1. **Introduction**

    1. Purpose of the document 

  
  

  

  

The purpose of the Data Management Plan (DMP) is to provide an analysis of the
main elements of the data management policy that will be used with regard to
all the datasets that have been generated by the project. A DMP details what
data the project has generated, whether and how it has been exploited and made
accessible for verification and re-use, and how it will be curated and
preserved.

  2. Scope of the document 

  

  

This document (Deliverable D9.5) describes the final version of the AEROARMS
DMP. The first version was described in Deliverable D9.4, which was submitted
in M6. The final AEROARMS DMP includes all the data that were predicted in
D9.4.

  3. Structure of the document 

  

  

The DMP describes datasets and reflects the current point of view of the
consortium about the data that will be produced. The description of each data
includes the following:

  * Dataset name 

  

  * Authors 

  

  * Data contact and responsible 

  

  * Dataset objective 

  

  * Dataset description 

  

  * Data description 

  

  * Dataset sharing 

  

  * Archiving and preservation 

  

  * Dataset size 

  

  * Zenodo link 

  

It has been agreed by the AEROARMS consortium that all the datasets that will
be produced within the project and that are not affected by IPR (clause 8.0 of
the consortium agreement) will be shared between the partners. Moreover, all
the datasets with potential interest for the community and that are not
related to further exploitation activities will be shared with the whole
scientific community after their publication in conference proceedings and/or
international journals.

Besides the introduction and conclusions, this document is structured in 4
Sections, devoted to the datasets of WPs 3, 4, 5, 6 and 8, which have
generated the datasets with the highest potential interest.

  2. **Control of aerial robots with multiple manipulation means**

  

  

This section is devoted to datasets that have been collected during the
activities in WP3. These datasets have been collected in preliminary tests in
the laboratory, indoor settings or in outdoor experiments. These datasets are
grouped depending on the tasks in WP3 they are involved in.

  1. Modelling of aerial multirotors with two arms 

  

  

Several datasets that include data for modeling of aerial multirotors with
arms have been published in AEROARMS. These datasets are the following:

  1. **Dataset “AEROARMS Behavioural coordinated control”** , described in Section 2.4. 

  

  

The provided data have been acquired during the experiment described in
Section 4 of Deliverable D3.5, which was conducted at the flight arena of
CATEC by using the dual arm manipulator developed by USE. The following data
for modelling of the aerial robot with two arms are included in the dataset:

  * Reference and actual position and orientation of the UAV 

  

  * Reference and actual joint positions of the two arms 

  

  * Desired (planned), reference (output of the Inverse Kinematics) and actual position and orientation of the two manipulators end effectors 

  

  * Position and orientation tracking errors of the two end effectors 

  

The full description and details of the dataset are included in Section 2.4.

  2. **Dataset “Visual servoing with actively movable camera”** , described in Section 2.5. 

  

  

The provided data have been acquired during the experiment described in
Section 5.1 of Deliverable D3.5, which was conducted at the flight arena of
CATEC by using the dual arm manipulator developed by USE. The following data
for modelling of the aerial robot with two arms are included in the dataset:

  * Reference and actual position and orientation of the UAV 

  

  * Reference and actual joint positions of the two arms 

  

  * Desired (planned), reference (output of the Inverse Kinematics) and actual position and orientation of the end effector of the left manipulator 

  

  * Position and orientation tracking error of the end effector of the left manipulator The full description and details of the dataset are included in Section 2.5. 

  

  3. **Dataset “Multirotor with two arms: multirotor/arms interaction”** , described in the 

  

  

#  following.

**Dataset name:** Multirotor with two arms: multirotor/arms interaction

**Authors:** A. Suarez, G. Heredia, A. Ollero

**Data contact and responsible:** USE, Guillermo Heredia

**Dataset objective:** Unlike fixed base manipulators, in an aerial
manipulation robot the reaction wrenches caused by the motion of the arms or
the physical interactions raised on flight are supported by the aerial
platform, causing typically undesired oscillations in the attitude or
deviations in the position that may complicate the realization of grasping
tasks or installation operations. Since it is difficult to appreciate this
effect on flight due to the action of the autopilot and the noise generated by
the propellers, the goal of this dataset is to analyze the effect of the
motion of a dual arm manipulator over the attitude of a multirotor platform
supported by wires, emulating hovering conditions. In particular, it is
interesting to evaluate the partial reaction compensation capability of a dual
arm manipulator, generating coordinated symmetric trajectories to cancel the
reactions in two axes (roll and yaw). The data logs also reveal how the
reaction oscillation in the multirotor is higher as the velocity/acceleration
of the arms increases.

**Dataset description:** This dataset is obtained with the dual arm aerial
manipulator (DJI Matrice 600 hexarotor equipped with USE dual arm) hanging
from four cables attached to the multirotor base emulating hovering
conditions. Although the datasets presented here were obtained with a
particular platform and dual arm manipulator, these may result of interest for
a preliminary analysis of the dynamic coupling effect. The orientation data
was obtained with a STM32F3 Discovery board attached to the multirotor base,
providing the measurements from the accelerometer, gyroscope and magnetometer
sensors. The arms are built with the Herkulex DRS-0402 and DRS-0602 servos and
a customized aluminium frame structure. The experiments consist of generating
a sequence of rotations around the shoulder pitch and shoulder yaw joints with
one arm (non-compensated reaction wrenches) and with both arms (partial
reaction compensation), considering different joint speeds. The measurement
given by the gyroscope in the roll-pitch-yaw angles is evaluated to analyze
the amplitude of the reaction wrenches. Note that the rotational motion of the
multirotor is constrained by the cables.

Three experiments are conducted:

  1. Symmetric trajectory with 1 second play time: the left and right arms generate a sequence of rotations around the shoulder pitch and elbow pitch joints which is symmetric with respect to the XZ plane, where the X-axis is the forward axis, the Y axis is parallel to the shoulder pitch rotation angle, and the Z-axis is the vertical axis parallel to the gravity vector. The play time indicates the desired time to reach the reference angular position. This parameter is sent to the servos in the motion commands. 

  

  2. Symmetric trajectory with 0.5 seconds play time: the same trajectory is executed with both arms, with a lower play time so the reaction wrenches in the pitch angle are more evident (higher inertias and centrifugal terms). 

  

  3. Asymmetric motion with 0.5 seconds play time: the same trajectory is executed only by the left arm while the right arm stays in a fixed position, causing a reaction wrench in the three roll, pitch and yaw angles. 

  

  

A video file is also provided in the dataset file, showing the execution of
the three experiments.

**Data description** : Three groups of log files are provided in the
corresponding folders. The left and right arm data files contain the joint
position, velocity and PWM signal provided by the Herkulex servos, whereas the
STM32Board_Data file contains the accelerometer, gyroscope and magnetometer
data. A MATLAB .m script file is provided to load and plot the data,
indicating clearly each field. The three files share the same time stamp.

