Trajectory Planning For Car-like Robots Through Curve Parametrization And Genetic Algorithm Optimization With Applications To Autonomous Parking

Abstract

Parallel parking a car is a difficult task and may be frustrating and stressful for the driver, while commonly causes traffic jam. One way to mitigate such negative effects is to provide vehicles with self-driving capabilities. As a cornerstone of a mobile robot's ability to move autonomously stands trajectory planning, which despite many works in the literature, is still considered an open problem especially with regards to nonholonomic vehicles such as car-like robots. Based on this scenario, this work presents a trajectory planning algorithm to parallel park car-like mobile robots based on polynomial parametrization and genetic algorithm optimization. The aim is to define a law of motion to lead the vehicle from an initial pose near a parking space to a final pose within the latter in a smooth way, with no interruption and avoiding any obstacles in the way. Simulation results validate the feasibility of the proposed algorithm which lays the foundation to broader studies.