Safe Model Predictive Tracking with an Improved APF-RRT* Path Planning for UGVs

In complex road scenarios, control with excellent robustness and safety is important and critical for unmanned ground vehicles (UGVs). This paper focuses on studying safe tracking control for front-wheel steering UGVs. Specifically, this paper investigates trajectory planning based on the improved Artificial Potential Field (APF) and Rapidly-Exploring Random Trees* (RRT*), as well as trajectory tracking using Model Predictive Control (MPC). To address obstacle avoidance in complex environments, this paper designs a trajectory planning algorithm based on an improved APF-RRT*. In addition, this paper proposes a new evaluation criterion for trajectory planning methods, and compares the improved APF-RRT* algorithm with the existing typical algorithms. Then, an MPC controller is designed to track the planned trajectory. The effectiveness of the safe model predictive tracking is verified finally through simulations.