Journal Article

Improved Model Predictive Control for Dynamical Obstacle Avoidance

Choi, Seonggon, Yoo, Heonjong

2025

Overview

Model Predictive Control (MPC) predicts the vehicle’s motion within a fixed time window, known as the prediction horizon, and calculates potential collision risks with obstacles in advance. It then determines the optimal steering input to guide the vehicle safely around obstacles. For example, when a sudden obstacle appears, sensors detect it, and MPC uses the vehicle’s current speed, position, and heading to predict its driving trajectory over the next few hundred milliseconds to several seconds. If a collision is predicted, MPC computes the optimal steering path among possible avoidance trajectories that are feasible within the vehicle’s dynamics. The vehicle then follows this input to steer away from the obstacle. In the proposed method, MPC is combined with Adaptive Artificial Potential Field (APF). The APF dynamically adjusts the repulsive force based on the distance and relative speed to the obstacle. MPC predicts the optimal driving path and generates control inputs, while the avoidance vector from APF is integrated into MPC’s constraints or cost function. Simulation results demonstrate that the proposed method significantly improves obstacle avoidance response, steering smoothness, and path stability compared to the baseline MPC approach.

• Publisher: MDPI AG
• Subject: Adaptive Artificial Potential Field (APF) / Algorithms / Autonomous vehicles / Cost function / exponential repulsive force / Obstacle avoidance / Optimization / path smoothness / Planning / Potential fields / Predictive control / reactive navigation / Robotics / Smoothness / Steering / Trajectories / trajectory planning / Velocity