Mechanism-Data Hybrid Driven Control for Snake Robots Subject to Nonaffine Dynamics and Practical Issues

Navigating complex environments and completing tasks with snake robots demand precise path following and velocity tracking. However, current solutions are hampered by challenges such as the expected model mismatch, nonaffine dynamics, safe operation and external disturbances. Addressing these issues is critical for enhancing the versatility and effectiveness of snake robots in real-world applications. This article proposes an efficient mechanism-data hybrid driven robust control architecture for snake robots, addressing critical challenges in path following with velocity tracking. Through analyzing the interplay between gait parameters and state variables, we found that the nonaffine term can be eliminated by decoupling and hierarchical control. Then, a hybrid driven model is developed by augmenting a mechanism model with both a Gaussian process, which learns the expected model mismatch from offline data, and an additive disturbance model for efficient online estimation of residual disturbances via extended state observers. Moreover, a hierarchical control architecture is designed by first formulating a path following controller using model predictive control and then designing a velocity tracking controller based on feedback linearization. The proposed controller is also computationally efficient and takes constraints into account. Extensive experimental scenarios and comparisons validate the effectiveness of the proposed control architecture and also show that the control update interval (0.02 s) is ten times faster than that in previous work.