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. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract-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 onroad planning component of this system. We provide examples and results from "Boss", an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the Urban Challenge.

Abstract-With the continued improvements in portable computing power and sensing systems it is becoming more common for groups of robots to cooperate to achieve a goal. When the robots are operating in an initially unknown environment, the most natural form of cooperation is multi-robot exploration. For many years frontier based approaches have been commonly used to assign target points for each of the robots in the group based on expected information gain and distance to travel. In this paper we present an expansion to these approaches allowing for the incorporation of multiple objective utility functions that allow adjustment of the exploration priorities both for the individual robots and the group as a whole. In addition, we discuss real world results of our algorithm including our first place finish at the Old Ram Shed Challenge and second place at the MAGIC2010 main competition.

No abstract