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facing camera, the autonomously detected peo-
ple or the local and global cost map for the au-
tonomous drive. In addition, the GUI can also
be used for tele-operation of the robot arm in
case theconnection to thecontrolpanel fails, so
that a redundant tele-operation system is avail-
able (6).
3. Controlpanel (Figure3 (3))
The control panel for teleoperation is used to
tele-operator Robbie. Here the steering of the
base platform as well as the robot-arm is han-
dled. Further the autonomous ”come-home”
functionalitycanbestartedandstopped.
2.4. Implementation -Software
The Robot Operating System (ROS) [14] is used
as high-level API to evaluate sensor data and control
actuators. To improve the tele-operation process, a
GUI plug-in for rviz [19] has been developed, which
displaysall sensordataandenables tele-operationof
the robot arm(seefigure3).
For 2D and 3D mapping the open source frame-
worksCartographer [15]andOctomap[16]were im-
plemented. The main advantage of the Cartogra-
pher algorithm is the ability to detect and calculate
online loop closure with graph optimization, which
minimizestheabsolutetranslationandrotationerrors
during map generation [15]. Octomap, on the other
hand, uses a probabilistic estimation of the occu-
pancyof3Dspaceandrepresents theenvironment in
octaves,whichconsistsofoccupiedvoxels [16]. Fig-
ure 4 visualize a generated 2D or 3D map with these
SLAMapproachesusingtheLIDARsensors listedin
section 2.2, recordedduring theEnRicH2019 trial.
To overcome the need for manual victim recogni-
tion and mappinga ROS package based on Octomap
and YOLO-ROS [2], a convolutional neural network
(CNN) for object recognition in RGB images, was
developed. ByutilizingraycastingandtheBounding
Box the xand ycoordinatesof thevictim,with the6
DOF transformation between the map frame and the
RGB camera frame, is calculated. Detected victims
arevisualizedon the2Dand3Dmapof theGUI.By
utilizing 2 different sensors to calculate the position
of the victim thermal imaging cameras can also be
used forvictim detection.
Further a ROS package for automatic drive
has been developed which uses the move-base-flex
framework [26], a flexible navigation framework,
and SMACH [3], a task level architecture for build- ing complex robot behaviors. Currently two path
planners are implemented. The Timed Elastic Band
(TEB) planner, a planner that takes travel time into
account. Movement is not calculated by the simu-
lated forces within the virtual elastic band, but by
optimizing the travel time and the path [28]. The
TEBplannercalculatesseveral feasiblepathsandse-
lects the fastest one. If the planner does not reach
the target, recovery behaviours are called up. After
each behaviour call the planner tries to reach the tar-
get again. If the target is still not reachable, the next
recovery behaviour is called. After all three imple-
mentedbehaviorsareexecuted, the localscheduler is
switched to the Dynamic Window Approach (DWA)
algorithm. The DWA breaks up the global plan into
smaller windows, whereby only the current and the
next window are used to calculate the path [11]. The
speeds within the next window are calculated using
the current robot speed, the possible acceleration of
the robot and objects to be avoided. The target tol-
erance of the DWA planner is increased to ensure
that the target position can be reached. If the plan-
ner cannot reach the specified target, the recovery
behaviours are called up as with the TEB planner.
If the system still cannot reach the target after call-
ing all recovery behaviors, the execution of the local
andglobalplanner is terminated. TheSMACHscript
then returns an error and waits for a new target. The
first implemented recovery behavior clears the cost
maps, the second moves the robot back for 0.3m or
5 seconds and the third turns the robot 360◦ on the
spot.
During the exploration the radioactivity is contin-
uously measured with the radiometer. After explo-
ration the nuclear radiation of the area around the
driven path is estimated using a Gaussian process.
The amount of radiation is then visualized and piled
over the 2D map together with a legend, further the
radioactivity is alsovisualized in the3Dmap.
Section 3 now introduces the results achieved using
this systemconceptduring the2019EnRicHtrial.
3.ResultsandDiscussion
Table 2 evaluates the System Readiness Level
(SRL) of the mobile Robot of the UAS Technikum
Vienna based on the survey evaluated in [7, 24]. Us-
ingthesurveyresultsof[7]and[24]Robbie’sSRLis
defined as 9/10, since only the IP65 resistance could
notbe fulfilled.
Figure 4 visualizes the environment mapped dur-
24
Joint Austrian Computer Vision and Robotics Workshop 2020
- Titel
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Herausgeber
- Graz University of Technology
- Ort
- Graz
- Datum
- 2020
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Kategorien
- Informatik
- Technik