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Team Cornell's Skynet: Robust perception and planning in an urban environment
Author(s) -
Miller Isaac,
Campbell Mark,
Huttenlocher Dan,
Kline FrankRobert,
Nathan Aaron,
Lupashin Sergei,
Catlin Jason,
Schimpf Brian,
Moran Pete,
Zych Noah,
Garcia Ephrahim,
Kurdziel Mike,
Fujishima Hikaru
Publication year - 2008
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20253
Subject(s) - planner , position (finance) , event (particle physics) , obstacle , computer science , estimator , perception , control engineering , artificial intelligence , engineering , geography , mathematics , statistics , physics , archaeology , finance , quantum mechanics , neuroscience , economics , biology
Team Cornell's Skynet is an autonomous Chevrolet Tahoe built to compete in the 2007 DARPA Urban Challenge. Skynet consists of many unique subsystems, including actuation and power distribution designed in‐house, a tightly coupled attitude and position estimator, a novel obstacle detection and tracking system, a system for augmenting position estimates with vision‐based detection algorithms, a path planner based on physical vehicle constraints and a nonlinear optimization routine, and a state‐based reasoning agent for obeying traffic laws. This paper describes these subsystems in detail before discussing the system's overall performance in the National Qualifying Event and the Urban Challenge. Logged data recorded at the National Qualifying Event and the Urban Challenge are presented and used to analyze the system's performance. © 2008 Wiley Periodicals, Inc.
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