An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization

In this article, an automatic vehicle parallel parking algorithm, consisting of path planning, controller design, and state estimation is developed. The planned using clothoid sequences a straight line, which avoids stopping the car to reorient wheels. control inputs, including speed steering angle, are function traveled distance. This method enables park from different initial poses, achieving reduced time ability in one or two maneuvers, smaller than standard places. An evolutionary optimization algorithm used calculate best parameter according defined criteria. proposed technique utilizes Unscented Kalman Filter (UKF) estimate distance, resulting error compared conventional Extended (EKF). research aims introduce optimal improve existing methods terms duration, required space size for maximum continuity. Finally, fidelity improved performance assessed various probable conditions powerful Monte Carlo simulations.