The content of the dataset is detailed in Table 2.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Left arm data

</td>  
<td>

Text file “Log_LeftArm.txt”:

  * Col 1: time stamp in sec 

  

  * Cols 2-4: TCP position ref in meters 

  

  * Cols 5-7: TCP position in meters 

  

  * Cols 8-10: joint angular position in deg 

  

  * Cols 11-13: joint angular 

  

position velocity in deg/s

  * Cols 14-16: normalized PWM signal applied to the servos in the range [-1, 1] 

  

</td>  
<td>

The “Log_LeftArm.txt” data file contains different data of interest from the
left arm sampled at 50 Hz, including the reference Cartesian position of the
tool center point (TCP, not used), the current TCP position, the joint
position and velocity, and the normalized PWM signal applied by the servos.
These signals are obtained from the internal registers of the Herkulex servos,
applying the forward kinematic model to obtain the TCP position. The sequence
of rotations indicated in the README file within each experiment folder. This
data file can be loaded and plotted easily with MATLAB

</td> </tr>  
<tr>  
<td>

Right arm data

</td>  
<td>

Text file “Log_RightArm.txt” with the same format used for the left arm

</td>  
<td>

The “Log_RightArm.txt” contains the data of interest from the right arm with
the same format that for the left arm

</td> </tr>  
<tr>  
<td>

IMU data

</td>  
<td>

Text file “STM32_Board_DataFIle

.txt”:

  * Col 1: time stamp in sec 

  

  * Col 2: packet ID 

  

  * Cols 3-4: internal time stamp of the board sec-ms 

  

  * Cols 5-7: acceleration in m/s^2 

  

  * Cols 8-10: angular velocity in deg/s 

  

  * Cols 11-13: magnetic field in Gauss 

  

  * Col 14: temperature of the sensor in degrees Celsius x100 

  

  * Col 15-17: roll-pitch-yaw orientation estimated from the 

  

Madgwick algorithm

</td>  
<td>

A STM32F3 Discovery board is used as IMU, logging the data from the
accelerometer, gyroscope and magnetometer at 100 Hz, sent to the main computer
board through a USART interface. The Madgwick algorithm is used to estimate
the orientation in the rollpitch-yaw angles. The effect of the dynamic
coupling can be observed more clearly in the data from the gyroscope

</td> </tr> </table>

**Table 2: Content of the dataset named: “Multirotor with two arms:
multirotor/arms interaction”.**

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, USE will preserve a copy of the
dataset.

**Dataset size** : 20.1 MB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2657640_

**2.2.** Integrated force and position control

**Dataset name:** Integrated force and position control dataset

**Authors:** A. Suarez, G. Heredia, A. Ollero

**Data contact and responsible:** USE, Guillermo Heredia

**Dataset objective:** The goal of this dataset is to evaluate the performance
of a contact force control task carried out on flight by a compliant joint arm
integrated in a hexarotor platform. The pushing force exerted by the arm is
estimated and controlled from the joint deflection of the spring-lever
transmission mechanism introduced between the servo shaft and the output link,
measuring the deflection angle with an encoder. The data obtained from the IMU
allows to analyse the effect of the physical interactions over the multirotor.

**Dataset description:** The dataset corresponds to two experiments carried
out in outdoors with a Cartesian aerial manipulator consisting of a 2-DOF
Cartesian base (XY axes) and a compliant joint arm attached at its base (third
joint). A safety rope was used for safety in the realization of the
experiments. In the experiment, the multirotor approaches to the contact point
with the arm stretched with its link pointing downwards to ensure that the
force is transmitted in the forward direction (X-axis). Then, it exerts a
sequence of two force references (1 N and 1.5 N) while the aerial platform
tries to stay in hover. The data from the experiment include the position of
the Cartesian base, the force reference and the estimation, the joint
deflection signal and the control signals of the PI controller, as well as the
position, velocity, orientation and angular rate of the aerial platform. The
autopilot is based on the Raspberry Pi-NAVIO board, with the PX4 estimator. A
Leica laser tracking system was used to measure the position of the platform.

**Data description** : The data of each experiment is stored in a single plain
text file with the format indicated below. This file can be loaded and plotted
with MATLAB, providing a script for this purpose, indicating clearly all the
fields.

The content of the dataset is shown in Table 3.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Contact force experiment data

</td>  
<td>

File “Log_Contact_Force_ControlDATE-TIME.txt”

  * Col 1: time stamp 

  

  * Col 2-3: Cartesian base XY axes position in mm 

  

</td>  
<td>

The file contains all the data from the contact force control experiment
including the joint position references and feedback, the control signals, and
the pose of the multirotor platform. The position of the

</td> </tr>  
<tr>  
<td>

</td>  
<td>

•

•

•

• • •

•

•

•

•

•

•

</td>  
<td>

Col 4: compliant joint servo position in deg

Cols 5-6: Cartesian base XY axes reference (PWM or position, depending on the
control mode).

Col 7: compliant joint servo position reference in degrees. Col 8: force
reference in N

Col 9: force estimation in N

Col 10: torque estimation in Nm/rad

Col 11: torque reference in Nm/rad

Col 12-13: proportional and integral correction terms of the PI deflection-
force controller in deg

Cols 14-16: multirotor position in m

Cols 17:19: multirotor velocity in m/s

Cols 20:22: multirotor

orientation in deg

Cols 23:25: UAV angular rate in deg/s

</td>  
<td>

Cartesian base is estimated from the rotation angle and number of turns of the
corresponding DC motor that drives the linear guide. The servo position is
provided by the servo itself (Herkulex DRS-0101), measuring the deflection
angle with a magnetic encoder. The force and torque are estimated from the
deflection angle, knowing the stiffness of the springs in the spring-lever
transmission mechanism. The position of the multirotor is measured with a
Leica laser tracker, whereas the velocity, orientation and angular rate are
obtained from the PX4 estimator

</td> </tr> </table>

#  Table 3: Content of the dataset named: “Integrated force and position
control dataset”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, USE will preserve a copy of the
dataset.

**Dataset size** : 630 KB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2641222_

**2.3.** Control for novel fully-actuated aerial platforms

Two datasets have been made available regarding the control of novel fully-
actuated aerial platforms:

  * Dataset "Towards a Flying Assistant Paradigm: the OTHex" contains experimental data relative to the validation of the controller for fully-actuated aerial robots. 

  

  * Dataset "The Tele-MAGMaS: An Aerial-Ground Comanipulator System" contains experimental data regarding the validation of the controller for fully-actuated aerial robots, while cooperatively manipulating a long object together with ground manipulators. 

