Date of this Version
We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultra-reliability, high-speed operation, complex inter-vehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for computing dynamically-feasible actions with two higher-level planners for generating long range plans in both on-road and unstructured areas of the environment. In this Part I of a two-part paper, we describe the underlying trajectory generator and the on-road planning component of this system. We provide examples and results from ldquoBossrdquo, an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the Urban Challenge.
mobile robots, navigation, path planning, road vehicles, robot dynamics, vehicle dynamics, autonomous vehicle navigation, complex intervehicle interaction, constrained maneuvers, dynamically feasible action, high-speed operation, large unstructured lots, model predictive trajectory generation, motion planning, on-road planning component, trajectory generator, ultrareliability, urban environment
Ferguson, Dave; Howard, Thomas M.; and Likhachev, Maxim, "Motion Planning in Urban Environments: Part I" (2008). Lab Papers (GRASP). Paper 30.
Date Posted: 01 October 2009