Spline-Based Motion Planning for Autonomous Guided Vehicles in a Dynamic Environment

Abstract

Autonomous vehicles require a collision-free motion trajectory at every time instant. This brief presents an optimization-based method to calculate such trajectories for autonomous vehicles operating in an uncertain environment with moving obstacles. The proposed approach applies to linear system models, as well as to a particular class of nonlinear models, including industrially relevant vehicles, such as autonomous guided vehicles with front wheel, differential wheel, and rear-wheel steering. The method computes smooth motion trajectories, satisfying the vehicle’s kinematics, by using a spline parameterization. Furthermore, it exploits spline properties to keep the resulting nonlinear optimization problem small scale and to guarantee constraint satisfaction, without the need for time gridding. The resulting problem is solved sufficiently fast for online motion planning, dealing with uncertainties and changes in the environment. This brief demonstrates the potential of the method with extensive numerical simulations. In addition, it presents an experimental validation in which a KUKA youBot , steered as a holonomic or differential drive vehicle, drives through an environment with moving obstacles. To facilitate the further development and the numerical and experimental validation of the presented method, it is embodied in a user-friendly open-source software toolbox.