  

The description of both datasets is in the following:

**Dataset name:** Towards a Flying Assistant Paradigm: the OTHex

**Authors:** N. Staub, D. Bicego, Q. Sablé, V. Arellano-Quintana, S. Mishra
and A. Franchi

**Data contact and responsible:** CNRS, Antonio Franchi

**Dataset objective:** This dataset contains the experimental data relative to
the validation of the controller for fully-actuated (more in general, multi-
directional thrust) aerial robots. The task consists in approaching a metallic
bar which is fixed to the ground with a revolute joint on one side and lies
horizontally, then grasping it from the free side and lifting it vertically.
This task has been chosen with the goal of showing the capability of the
aerial robot to act as a flying assistant, aiding human operators and/or
ground manipulators to move long bars for assembly and maintenance tasks.

**Dataset description:** The name of the dataset has been changed with respect
to what mentioned in D9.4 to better link the dataset to the corresponding
paper. Thus, we preferred using the title of the paper, i.e., “Towards a
Flying Assistant Paradigm: the OTHex”. This dataset contains the data related
to the experiment used to validate the control of the OTHex, a multi-
directional thrust aerial robot tailored for physical interaction tasks, in
particular for cooperative transportation and manipulation of long beams
together with human operators and/or ground manipulators. In this experiment,
the pose control for the robot has been integrated with an admittance filter
control, which modifies the reference trajectory to the position control based
on the information of the external wrench, computed by a model-based wrench
estimator. This has been done with the goal of preserving stability during the
interaction task.

More in details, the following quantities have been collected:

  * Aerial robot desired and actual pose (position plus orientation) 

  

  * Aerial robot estimated angle of the passive joint 

  

  * Aerial robot desired and measured angle of the bar w.r.t. the ground plane 

  

  * Aerial robot desired value for the exerted body wrench (force and torque) 

  

  * Aerial robot estimated body external wrench (force and torque) 

  

  * Aerial robot desired and estimated rotor spinning velocities 

  

**Data description** : The time history of the above-described variables is
provided in the _mat_ format for reading with MATLAB. Additionally, one MATLAB
script is included for plotting the main variables.

The content of the dataset is shown in Table 4.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Collection of data

</td>  
<td>

othex_record-2017.09.10-

10.29_rpy.mat

</td>  
<td>

Includes all the measurements related to the aerial robot and the bar to be
manipulated

</td> </tr>  
<tr>  
<td>

MATLAB script

</td>  
<td>

check_plotter.m

</td>  
<td>

Prints all the main variables mentioned above

</td> </tr> </table>

#  Table 4: Content of the dataset named: “Towards a Flying Assistant
Paradigm: the OTHex”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, CNRS will preserve a copy of
the dataset.

**Dataset size** : 54.2 MB

**Zenodo link:** _https://doi.org/10.5281/zenodo.2640502_

**Dataset name:** The Tele-MAGMaS: An Aerial-Ground Comanipulator System

**Authors:** N. Staub, M. Mohammadi, D. Bicego, Q. Delamare, H. Yang, D.
Prattichizzo, P.

Robuffo Giordano, D. Lee and A. Franchi

**Data contact and responsible:** CNRS, Antonio Franchi

**Dataset objective:** This dataset contains the experimental data relative to
the validation of the controller for fully-actuated (more in general, multi-
directional thrust) aerial robots, while cooperatively manipulating a long
object together with ground manipulators. The task consists in approaching a
metallic bar which initially lies horizontally on a support structure, then
grasping it with an aerial robot, i.e., the OTHex, and a ground manipulator,
i.e., a KUKA IIWA. Finally, the two robots should cooperatively manipulate the
bar with a master-slave approach, with the robotic arm on the ground acting as
the leader and the aerial robot as the follower. This task has been chosen
with the goal of showing the capability of the aerial robot to act as a flying
assistant, aiding ground manipulators to move long bars for assembly and
maintenance tasks. This task represents an upgrade w.r.t. the one developed in
another work, i.e., “Towards a Flying Assistant Paradigm: the OTHex”. In that
work, the aerial robot was supposed to lift the bar alone, while in this
experiment it has do it together with a ground robot. Therefore, this dataset
represents an additional validation of the controller (of both the pose and
the admittance loops) for multi-directional thrust aerial platforms.

**Dataset description:** The name of the dataset has been changed with respect
to what mentioned in D9.4 to better link the dataset to the corresponding
paper. Thus, we preferred using the title of the paper, i.e., “The Tele-
MAGMaS: An Aerial-Ground Comanipulator System”. This dataset contains the data
related to the experiment used to validate the control of the OTHex, a multi-
directional thrust aerial robot tailored for physical interaction tasks.
Furthermore, this dataset contains the data related to the control of the
ground robot, which is a KUKA IIWA industrial manipulator. In this experiment,
the pose control for the aerial robot has been integrated with an admittance
filter control, which modifies the reference trajectory to the position
control based on the information of the external wrench, computed by a model-
based wrench estimator. This has been done with the goal of preserving
stability during the interaction task.

More in details, the following quantities have been collected:

  * Ground manipulator joint angles and commands 

  

  * Ground manipulator measured joint torque 

  

  * Ground manipulator estimated external joint torque 

  

  * Ground manipulator estimated external Cartesian wrench 

  

  * Aerial robot desired and actual pose (position plus orientation) 

  

  * Aerial robot desired value for the exerted body wrench (force and torque) 

  

  * Aerial robot estimated body external wrench (force and torque) 

  

  * Aerial robot desired and estimated rotor spinning velocities 

  

**Data description** : The time history of the above-described variables is
provided in the _mat_ format for reading with MATLAB. Additionally, one MATLAB
script is included for plotting the main variables.

The content of the dataset is shown in Table 5:  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Collection of data

</td>  
<td>

othex_record-2017.06.23-

22.18.mat

</td>  
<td>

Includes all the measurements related to the aerial robot

</td> </tr>  
<tr>  
<td>

Collection of data

</td>  
<td>

iiwa_record-2017.06.23-22.18.mat

</td>  
<td>

Includes all the measurements related to the ground robot

</td> </tr>  
<tr>  
<td>

MATLAB script

</td>  
<td>

check_plotter.m

</td>  
<td>

Prints all the main variables mentioned above

</td> </tr> </table>

#  Table 5: Content of the dataset named: “The Tele-MAGMaS: An Aerial-Ground
Comanipulator System”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, CNRS will preserve a copy of
the dataset.

**Dataset size** : 72.8 MB

**Zenodo link:** _https://zenodo.org/record/2640461#.XNlGihVYe71_

**2.4.** Behavioural coordinated control

**Dataset name:** AEROARMS Behavioural coordinated control

**Authors:** E. Cataldi, D. Di Vito, G. Antonelli, P.A. Di Lillo, F. Pierri,
F. Caccavale, A. Suarez, F. Real, G. Heredia, A. Ollero

**Data contact and responsible:** CREATE, Gianluca Antonelli

**Dataset objective:** This dataset contains the code and the experimental
data for the development, testing and validation of the devised behavioural
control techniques for dual arm aerial manipulators. The dataset can be used
for performing simulations and tests and it could be of interest for
researchers working on the behavioral and kinematic control of robots, since
it is one of the first applications to the aerial manipulation.

