Authors: Bharat Singh; Suchit Patel; Ankit Vijayvargia; Rajesh Kumar
Addresses: Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur-302017, Rajasthan, India ' Centre for Converging Technologies, University of Rajasthan, Jaipur-302004, Rajasthan, India ' Department of Electrical Engineering, Malaviya National Institute of Technology, Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur-302017, Rajasthan, India ' Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur-302017, Rajasthan, India
Abstract: Gait generation for biped robot is a tedious task for locomotion in an uncertain environment. In this research paper, the authors have developed the deep learning approach for modelling the human locomotion kinematics dataset on an uneven surface which can be further used as a reference trajectory for the biped robot. However, choosing the right activation function for deep learning is a very challenging task. This research work has proposed the universal activation function for the kinematic modelling which is adaptive in sense of application. Twenty-five different activation functions from the literature are compared with the presented activation function in term of mean and maximum model prediction error along the gait trajectory. It shows that the universal activation function-based gait model outperforms others by large margins. Additionally, the parameter sensitivity of the presented activation function is discussed in detail. Moreover, the complexity analysis and running time are also investigated. Furthermore, two cases of 5% and 10% variation in the input are analysed to evaluate the prediction ability of the developed gait model with a 95% prediction interval.
Keywords: gait model; activation function; prediction interval; data-driven; biped robot.
International Journal of Modelling, Identification and Control, 2023 Vol.42 No.4, pp.312 - 322
Received: 26 Mar 2022
Received in revised form: 20 May 2022
Accepted: 07 Jun 2022
Published online: 01 Jun 2023 *