A Rapid Iterative Trajectory Planning Method for Automated Parking through Differential Flatness

TL;DR

This paper introduces a rapid iterative trajectory planning method for automated parking that uses differential flatness and terminal smoothing constraints to ensure collision-free, feasible, and efficient paths, validated through simulations and real-world tests.

Contribution

It presents a novel PVD-based iterative planning approach that balances speed and precision, incorporating vehicle kinematics and curvature continuity for improved control feasibility.

Findings

Superior time efficiency in simulation compared to existing methods

Reduced tracking errors in simulation results

Successful real-world implementation on a ROS-based vehicle

Abstract

As autonomous driving continues to advance, automated parking is becoming increasingly essential. However, significant challenges arise when implementing path velocity decomposition (PVD) trajectory planning for automated parking. The primary challenge is ensuring rapid and precise collision-free trajectory planning, which is often in conflict. The secondary challenge involves maintaining sufficient control feasibility of the planned trajectory, particularly at gear shifting points (GSP). This paper proposes a PVD-based rapid iterative trajectory planning (RITP) method to solve the above challenges. The proposed method effectively balances the necessity for time efficiency and precise collision avoidance through a novel collision avoidance framework. Moreover, it enhances the overall control feasibility of the planned trajectory by incorporating the vehicle kinematics model and including terminal smoothing constraints (TSC) at GSP during path planning. Specifically, the proposed method leverages differential flatness to ensure the planned path adheres to the vehicle kinematic model. Additionally, it utilizes TSC to maintain curvature continuity at GSP, thereby enhancing the control feasibility of the overall trajectory. The simulation results demonstrate superior time efficiency and tracking errors compared to model-integrated and other iteration-based trajectory planning methods. In the real-world experiment, the proposed method was implemented and validated on a ROS-based vehicle, demonstrating the applicability of the RITP method for real vehicles.