**Dataset description:** This dataset includes a library of elementary
behaviors, namely atomic tasks to be assigned to a dual arm aerial manipulator
in a priority order. On the basis of the theory described in Deliverable D3.3,
both equality and set-based behaviors have been considered. For each
elementary task the Jacobian matrix and the task function are provided. The
code of the kinematic control developed in C++ under the ROS environment and
the simulation model of the aerial dual arm manipulator are provided. The
simulation model has been developed by using the commercial software V-Rep
available with free educational license. More in details, in the provided code
the following equality tasks are included:

  * Position and orientation trajectory tracking of both end-effectors 

  

  * Center of mass, this task is aimed at ensuring that the center of mass of the dualarm system is, as much as possible, aligned with that of the UAV, in such a way to avoid destabilizing the flight and reduce the power consumption As concerns the set-based tasks, the following are included: 

  

  * Joint limits: for each joint, upper and lower limits are set in order to avoid its mechanical limits 

  

  * Virtual wall between the two arms: to avoid collisions between the two arms, a virtual wall is implemented in order to delimit their working spaces 

  

  * Virtual wall between the arms and the vehicle: to avoid collisions between the arms and the vehicle, virtual walls are implemented in order to delimit their working spaces 

  

  * Manipulability, aimed at keeping the manipulators far enough from singular configurations, at which the structure loses mobility 

  

The provided data have been acquired during the experiment described in
Section 4 of Deliverable D3.5 and conducted at the flight arena of CATEC by
using the anthropomorphic compliant and lightweight dual arm developed by USE
integrated in an hexarotor platform. More in details, the following quantities
have been collected:

  * Reference and actual position and orientation of the UAV 

  

  * Reference and actual joint positions of the two arms 

  

  * Desired (planned), reference (output of the Inverse Kinematics) and actual position and orientation of the two manipulators end effectors 

  

  * Position and orientation tracking errors of the two end effectors 

  

  * Time histories of the task variables for each implemented task 

  

**Data description** : The time history of the above described variables is
provided both in the _mat_ format for reading with MATLAB and in the _bag_
format for ROS. Moreover, also the _ASCII_ format is provided in order to be
used by any software. A file with the description of the formats and standards
is included in the dataset in order to facilitate sharing and re-usability.

The ROS bag (Exp_2_1_2018-05-30-16-25-08.bag) contains all measurements and
all task variables captured and logged with their corresponding ROS time
stamp.

The content of the dataset is shown in Table 6.  
  
<table>  
<tr>  
<th>

**Data**

</th>  
<th>

**Format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

UAV pose

</td>  
<td>

ROS topic:

/IK/Quadricopter_base/pose

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Measurement of the pose of the aerial platform in terms of position and
orientation (quaternion), provided by VICON system (position) and the IMU

(orientation)

</td> </tr>  
<tr>  
<td>

UAV reference

pose

</td>  
<td>

ROS topic:

/IK/Quadricopter_base/pose_des

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Reference values of the aerial platform pose computed by the inverse
kinematics

</td> </tr>  
<tr>  
<td>

Joint positions

</td>  
<td>

ROS topic: /joint_states

Format “sensor_msgs/JointState”

</td>  
<td>

Joint position measurements of both the arms

</td> </tr>  
<tr>  
<td>

Reference joint positions

</td>  
<td>

ROS topic /IK/jointCommand

Format “sensor_msgs/JointState”

</td>  
<td>

Reference values of the joint position of both the arms computed by the
inverse kinematics

</td> </tr>  
<tr>  
<td>

End-effector pose of the right arm

</td>  
<td>

ROS topic /IK/Kinematic/EE_1

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Pose of the end-effector of the right arm, computed via the direct kinematics
on the basis of the measured pose of the UAV and the measured joint positions

</td> </tr>  
<tr>  
<td>

Planned end-

effector pose of the right arm

</td>  
<td>

ROS topic /IK/Planner/EE_1

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Planned pose of the end-effector of the right arm, computed via an off-line
planner

</td> </tr>  
<tr>  
<td>

Reference endeffector pose of the right arm

</td>  
<td>

ROS topic /IK/Kinematic/EE_1_des

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Reference pose of the end-effector of the right arm, computed via the direct
kinematics on the basis of the reference pose of the UAV and the reference
joint positions

</td> </tr>  
<tr>  
<td>

End-effector pose of the left arm

</td>  
<td>

ROS topic /IK/Kinematic/EE_2

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Pose of the end-effector of the left arm, computed via the direct kinematics
on the basis of the measured pose of the UAV and the measured joint positions

</td> </tr>  
<tr>  
<td>

Planned end-

effector pose of the left arm

</td>  
<td>

ROS topic /IK/Planner/EE_2

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Planned pose of the end-effector of the left arm, computed via an offline
planner

</td> </tr>  
<tr>  
<td>

Reference endeffector pose of the left arm

</td>  
<td>

ROS topic /IK/Kinematic/EE_2_des

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Reference pose of the end-effector of the left arm, computed via the direct
kinematics on the basis of the reference pose of the UAV and the reference
joint positions

</td> </tr>  
<tr>  
<td>

Task errors

</td>  
<td>

ROS topic /IK/ErrorTasks

</td>  
<td>

End-effectors position and orientation errors, center of mass task error,
manipulability measure, task errors of the virtual wall tasks (between the two
arms and between the arms and the UAV)

</td> </tr> </table>

#  Table 6: Content of the dataset named: “AEROARMS Behavioural coordinated
control”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, both UNIBAS and UNICAS will
preserve a copy of the dataset.

**Dataset size** : 13.9 MB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2641131_

**2.5.** Visual servoing with actively movable camera

**Dataset name:** AEROARMS Visual servoing with actively movable camera

**Authors:** E. Cataldi, G. Antonelli, D. Di Vito, P.A. Di Lillo, F. Pierri,
F. Caccavale, A. Suarez, F. Real, G. Heredia, A. Ollero

**Data contact and responsible:** CREATE, Gianluca Antonelli

**Dataset objective:** This dataset contains the code and the experimental
data for the development, testing and validation of the task-space approach to
hand-eye coordination. The dataset can be used for performing simulations and
tests.

**Dataset description:** This dataset includes the results obtained within the
WP3 referred to the task-space approach to hand-eye coordination, described in
Deliverable D3.4. The use case for this approach is represented by an arm
involved in specific operations while the other arm is used for moving a
camera. The goal is to have the first end-effector working close to a pipe
always in the field of view of the second one. The code developed in C++ under
the ROS environment and the simulation model of the aerial dual arm
manipulator are provided. The simulation model has been developed by using the
commercial software V-Rep available with free educational license. More in
detail, by recurring to the prioritized NSB approach developed in Task T3.3,
the following equality tasks have been included for the two arms:

  * Trajectory tracking of the end-effector of the left arm, both in terms of position and orientation 

  

  * Field of View of the end effector of the right arm, equipped with a micro-camera 

  

  * Center of mass, aimed at ensuring that the center of mass of the dual-arm system is, as much as possible, aligned with that of the UAV 

  

The same set-based tasks described in Section 2.4 have been included.

