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Neural network temporal quantized lagrange dynamics with cycloidal trajectory for a toe-foot bipedal robot to climb stairs

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Abstract

A novel technique for joint angles trajectory tracking control with energy optimization is proposed for a biped robot with toe foot. For the task of climbing stairs by a 9-link biped model, an adaptive cycloid trajectory for the swing phase is planned as a function of the staircase rise/run ratio. We consider Zero Moment Point criteria for satisfying stability constraints. The paper is primarily divided into three sections: 1) Planning stable cycloid trajectory for the initial step and subsequent steps for climbing upstairs. We incorporate inverse kinematics using an unsupervised artificial neural network with a knot shifting procedure for jerk minimization. 2) Developing dynamics for toe-foot biped model using Lagrange formulation along with contact modeling using the spring-damper system. We propose Neural Network Temporal Quantized Lagrange Dynamics, which couples inverse kinematics neural network with dynamics. 3) Using Ant Colony Optimization to tune Proportional-Derivative controller and torso angle in order to minimize joint trajectory errors and total energy consumed. Three cases with variable staircase dimensions have been taken, and a comparison is made to validate the effectiveness of the proposed work. Generated patterns have been simulated in Ⓒ Matlab and MuJoCo.

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Authors and Affiliations

  1. Computer Science and Engineering Department, IIT Roorkee, Haridwar, Uttarakhnand, India

    Gaurav Bhardwaj & R. Balasubramanian

  2. Mechanical and Industrial Engineering Department, IIT Roorkee, Haridwar, Uttarakhnand, India

    Utkarsh A. Mishra

  3. Mathematics Department, IIT Roorkee, Haridwar, Uttarakhnand, India

    N. Sukavanam

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  1. Gaurav Bhardwaj
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  4. R. Balasubramanian
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Bhardwaj, G., Mishra, U.A., Sukavanam, N. et al. Neural network temporal quantized lagrange dynamics with cycloidal trajectory for a toe-foot bipedal robot to climb stairs. Appl Intell 53, 10995–11018 (2023). https://doi.org/10.1007/s10489-022-03921-6

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