Squat Motion of a Humanoid Robot Using Three-Particle Model Predictive Control and Whole-Body Control - Hongxiang Chen; Xiuli Zhang; Mingguo Zhao (Sensors)
Squatting is a fundamental and crucial movement, often employed as a basic test during robot commissioning, and it plays a significant role in some service industries and in cases when robots perform high-dynamic movements like jumping. Therefore, achieving continuous and precise squatting actions is of great importance for the future development of humanoid robots. In this paper, we apply three-particle model predictive control (TP-MPC) combined with weight-based whole-body control (WBC) to a humanoid robot. In this approach, the arms, legs, and torso are simplified into three particles. TP-MPC is utilized to optimize the rough planning’s reference trajectory, while WBC is employed to follow the optimized trajectory. The algorithm is tested through simulations of a humanoid robot performing continuous squatting motions. It demonstrates the ability to achieve more accurate trajectory tracking compared to using WBC alone and also optimizes the issue of excessive knee torque spikes that occur with WBC alone during squatting. Moreover, the algorithm is less computationally intensive, and it is capable of operating at a frequency of 100 Hz.