The provided data have been acquired during the experiment described in
Section 5.1 of Deliverable D3.5 and conducted at the flight arena of CATEC by
using the anthropomorphic compliant and lightweight dual arm developed by USE
integrated in an hexarotor platform. More in details, the following quantities
have been collected:

  * Reference and actual position and orientation of the UAV 

  

  * Reference and actual joint positions of the two arms 

  

  * Desired (planned), reference (output of the Inverse Kinematics) and actual position and orientation of the end effectors of the left manipulator 

  

  * Position and orientation tracking error of the end effectors of the left manipulator 

  

  * Time histories of the task variables for each implemented task, included the Field of View 

  

**Data description** : The time history of the above described variables is
provided both in the _mat_ format for reading with MATLAB and in the _bag_
format for ROS. Moreover, also the _ASCII_ format is provided in order to be
used by any software. A file with the description of the formats and standards
is included in the dataset in order to facilitate sharing and re-usability.

The ROS bag (Exp_2_1_2018-05-30-14-19-21.bag) contains all measurements and
all task variables captured and logged with their corresponding ROS time
stamp.

The content of the dataset is shown in Table 7.  
  
<table>  
<tr>  
<th>

**Data**

</th>  
<th>

**Format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

UAV pose

</td>  
<td>

ROS topic:

/IK/Quadricopter_base/pose

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Measurement of the pose of the aerial platform in terms of position and
orientation (quaternion), provided by VICON system (position) and the IMU
(orientation)

</td> </tr>  
<tr>  
<td>

UAV reference

pose

</td>  
<td>

ROS topic:

/IK/Quadricopter_base/pose_des

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Reference values of the aerial platform pose output by the inverse kinematics

</td> </tr>  
<tr>  
<td>

Joint positions

</td>  
<td>

ROS topic: /joint_states

Format “sensor_msgs/JointState”

</td>  
<td>

Joint position measurements of both the arms

</td> </tr>  
<tr>  
<td>

Reference joint positions

</td>  
<td>

ROS topic /IK/jointCommand

Format “sensor_msgs/JointState”

</td>  
<td>

Reference values of the joint position of both the arms output by the inverse
kinematics

</td> </tr>  
<tr>  
<td>

End-effector pose of the right arm

</td>  
<td>

ROS topic /IK/Kinematic/EE_1

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Pose of the end-effector of the right arm, computed via the direct kinematics
on the basis of the measured pose of the UAV and the measured joint positions

</td> </tr>  
<tr>  
<td>

End-effector pose of the left arm

</td>  
<td>

ROS topic /IK/Kinematic/EE_2

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Pose of the end-effector of the left arm, computed via the direct kinematics
on the basis of the measured pose of the UAV and the measured joint positions

</td> </tr>  
<tr>  
<td>

Planned end-

effector pose of the left arm

</td>  
<td>

ROS topic /IK/Planner/EE_2

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Planned pose of the end-effector of the left arm, computed via an offline
planner

</td> </tr>  
<tr>  
<td>

Reference endeffector pose of the left arm

</td>  
<td>

ROS topic /IK/Kinematic/EE_2_des

Format

“geometry_msgs/PoseStamped”

</td>  
<td>

Reference pose of the end-effector of the left arm, computed via the direct
kinematics on the basis of the reference pose of the UAV and the reference
joint positions

</td> </tr>  
<tr>  
<td>

Task errors

</td>  
<td>

ROS topic /IK/ErrorTasks

</td>  
<td>

Left end-effectors position and orientation errors, Field of View task error,
center of mass task error, manipulability measure, task variables of the
virtual wall task between the two arms and between the arms and the UAV

</td> </tr> </table>

#  Table 7: Content of the dataset named: “AEROARMS Visual servoing with
actively movable camera”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, both UNIBAS and UNICAS will
preserve a copy of the dataset.

**Dataset size** : 15.2 MB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2641129_

  3. **Aerial tele-manipulation in inspection and maintenance**

  

  

This section is devoted to data that have been collected during the activities
performed in WP4 devoted to aerial telemanipulation in inspection and
maintenance. These datasets are grouped depending on the tasks in WP4 they are
involved in.

**3.1.** Aerial telemanipulation system

**Dataset name:** Aerial Telemanipulation System

**Authors:** R. Balachandran, M. De Stefano

**Data contact and responsible:** DLR, R. Balachandran

**Dataset objective:** This dataset contains the experimental data of the
development, testing and validation of the bilateral controller for aerial
telemanipulation. The dataset can be used for performing simulations and
tests.

**Dataset description:** The dataset was collected during the experiments
performed in WP4. The bilateral controller was used for the Cartesian space
telemanipulation of the manipulator (slave) attached to the helicopter base
(simulated by Aerial telemanipulation Simulator). The slave device is
teleoperated using a lightweight robot based haptic device (master) with force
feedback.

**Data description** : The data uses standard _.txt_ format where all the
states of the master, slave and the helicopter base are row-wise logged. It
uses MATLAB datatype double for all the states. Additionally, a . _mat_ file
(standard MATLAB file) has been made available for direct use in MATLAB. A
readme file with the description of acquired data been added to the dataset
folder in order to facilitate sharing and re-usability.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, DLR will preserve a copy of the
dataset.

**Dataset size** : 22.6 MB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2639690_

**3.2.** Local planning for constrained aerial telemanipulation

**Dataset name:** Cooperative Aerial Tele-Manipulation with Haptic Feedback

**Authors:** M. Mohammadi, A. Franchi, D. Barcelli and D. Prattichizzo

**Data contact and responsible:** CNRS, Antonio Franchi

**Dataset objective:** This dataset contains the experimental data relative to
the validation of the bilateral teleoperation scheme for cooperative aerial
manipulation in which a human operator drives a team of Vertical Take-Off and
Landing (VTOL) aerial vehicles. While the robots grasp and manipulate an
object, the human operator should receive force feedback depending on the
state of the system. This task has been chosen with the goal of showing the
capability of the framework to produce local planning for all the aerial
robots that solves the task of moving the load subject to the system
constraints.

**Dataset description:** The name of the dataset has been changed with respect
to what mentioned in D9.4 to better link the dataset to the corresponding
paper. Thus, we preferred using the title of the paper, i.e., “Cooperative
Aerial Tele-Manipulation with Haptic Feedback”. This dataset contains the data
related to the experiment used to validate the local planning for constrained
aerial telemanipulation. In the particular case, the experiment refers to the
case in which a single quadrotor aerial vehicle is commanded in order to push
an object by means of a passive tool. The most relevant data associated with
this dataset are the force commands given by the human operator, the forces
allocated by the force allocator in order to keep the contact and satisfy the
system constraints, and the measured contact forces. Furthermore, the desired
and measured robot positions have been registered.

