Collaborative Line-of-Sight Guidance-Based Robust Formation Control for a Multi-Snake Robot

For the collaborative formation control problem of a multi-snake robot with uncertain sideslip and lumped unknowns, this study proposed collaborative line-of-sight (LOS) guidance and consensus-based path variable, which enables snake robots to track multiple parametric paths in a fixed formation. Meanwhile, the improved finite-time sideslip observer effectively compensates for the kinematic deviation, thereby improving the accuracy of the multi-snake robot&#x2019;s path tracking. A robust adaptive controller based on an integral sliding mode manifold is developed to eliminate external disturbances. The model uncertainty and time-varying disturbances are comprehensively involved and incorporated as lumped unknowns in dynamics. The prediction-based RBF neural network estimators are derived to approximate unknowns and compensate controllers. The Lyapunov theory is adopted to prove the stability of formation errors for snake robots in the closed-loop system. Simulation and experiment results show the practicability and superiority of this work. <italic>Note to Practitioners</italic>&#x2014;This paper is mainly motivated to study a cooperative formation control framework suitable for snake robots&#x2019; dynamics, especially for the application of resisting model uncertainty and external interference. In practice, the collaborative line of sight guidance law is employed to generate and adjust the reference direction and speed of robots in real time, and then the method is applied to the joint torque controller to guide robots to track the desired parameterized path. Meanwhile, the improved finite time sideslip observer corrects the deviation of the guidance law, ensuring the observation accuracy while reducing the computational time. During the control process, for highly coupled and complex snake robot models, solving external disturbances and model uncertainties remains a challenge. A neural network based state estimator is used to handle these unknowns to pre-compensate for underrotation/overrotation of the joint steering gear when disturbed. Simulation and experimental results validate that the proposed scheme can enable multiple snake robots to synergistically perform tasks, expressing high potential in unknown environments. In the future, we plan to design a three-dimensional spiral gait instead of the two-dimensional one used in this study. With this change, we will further improve the usability of snake robots in real-world scenarios.