challenge posed by real-world uneven terrain. To address this complexity, this paper proposes a data-driven Gait model that can handle continuously changing conditions. Data-driven approaches are used to incorporate the joint relationships. Therefore, the deep learning methods are employed to develop seven different data-driven models, namely DNN, LSTM, GRU, BiLSTM, BiGRU, LSTM+GRU, and BiLSTM+BiGRU. The dataset used for training the Gait model consists of walking data from 10 able subjects on continuously changing inclines and speeds. The objective function incorporates the standard error from the inter-subject mean trajectory to guide the Gait model to not accurately follow the high variance points in the gait cycle, which helps in providing a smooth and continuous gait cycle. The results show that the proposed Gait models outperform the traditional finite state machine (FSM) and Basis models in terms of mean and maximum error summary statistics. In particular, the LSTM+GRU-based Gait model provides the best performance compared to other data-driven models.

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