**Data description** : The time history of the above-described variables is
provided in the _mat_ format for reading with MATLAB. Additionally, one MATLAB
script is included for plotting the main variables.

More in details, the following quantities have been collected:

  * Aerial robot desired and actual position 

  

  * Aerial robot commanded, feasible and measured forces The content of the dataset is shown in Table 8. 

  

  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Collection of data

</td>  
<td>

dataSet11.mat

</td>  
<td>

Includes position and force measurements / estimations related to the aerial
robot

</td> </tr>  
<tr>  
<td>

MATLAB script

</td>  
<td>

check_plotter.m

</td>  
<td>

Prints all the main variables mentioned above

</td> </tr> </table>

#  Table 8: Content of the dataset named: “Cooperative Aerial Tele-
Manipulation with Haptic Feedback”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, CNRS will preserve a copy of
the dataset.

**Dataset size** : 3.2 MB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2640409_

  4. **Perception for robotic manipulation in aerial operations**

  

  

This section is devoted to data that can be collected during the activities
performed in WP5 devoted to perception for robotic manipulation in aerial
operations. These datasets have been collected in preliminary tests in the
laboratory, indoor settings or in outdoor experiments. These datasets are
grouped depending on the tasks in WP5 they are involved in.

**4.1.** Adaptive perception for robot operation

Several datasets that include data for the detection and accurate localization
of the crawler adopting different approaches and in different conditions have
been published in AEROARMS:

  * Dataset **“** Crawler Direct Detection Image Dataset" contains data for the development of vision-based techniques for the direct detection of the crawler: for the detection of the crawler itself. 

  

  * Dataset **“** Image Dataset for the Crawler Indirect Detection through its Cage" contains data for the development of vision-based techniques for the indirect detection of the crawler, i.e. the detection of the crawler through the crawler's cage. 

  

  * Dataset **“** Crawler RGB-D dataset for accurate localization **”** contains data to train and test algorithms in which the aerial robot gives support to the crawler by computing its relative position and orientation in different environments and lighting conditions. 

  

In the first Data Management Plan in Deliverable D9.4, this was structured in
two datasets: "Adaptive vision for accurate grabbing" and "Perception for the
support of the aerial and ground robot operation". In the Final Data
Management Plan we finally provide three related datasets and prefer to
present them in the same section of this deliverable. The description of these
datasets are in the following:

**Dataset name: “** Crawler Direct Detection Image Dataset **”**

**Authors:** Albert Pumarola; Juan Andrade; Alberto Sanfeliu

**Data contact and responsible:** UPC, Albert Pumarola

**Dataset objective:** This dataset contains the necessary data for the
development of perception tools for the direct detection of the crawler:
detection of the crawler itself. The dataset contains images and ground-truth
position of the crawler used in the AEROARMS project experiments.

**Dataset description:** The dataset is subdivided into positive (images
containing the crawler) and negative (images NOT containing the crawler)
samples. For the positive samples, it is also included the image coordinates
(uv pixels) of the crawler centroid.

**Data description:** The dataset uses standard formats jpg for images and, as
well as _PKL_ and _XML_ for image coordinates (uv pixels) of the crawler
centroid.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, UPC will preserve a copy of the
dataset.

**Dataset size** : 2.6 GB

**Zenodo link:** _https://doi.org/10.5281/zenodo.2636697_

**Dataset name: “** Image Dataset for the Crawler Indirect Detection through
its Cage **”**

**Authors:** Javier Laplaza; Albert Pumarola; Juan Andrade; Alberto Sanfeliu

**Data contact and responsible:** UPC, Javier Laplaza

**Dataset objective:** In many computer vision complex problems it is more
convenient to detect objects indirectly rather than directly. This dataset
contains data for the development of perception tools to detect the crawler's
cage as a way of detecting the crawler itself. The dataset contains images and
ground-truth position of the crawler’s cage used in the AEROARMS project
experiments.

**Dataset description:** The dataset is subdivided into images containing and
NOT containing the crawler’s cage. For the positive samples, it is also
included the image coordinates (uv pixels) of the cage handle.

**Data description:** The dataset uses standard formats jpg for images as well
as _PKL_ and _XML_ for image coordinates (uv pixels) of the cage handle.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, UPC will preserve a copy of the
dataset.

**Dataset size** : 1.5 GB

**Zenodo link:** _https://doi.org/10.5281/zenodo.2636666_

**Dataset name:“** Crawler RGB-D dataset for accurate localization **”**

**Authors:** P. Ramon-Soria, B.C. Arrue

**Data contact and responsible:** USE, Pablo Ramón Soria

**Dataset objective:** The objective of this dataset is to provide data to
train and test algorithms in which the aerial robot gives support to the
crawler by computing its relative position and orientation in different
environments and lighting conditions.

**Data description:** The dataset contains five folders in different indoor
and outdoor environments and lighting conditions. Each folder contains the
RGB-D images obtained from an Intel RealSense d435 camera. A 3D point cloud
model of the crawler is also provided. Additionally, the calibration file has
been provided in _XML_ format.

The content of the dataset is shown in Table 9.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

PLY file

</td>  
<td>

Crawler_model.ply

</td>  
<td>

3D point cloud of the crawler

</td> </tr>  
<tr>  
<td>

Text file

</td>  
<td>

CalibrationFile.XML

</td>  
<td>

Text file containing the calibration of the camera

</td> </tr>  
<tr>  
<td>

PNG Image file

</td>  
<td>

[1,2,3,4] _[workshop,engine, machine,grass]/left_%d.png

</td>  
<td>

PNG image containing the color image from the camera

</td> </tr>  
<tr>  
<td>

PNG Image file

</td>  
<td>

[1,2,3,4]_[workshop,engine, machine,grass]/depth_%d.png

</td>  
<td>

PNG image containing the depth information from the camera. Coded in unsigned
int of 16bits

</td> </tr> </table>

#  Table 9: Content of the dataset named: “Crawler detection”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, USE will preserve a copy of the
dataset.

**Dataset size** : 76.3 MB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.3066232_

**4.2.** Multi-sensor mapping and localization

**Dataset name:** Multi-sensor mapping and localization

**Authors:** J.R. Martínez-de Dios, M. Polvillo, J.L. Paneque, V. Vega, A.
Ollero

**Data contact and responsible:** USE, J. Ramiro Martinez-de Dios

**Dataset objective:** This dataset contains the necessary data for the
development, testing and validation of 3D mapping and 6DOF localization
techniques of aerial robot in GNSSdenied environments. These datasets can be
used for the configuration and setting of the methods as well as for
performing simulations and tests.

**Dataset description:** This dataset contains measurements from two multi-
sensor 6DOF aerial robot localization and mapping experiments performed in the
Karting AEROARMS outdoor scenario in September 2018. The first experiment took
3 minutes and 56 seconds.

