Parallel Parking Path Planning and Trajectory Tracking in Narrow Environments for Autonomous Unmanned Vehicles

To address the challenge of parallel parking in narrow environments, a multi-segment path planning method is proposed for autonomous unmanned vehicles (AUVs), which integrates circular arc and dual clothoid curves, ensuring continuous path curvature and minimal longitudinal space occupancy. First, considering the narrow environmental constraints and the parameters of AUV, the planning for the preparatory phase path and the parking-in phase path are completed, respectively. The role of the preparatory phase path is to guide the AUV to the target point, and the parking-in phase path can guide the AUV into the target parking space. Then, by using the particle swarm optimization (PSO) algorithm with a penalty function framework, a multi-constraint nonlinear programming path function model is established to seek out the parking path. Subsequently, the pure pursuit (PP) algorithm is employed to control the movement of AUV along the planned path. Finally, the simulation result and real-vehicle experiment are conducted, which show that the parking path planning method proposed in this article enables AUV to safely complete parking operations in the narrow environment.