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Analysis of MPC and Whole-Body Control Locomotion Methods for the Qinglong Humanoid Robot
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Published by Guyuehome, this content uses the Qinglong configuration as an example to derive humanoid robot locomotion control methods, covering the construction of the center-of-mass model, zero moment point (ZMP) constraints, receding-horizon quadratic programming, and task-priority force distribution, and validates locomotion performance in real physics simulation across walking, speed variation, steps, jumping, and disturbance-rejection scenarios.
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- Humanoid Robot Motion Control Based on Model Predictive Control and Whole-Body Control: A Case Study of the Qinglong Configuration
Using the Qinglong configuration as a case study, this work derives the construction methods for the center-of-mass model, zero-moment point constraints, receding-horizon quadratic programming, and task-priority force distribution in humanoid robot motion control. The method was validated in real physics simulation for locomotion performance in walking, speed variation, stair climbing, jumping, and disturbance-rejection scenarios.
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