A Fuzzy Pure Pursuit for Autonomous UGVs Based on Model Predictive Control and Whole-Body Motion Control

In this paper, we propose an adaptive fuzzy pure pursuit trajectory tracking algorithm for autonomous unmanned ground vehicles (UGVs), addressing the challenges of accurate and stable navigation in complex environments. Traditional pure pursuit methods with fixed look-ahead distances struggle to maintain precision in dynamic and uneven terrains. Our approach uniquely integrates a fuzzy control algorithm that allows for real-time adjustments of the look-ahead distance based on environmental feedback, thereby enhancing tracking accuracy and smoothness. Additionally, we combine this with model predictive control (MPC) and whole-body motion control (WBC), where MPC forecasts future states and optimally adjusts control actions, while WBC ensures coordinated motion of the UGV, maintaining balance and stability, especially in rough terrains. This integration not only improves responsiveness to changing conditions but also enables dynamic balance adjustments during movement. The proposed algorithm was validated through simulations in Gazebo and real-world experiments on physical platforms. In real-world tests, our algorithm reduced the average trajectory tracking error by 45% and the standard deviation by nearly 50%, significantly improving stability and accuracy compared to traditional methods.