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

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Abstract

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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Abbreviations

DNN:

Deep neural network

LSTM:

Long short term memory

GRU:

Gated recurrent units

BiLSTM:

Bidirectional LSTM

BiGRU:

Bidirectional GRU

COMBO BI:

BiLSTM + BiGRU

FSM:

Finite state machine

MAE:

Mean absolute error

MSE:

Mean squared error

SE:

Standard error

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Electrical Engineering, MNIT, Jaipur, India

    Bharat Singh

  2. Center for Converging Technologies, University of Rajasthan, Jaipur, India

    Suchit Patel

  3. Department of Electrical Engineering, MNIT, Jaipur, India

    Ankit Vijayvargiya & Rajesh Kumar

  4. Department of Electrical Engineering, SKIT, Jaipur, India

    Ankit Vijayvargiya

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  1. Bharat Singh
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  2. Suchit Patel
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  3. Ankit Vijayvargiya
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  4. Rajesh Kumar
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Contributions

Mr. Bharat Singh has contributed to develop the methodology, analysis, and drafting of this paper by carrying out a thorough study of the literature regarding the mentioned topics. He has also contributed in the sense of presenting a comparison between the various techniques of gait models. All authors read and approved the final manuscript. Mr. Suchit Patel has contributed on the development of gait model. Mr. Ankit Vijayvargiya has contributed in analyzing the results and provided the suggestion in developing the gait models. Prof. Rajesh Kumar has also contributed this work by supervising all aspects related to methodology, analysis and drafting.

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Correspondence to Bharat Singh.

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Singh, B., Patel, S., Vijayvargiya, A. et al. Data-driven gait model for bipedal locomotion over continuous changing speeds and inclines. Auton Robot 47, 753–769 (2023). https://doi.org/10.1007/s10514-023-10108-6

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