Robust humanoid robot walking using hybrid flywheel evolutionary neural control

Humanoid robot represents a highly uncertain dynamic plant. Nowadays, humanoid push recovery in stepping represents a complicated and challenging task. This paper proposes a new control approach in order to improve the biped push recovery using flywheel-based auto-balance. The core of the proposed approach relies on the original implementation of an additional control scheme that equalizes the unexpected force acting on the humanoid during robust stepping. Our novel control approach includes an evolutionary neural (IDE-NN: Improved Differential Evolution-Neural Networks) controller for robust biped walking and an additional optimal Proportional Integral (PI) used to regulate the flywheel integrated to the humanoid upper body. The proposed solution helps the humanoid stepping robustly to follow the trajectory required and further empirically guarantees the small-sized experiment humanoid HUBOT-5 robot stably stepping, even in case an unexpected force acting on HUBOT-5 biped. The comprehensive benchmark tests confirm that our proposed method is initiatively efficient.

Automated summary

This paper proposes a new control approach using flywheel-based auto-balance to improve the biped push recovery of humanoid robots. The approach includes a neural controller for robust biped walking and a proportional integral controller to regulate the flywheel integrated to the humanoid upper body. Experimental results demonstrate that the proposed method enables stable walking of the humanoid robot.