Trajectory tracking control of trackless rubber-tired vehicles in underground mines under multi-sensor time delays

The underground mine unmanned driving system typically uses SLAM algorithms based on vision and Light Detection and Ranging (LiDAR) sensors for scene reconstruction and vehicle positioning. However, in underground environments, the dust density is high, and lighting conditions are poor, making complex perception-based positioning algorithms prone to significant computational delays. Additionally, most underground vehicles use modified solutions, which inevitably introduce actuator delays. These factors can have a cumulative effect, making it necessary to study trajectory tracking control for underground mine trackless rubber-tired vehicles under multi-sensor delays. A lateral control dynamics model for the vehicle was developed, and two modeling methods were proposed to analyze the impact of delays on the stability of vehicle dynamics control: one was the state augmented modeling method, and the other was the Lyapunov functional-based method. The CarSim and Simulink platforms were used to construct a simulation testing environment for underground vehicle trajectory tracking control. Simulation results indicated that when focusing on the average tracking distance error relative to the reference trajectory, it was necessary to limit the delay of the perception algorithm and the standard deviation of positioning. When there was a maximum tracking distance error constraint related to safety, the delay requirements of the perception algorithm should be prioritized.