The second, 3 minutes and 31 seconds. Both datasets contain the RTK GPS robot
localization as ground truth.

**Data description** : The dataset uses standard formats and metadata typical
of ROS and aerial robotics to represent sensor data, robot position and
orientation, among others. A file with the description of the formats and
standards is included in the dataset in order to facilitate sharing and re-
usability.

This dataset contains two ROS bags (aeroarms_us_2018-09-06-13-33-56 and
aeroarms_us_2018-09-06-15-04-39), one for each outdoor experiment. All
measurements were captured and logged with their corresponding ROS time stamp.

The content of each ROS bag is shown in Table 10.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

UWB measurements

</td>  
<td>

ROS topic: /ranges_uwb Format “range_msgs/P2Prange” which contains:

  * ID of the UWB receiver 

  

  * ID of the UWB transmitter 

  

(anchor in the scenario)

  * range measurements in m 

  

  * timestamp 

  

  * frame ID 

  

</td>  
<td>

Ultra-Wide Band (UWB) range measurements obtained by the UWB receiver on the
aerial robot from UWB tags located at the scenario. The rate was of 20 Hz. The
location of the UWB tags can be found in a document within the dataset

</td> </tr>  
<tr>  
<td>

RTK GPS

</td>  
<td>

ROS topic: /fix

Format "sensor_msgs/NavSatFix"

</td>  
<td>

RTK GPS measurements provided by a Novatel FlexPak6D obtained in "narrow
float" accuracy level during the flight at a rate of 100 Hz

</td> </tr>  
<tr>  
<td>

RTK GPS Local

Localization

</td>  
<td>

ROS topic:

/odometry_ground_truth

Format "nav_msgs/Odometry"

</td>  
<td>

RTK GPS localization in the robot frame during the flight at a rate of 10 Hz

</td> </tr>  
<tr>  
<td>

Laser altimeter

</td>  
<td>

ROS topic:

/use_robot/lidarlite_range

Format "std_msgs/Float32"

</td>  
<td>

Altitude measurements provided by a LIDARLite v3 sensor pointing downwards at
a rate of 25 Hz

</td> </tr>  
<tr>  
<td>

IMU data

</td>  
<td>

ROS topic: /imu/data

Format "sensor_msgs/Imu"

</td>  
<td>

IMU measurements provided by a Mti-G IMU sensor at a rate of 100 Hz

</td> </tr>  
<tr>  
<td>

Velodyne data

</td>  
<td>

ROS topic: /velodyne_points

Format

"sensor_msgs/PointCloud2"

</td>  
<td>

Scans provided by a Velodyne HDL-32 lidar during the flight at a rate of 10 Hz

</td> </tr>  
<tr>  
<td>

Onboard camera

</td>  
<td>

ROS topic:

/camera/left/image_rect_color

Format "sensor_msgs/Image"

</td>  
<td>

Images from a visual camera during the flight at a rate of 10 Hz

</td> </tr>  
<tr>  
<td>

Camera internal calibration

</td>  
<td>

ROS topic:

/camera/left/camera_info

Format

"sensor_msgs/CameraInfo"

</td>  
<td>

Internal calibration of the camera

</td> </tr>  
<tr>  
<td>

External calibration transformations

</td>  
<td>

ROS topic: /tf

Format "tf2_msgs/TFMessage"

</td>  
<td>

Position of each sensor with respect to the robot frame. It is detailed by a
transformation tree with the relationships between the robot coordinate frame
(base_link) and:

  * uwb_frame (UWB coordinate frame) 

  

  * gps_frame (GPS coordinate frame) 

  

  * lidarlite_link (altimeter coordinate frame) - imu (IMU sensor coordinate frame) velodyne (velodyne coordinate frame) 

  

</td> </tr>  
<tr>  
<td>

**Type of data**

</td>  
<td>

**Data name and format**

</td>  
<td>

**Description**

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

  * camera_frame (visual sensor coordinate frame) 

  

  * camera_optical_frame(optical coordinate frame of the visual sensor) 

  

</td> </tr> </table>

#  Table 10: Content of the dataset named: “Multi-sensor mapping and
localization”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, USE will preserve a copy of the
dataset.

**Dataset size** : 6.3 GB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2648635_

**4.3.** Robust perception fusion from deployed sensors

**Dataset name:** Robust perception fusion from deployed sensors.

**Authors:** J.R. Martínez-de Dios, M. Polvillo, J.L. Paneque, A. Sanfeliu, J.
Andrade-Cetto,

A. Santamaría, M. Oetiker, E. Zwicker, V. Vega, A. Ollero

**Data contact and responsible:** USE, J. Ramiro Martinez-de Dios

**Dataset objective:** This dataset contains the necessary data for the
development of perception tools to fuse the information from the sensors on a
crawler robot and the sensors on an aerial robot. The objective is to improve
the perception required for the inspection and maintenance operations. The
datasets will be used for configuration and setting of the methods and
algorithms as well as for performing simulations and tests.

**Dataset description:** This dataset contains measurements from two
collaborative aerial robot-crawler experiments performed in the Karting
AEROARMS outdoor scenario in September 2018. The aerial robot used was a
octorotor platform developed by USE that was equipped with RTK GPS, Velodyne
32-HDL, laser altimeter, Zed stereo camera and IMU. The crawler -developed by
AIR- computed its odometry. The first experiment took 2 minutes and 47
seconds. The second, 3 minutes and 31 seconds. Both datasets contain the RTK
GPS robot localization as ground truth.

**Data description** : The dataset uses standard formats and metadata typical
of ROS and aerial robotics to represent sensor measurements and configuration
and robot position and orientation, among others. A file with the description
of the formats and standards is included in the dataset in order to facilitate
sharing and re-usability.

This dataset contains two ROS bags (aeroarms_2018-09-06-13-34-17 and
aeroarms_201809-06-15-04-40), one for each outdoor experiment. All
measurements were captured and logged with their corresponding ROS time stamp.

The content of each ROS bag is shown in Table 11.  
  
