Optimal Path Following Control for Motion Recovery From Joint Failure of Snake Robots Using Improved Lateral Undulatory Gait

The operating environment of snake robots is typically complex and harsh, often subjecting their actuators to high load conditions that can lead to joint failures. This article presents an optimal robust path following control framework for snake robots operating under joint failures, based on improved lateral undulatory gait. Two joint failure scenarios are considered: free-joint failure (including head-joint free) and free-joint failure combined with an adjacent joint locked at 0$^\circ$. To tackle these scenarios, we develop improved gait patterns that increase the control degrees of freedom and adjust the interjoint phase shift, enhancing the adaptability of the robot. The dynamic model is rederived based on the modified gait, with simplifications made to reduce computational overhead. Model predictive control (MPC) is then applied to generate optimal control inputs while considering multiple constraints. Furthermore, to achieve robust path following control in the presence of unknown and variable friction forces and external disturbances, adaptive interaction is employed for real-time tuning of the MPC weights. Extensive simulation and experimental results, across various failure scenarios, validate the effectiveness of the proposed control architecture in enabling motion recovery and robust path following under joint failures.