Discrete time model predictive control for humanoid walking with step adjustment
<|reference_start|>Discrete time model predictive control for humanoid walking with step adjustment: This paper presents a Discrete-Time Model Predictive Controller (MPC) for humanoid walking with online footstep adjustment. The proposed controller utilizes a hierarchical control approach. The high-level controller uses a low-dimensional Linear Inverted Pendulum Model (LIPM) to determine desired foot placement and Center of Mass (CoM) motion, to prevent falls while maintaining the desired velocity. A Task Space Controller (TSC) then tracks the desired motion obtained from the high-level controller, exploiting the whole-body dynamics of the humanoid. Our approach differs from existing MPC methods for walking pattern generation by not relying on a predefined foot-plan or a reference center of pressure (CoP) trajectory. The overall approach is tested in simulation on a torque-controlled Humanoid Robot. Results show that proposed control approach generates stable walking and prevents fall against push disturbances.<|reference_end|>
@article{joshi2024discrete,
    title={Discrete time model predictive control for humanoid walking with step
  adjustment},
    author={Vishnu Joshi, Suraj Kumar, Nithin V and Shishir Kolathaya},
    journal={arXiv preprint arXiv:2410.06790},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06790},
    primaryClass={cs.RO}
}