TY - GEN
T1 - Argos
T2 - Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques
AU - Abiola, Solomon O.
AU - Baldassano, Christopher A.
AU - Franken, Gordon H.
AU - Harris, Richard J.
AU - Hendrick, Barbara A.
AU - Mayer, Jonathan R.
AU - Partridge, Brenton A.
AU - Starr, Eric W.
AU - Tait, Alexander N.
AU - Yu, Derrick D.
AU - Zhu, Tony H.
PY - 2010/5/7
Y1 - 2010/5/7
N2 - In this paper, we present Argos, an autonomous ground robot built for the 2009 Intelligent Ground Vehicle Competition (IGVC). Discussed are the significant improvements over its predecessor from the 2008 IGVC, Kratos. We continue to use stereo vision techniques to generate a cost map of the environment around the robot. Lane detection is improved through the use of color filters that are robust to changing lighting conditions. The addition of a single-axis gyroscope to the sensor suite allows accurate measurement of the robot's yaw rate and compensates for wheel slip, vastly improving state estimation. The combination of the D* Lite algorithm, which avoids unnecessary re-planning, and the Field D* algorithm, which allows us to plan much smoother paths, results in an algorithm that produces higher quality paths in the same amount of time as methods utilizing A*. The successful implementation of a crosstrack error navigation law allows the robot to follow planned paths without cutting corners, reducing the chance of collision with obstacles. A redesigned chassis with a smaller footprint and a bi-level design, combined with a more powerful drivetrain, makes Argos much more agile and maneuverable compared to its predecessor. At the 2009 IGVC, Argos placed first in the Navigation Challenge.
AB - In this paper, we present Argos, an autonomous ground robot built for the 2009 Intelligent Ground Vehicle Competition (IGVC). Discussed are the significant improvements over its predecessor from the 2008 IGVC, Kratos. We continue to use stereo vision techniques to generate a cost map of the environment around the robot. Lane detection is improved through the use of color filters that are robust to changing lighting conditions. The addition of a single-axis gyroscope to the sensor suite allows accurate measurement of the robot's yaw rate and compensates for wheel slip, vastly improving state estimation. The combination of the D* Lite algorithm, which avoids unnecessary re-planning, and the Field D* algorithm, which allows us to plan much smoother paths, results in an algorithm that produces higher quality paths in the same amount of time as methods utilizing A*. The successful implementation of a crosstrack error navigation law allows the robot to follow planned paths without cutting corners, reducing the chance of collision with obstacles. A redesigned chassis with a smaller footprint and a bi-level design, combined with a more powerful drivetrain, makes Argos much more agile and maneuverable compared to its predecessor. At the 2009 IGVC, Argos placed first in the Navigation Challenge.
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U2 - 10.1117/12.839125
DO - 10.1117/12.839125
M3 - Conference contribution
AN - SCOPUS:77951761383
SN - 9780819479327
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXVII
Y2 - 18 January 2010 through 19 January 2010
ER -