Motion Planning in Urban Environments

被引:222
作者
Ferguson, Dave [1 ]
Howard, Thomas M. [2 ]
Likhachev, Maxim [3 ]
机构
[1] Intel Res Pittsburgh, Pittsburgh, PA 15213 USA
[2] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[3] Univ Penn, Philadelphia, PA 19104 USA
关键词
D O I
10.1002/rob.20265
中图分类号
TP24 [机器人技术];
学科分类号
080202 [机械电子工程]; 1405 [智能科学与技术];
摘要
We present the motion planning framework for in autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultrareliability, high-speed operation, complex intervehicle 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 the first part of this article, we describe the underlying trajectory generator and the on-road planning component of this system. We then describe the unstructured planning component of this system used for navigating through parking lots and recovering from anomalous on-road scenarios. Throughout, we provide examples and results from "Boss," an autonomous sport utility vehicle that has driven itself over 3,000 km and competed in, and won, the DARPA Urban Challenge. (c) 2008 Wiley Periodicals, Inc.
引用
收藏
页码:939 / 960
页数:22
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