Synchronous Obstacle Avoidance Tracking Control Scheme for Snake Robots With State Constraints and Interference Compensation

This work proposes a synchronous obstacle avoidance tracking control framework with state constraints and interference compensation to achieve autonomous obstacle avoidance and track reference paths for snake robots. In the kinematic, an improved robust line-of-sight (LOS) strategy based on reduced-order extended state observer is proposed. The sideslip angle estimation compensates for the deviation of the direction angle, thereby ensuring the accuracy of direction commands. Meanwhile, considering obstacle avoidance and non-differentiable inflection points of fold paths, a virtual dynamic guidance rule is designed to generate smooth references, which reduces the computational load of an actual robot turning and obstacle avoidance processes. In the dynamic, a barrier Lyapunov-based neural network robust controller is derived to cope with state constraints caused by actuator saturation. The adaptive law of dynamic deviation and external interference compensates for joint torque. Robots' autonomous obstacle avoidance ability and the superiority of the proposed method are demonstrated through experiments.