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Fig. 2. Sensor placement on the vehicle: 1 stereo camera, 2 laser scanner,
3 IMU, 4 steering angle encoder, 5 wheel speed encoder, 6 rotor position
encoder, 7 GPS
1) Actuators: TUW Racing had to adapt its vehicle with
actuators that still allow for human operation of the vehicle.
The brake pedal may not be blocked and the steering must
be easily steerable by hand despite the gearboxes and motors
that are to be added.
• Brake System: Due to the already-optimised brake
balance, the brake system is mechanically operated via
the pedal. Details in the mounting allow the driver to
still press the pedal. An alternative way to decelerate is
through reverse operation of the motors, although brake
response is faster and conserves energy.
The brake system must always be able to stop the car
within a maximum of 10 m, even in the face of a
single failure in the system, including power loss or
any mechanical failure.
• Steering: The additional steering motor is mounted to
the existing steering strut at the top of the monocoque.
With an average-size driver in the vehicle, the car
weighs about 240 kg and requires 25 Nm to steer while
the car is not moving. This is the force TUW Racing
designed for, since it would enable testing with a driver.
The competition requires students to design emergency
systems in a detailed Failure Mode and Effects Analysis
(FMEA). For instance, power or mechanical failure to the
brakes or steering must be accounted for with fallback
systems. When emergency braking is initiated by remote
or failure detection in another subsystem, the vehicle must
enter a safe state that simultaneously relies on the actuator’s
operation. For instance, a vehicle steering 60 degrees to the
left while a full brake is initiated should first steer to the
center position to optimise friction on the wheels.
2) Sensors: Sensor placement on the vehicle is shown in
Fig. 2.
• Camera:TUW Racing selected a ZED6 stereo camera
for its visual sensors. It features an opening angle of
90deg and a base line of 120mm, connected via USB3,
which enables generation of depth images at a range of
20m.Thecamera ismountedon the topmostpointof the
roll bar (1). In order to automate the calibration process
and to estimate the extrinsic camera matrix, the team
used visual markers attached to the vehicle’s chassis on
6ZED Stereo Camera: https://www.stereolabs.com/ Fig. 3. Image of left camera with visual markers attached to vehicle to
estimate extrinsic camera matrix
specific locations, as shown in Fig. 3
• Laser Scanner:A laser scanner was placed inside the
front wing, at the lowest point, as it is a planar scanner
and the cones are relatively short. It is tilted downwards,
so that it points at the bottom of a cone from the
maximum distance. This counteracts damping influence,
which could lead to the rising of laser orientation during
accelerations, and the lowering thereof during braking.
With theuseof (2)aHokuyo20LXplanar laser scanner,
the autonomous system is able to detect cones within
the field of view using circle detection and heuristics to
filter non-cone circular obstacles, e.g. wall detection.
• GPS: For accurate absolute position measurement, a
dGPS, provided by a Piksi Multi GNSS module (7),
is used along with two beacons placed outside the race-
track. The beacons allow for more precise positioning
than a common GPS system does.
• IMU:The relative movement of the vehicle is measured
by a motorsport-grade IMU (3), which measures rota-
tion in the yaw axis as well as acceleration in the x and
y directions.
• Odometry: To accurately determine the front wheels’
speed and distance travelled, TUW Racing uses an
inductive wheel spin sensor (5) at each front wheel, and
for the rear wheels a rotor position encoder is used for
the car’s TUW-Racing-developed motors(6).
The steering angle is measured by a rotary position
encoder (4)connected to thesteeringshaft.Allmeasure-
ments acquired from these devices are used within the
software framework which is described in the following
section.
C. Software
In order to integrate and process sensor measurements,
ROS is used as the base framework and nodes were imple-
mented for the following tasks.
• SLAM(SimulationsLocalizationandMapping):The
vehicle’sposemustbeestimatedand the race trackmust
be reconstructed while driving[8].
• Machine vision:The traffic cones, their positions, and
colors, must be detected using multiple cameras and a
laser range sensor.
53
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Titel
- Proceedings of the OAGM&ARW Joint Workshop
- Untertitel
- Vision, Automation and Robotics
- Autoren
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas MĂĽller
- Bernhard Blaschitz
- Svorad Stolc
- Verlag
- Verlag der Technischen Universität Graz
- Ort
- Wien
- Datum
- 2017
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Schlagwörter
- Tagungsband
- Kategorien
- International
- Tagungsbände