Active Lateral Obstacle Avoidance Planning Based on Event-Triggered Model Predictive Control

This paper investigates the problem of obstacle avoidance planning and control for unmanned ground vehicles (UGV) based on event-triggered model predictive control (EMPC). A unified framework comprising planning and control is established and the control strategy is designed to realize obstacle avoidance planning, which enables the vehicle to automatically calculate obstacle avoidance path in real-time when detecting obstacles. In addition, in order to reduce the computational complexity of model predictive control, an event-triggered mechanism is introduced, which can effectively reduce the computational burden of model predictive control. The stability of the system and the feasibility of the solution are demonstrated by rigorous derivations. The reference trajectory is generated using a point cloud dataset that matches the real map, and the effectiveness of the algorithm is verified by simulation of obstacle avoidance and trajectory planning.