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RC-racecar, its simulationand themotionmodel.
This paper is structured as follows. At first, related research is introduced and the interface as well
as the adapted velocity motion model are described. Based on this knowledge, the robot and its
simulation are annotated and their basic structure is discussed. Additionally, the trajectories of the
two vehicles are compared and the reasons why the trajectories and the motion model deviate from
eachotherareexplained. Finally, further improvements for theadaptedmotionmodel inusewith the
robot and the simulationare introduced.
2. RelatedWork
Autonomousdriving iscurrentlya research topicofbothmajorautomobilemanufacturers likeVolvo,
Ford or Nissan and newcomers to the topic of automobiles like Google [6]. At the DARPA urban
challenge, universities like the Massachusetts Institute of Technology and the Stanford University
present their research accomplishments [3]. An example for research on autonomous vehicles with
ackermann drive using ROS is Marvin, the autonomous car by the University of Texas at Austin.
Members of the Marvin-Team ported the software of the autonomous car to ROS and shared it this
way. The ackermann group represents a community developing open source ROS packages for such
vehicles. For the project discussed within this paper, ROS is used because is allows to combine and
enhance such packages for navigation and odometry. Twist messages3 and ackermann messages4 are
used tocontrol the robot, andodometrymessages5 areusedfor tracking. ThestructureofROSallows
tocombineall thesedifferentmessagescontained indifferentpackages into one interface.
3. Interface
Theinterface iscreated toensurecompatibilitybetweentherobotsof thefleetandthesimulation. For
that reason, the interfaceconverts twistmessages toackermannmessages. It alsoconverts theseROS
messages into serial commands and vice versa for those vehicles unable to run ROS. The converting
structureof the interface is shown inFigure1.
Twist messages are commonly used as motion commands because the six parameters they hold pro-
videenoughinformationtodefinemotionsinathreedimensionalspace. Inthefurther, twistmessages
holdingonlyone linearvelocityandoneangularvelocityareassumed, since theyprovideenough in-
formationformotions ina twodimensionalspace. Ackermannmessagescontainavelocity,asteering
angleand information about theacceleration and the jerk. The last twoarenotused for thisproject.
Thevelocityof theackermannmessagesequals the linearvelocityof the twistmessages. Thesteering
angleof theackermannmessagescanbecalculatedwith theknowledgeof thecarsgeometry. Acurve
radius of an imaginary third front wheel is calculated by dividing the rotational velocity of the twist
message by the its linear velocity. With this radius, the knowledge of the wheelbase and the usage of
trigonometric functions, the steering angleϕ can be calculated. Based on the motion commands the
car and its simulation receive, they return odometry messages containing the estimated pose and its
uncertainty. This information is calculatedbasedon themotion model.
3Twist Messages: http://wiki.ros.org/geometry msgs (25.04.2016)
4AckermannMessages: http://wiki.ros.org/ackermann msgs (25.04.2016)
5OdometryMessages: http://wiki.ros.org/nav msgs (25.04.2016)
194
Proceedings
OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
- Titel
- Proceedings
- Untertitel
- OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
- Autoren
- Peter M. Roth
- Kurt Niel
- Verlag
- Verlag der Technischen Universität Graz
- Ort
- Wels
- Datum
- 2017
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-527-0
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 248
- Schlagwörter
- Tagungsband
- Kategorien
- International
- Tagungsbände