A Model Predictive Control Based Robust Gait Generation with Turning Ability

Pengfei Yang, Zhaohui Yuan, Sheng Dong, Jingchao Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To enhance the anti-disturbance ability of biped robots and improve their full range of motion ability, this paper proposes a gait generation method based on model predictive control with turning ability, which can automatically generate gait trajectories with the given reference speed and turning speed. We integrate footstep positions into the objective function and use the change of center of pressure (CoP) as the optimal input to minimize the objective. The contribution of this paper is considering various feasible optimization constraints to generate gait trajectories online with anti-disturbance ability, adjustable stride, and turning ability. Simulation results show that this method can generate robust gait trajectories online and turn in any direction.

Original languageEnglish
Title of host publication2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages600-605
Number of pages6
ISBN (Electronic)9798350300178
DOIs
StatePublished - 2023
Event8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023 - Sanya, China
Duration: 8 Jul 202310 Jul 2023

Publication series

Name2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

Conference

Conference8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
Country/TerritoryChina
CitySanya
Period8/07/2310/07/23

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