<table>  
<tr>  
<th>

**Data**

</th>  
<th>

**Format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

RTK GPS

</td>  
<td>

ROS topic: /fix

Format "sensor_msgs/NavSatFix"

</td>  
<td>

RTK GPS measurements provided by a Novatel FlexPak6D onboard the aerial robot
obtained in "narrow float" accuracy level during the flight at a rate of 100
Hz

</td> </tr>  
<tr>  
<td>

RTK GPS Local

Localization

</td>  
<td>

ROS topic:

/odometry_ground_truth

Format "nav_msgs/Odometry"

</td>  
<td>

RTK GPS localization of the aerial robot in the robot frame during the flight
at a rate of 10 Hz

</td> </tr>  
<tr>  
<td>

Laser altimeter

</td>  
<td>

ROS topic:

/use_robot/lidarlite_range

Format "std_msgs/Float32"

</td>  
<td>

Altitude measurements provided by a LIDAR-Lite v3 sensor onboard the aerial
robot pointing downwards at a rate of 25 Hz

</td> </tr>  
<tr>  
<td>

IMU data

</td>  
<td>

ROS topic: /imu/data

Format "sensor_msgs/Imu"

</td>  
<td>

Aerial robot IMU measurements provided by a Mti-G IMU sensor at a rate of 100
Hz

</td> </tr>  
<tr>  
<td>

Velodyne data

</td>  
<td>

ROS topic: /velodyne_points

Format "sensor_msgs/PointCloud2"

</td>  
<td>

Scans provided by a Velodyne HDL-32 lidar onboard the aerial robot during the
flight at a rate of 10 Hz

</td> </tr>  
<tr>  
<td>

Image1

</td>  
<td>

ROS topic: /iri/image_raw

Format "sensor_msgs/Image"

</td>  
<td>

Images of a forward pointing monocular camera onboard the aerial robot at a
rate of 10 Hz

</td> </tr>  
<tr>  
<td>

Image2

</td>  
<td>

ROS topic:

/iri/mvbluefox3_camera/cam1/image_raw

Format "sensor_msgs/Image"

</td>  
<td>

Images of a monocular camera onboard the aerial robot pointing downwards at a
rate of 40 Hz

</td> </tr>  
<tr>  
<td>

Camera1 internal calibration

</td>  
<td>

ROS topic: /iri/camera_info

Format "sensor_msgs/CameraInfo"

</td>  
<td>

Internal calibration of the forwards pointing monocular camera onboard the
aerial robot

</td> </tr>  
<tr>  
<td>

Camera2 internal calibration

</td>  
<td>

ROS topic:

/iri/mvbluefox3_camera/cam1/camera_info

Format "sensor_msgs/CameraInfo"

</td>  
<td>

Internal calibration of the monocular camera onboard the aerial robot
downwards pointing

</td> </tr>  
<tr>  
<td>

Crawler odometry

</td>  
<td>

ROS topic:

/crawler/odom_feedback

Format "nav_msgs/Odometry"

</td>  
<td>

Position estimation of the crawler

</td> </tr>  
<tr>  
<td>

**Data**

</td>  
<td>

**Format**

</td>  
<td>

**Description**

</td> </tr>  
<tr>  
<td>

External calibration transformations

</td>  
<td>

ROS topic: /tf

Format "tf2_msgs/TFMessage"

</td>  
<td>

Position of each sensor with respect to the robot frame at a rate of 100 Hz.
It is detailed by a transformation tree with the relationships between the
robot coordinate frame (base_link) and:

  * gps_frame (GPS coordinate frame) 

  

  * lidarlite_link (altimeter coordinate frame) 

  

  * imu (IMU sensor coordinate frame) 

  

  * velodyne (velodyne coordinate frame) 

  

  * iri_uvc_camera_base (forward pointing monocular camera 

  

coordinate frame)

  * iri_uvc_camera_optical (optical frame of the forward pointing monocular camera) 

  

  * iri_mvbluefox_base (coordinate frame of the downwards pointing camera) 

  

  * iri_mvbluefox_optical (optical coordinate frame of the downwards pointing camera) 

  

</td> </tr> </table>

#  Table 11: Content of the dataset named: “Robust perception fusion from
deployed sensors".

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, USE will preserve a copy of the
dataset.

**Dataset size** : 7.5 GB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2649246_

  5. **Planning for aerial manipulation in inspection and maintenance**

  

  

This section is devoted to data that have been be collected during the
activities performed in WP6. These datasets have been collected in preliminary
tests in the laboratory and in real experiments. These datasets are grouped
depending on the tasks in WP6 they are involved in.

**5.1.** Planning for aerial manipulation

**Dataset name:** A Truly Redundant Aerial Manipulator System with Application
to Pushand-Slide Inspection in Industrial Plants

**Authors:** M. Tognon, H. Tello Chavez, E. Gasparin, Q. Sablé, D. Bicego, A.
Mallet, M. Lany,

G. Santi, B. Revaz, J. Cortés and A. Franchi

**Data contact and responsible:** CNRS, Antonio Franchi

**Dataset objective:** This dataset contains the experimental data relative to
the validation of the control-aware motion planner for task constrained
motions. The task consists in inspecting a real metallic pipe with an Eddy
Current sensor, performing a raster scan. The planner is used to generate a
collision-free robot trajectory that fulfills the task requirements (sensors
in contact and perpendicular to the surface) and the robot constraints related
to its dynamics and inputs.

**Dataset description:** The name of the dataset has been changed with respect
to what mentioned in Deliverable D9.4 to better link the dataset to the
corresponding paper. Thus, we preferred using the title of the paper. The
dataset "A Truly-Redundant Aerial Manipulator System with Application to Push-
and-Slide Inspection in Industrial Plants" contains the data relative to full
experiment integrating control, motion planning and Eddy Current sensing. This
experiment is an example of contact-based inspection were the end-effector,
equipped with an Eddy Current probe, needs to scan a pipe, sliding the sensor
on its surface in order to localize a weld. Since for the experiment the
system integrates control, motion planning and sensing as well, the dataset
does not only contain data relative to motion planning but also to control and
sensing as well. Regarding the control, one can check its performance in terms
of tracking error and task fulfillment: sensor always in contact and
perpendicular to the surface. Regarding the motion planner, the dataset
contains the desired trajectory of end-effector and state computed with our
proposed 'control-aware motion planner'. The computed trajectory allows to
execute a raster-scan not only respecting the task constraints, but also the
ones related to the dynamics and inputs of the system. Finally, the dataset
contains the raw and postprocessed measurements coming from the sensor. Based
on those, one can conclude that a weld can be effectively located and that the
contact-based inspection in exam is feasible with such an aerial manipulator.

More in details, the following quantities have been collected:

  * End-effector desired and actual pose (position plus orientation) 

  

  * Aerial platform desired and actual pose (position plus orientation) 

  

  * Joint desired and actual angles 

  

  * Raw and post-processed measurements of the EC sensor 

  

**Data description:** The time history of the above described variables is
provided in the _mat_ format for reading with MATLAB. Additionally, four
MATLAB scripts are included for plotting the main variables. A readme file is
also present with instructions for plotting.

The content of the dataset is shown in Table 12.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Collection of data

</td>  
<td>

18-09-01_12-14.mat

</td>  
<td>

Includes the row and post-processed data coming from the EC sensor

</td> </tr>  
<tr>  
<td>

Collection of data

</td>  
<td>

MATLAB.mat

</td>  
<td>

Include the end-effector and state trajectories computed by the planner; the
actual end-effector and state trajectories

</td> </tr>  
<tr>  
<td>

MATLAB script

</td>  
<td>

print_main_variables.m

</td>  
<td>

it prints all the main variables meaning the 1) lift-off 2) weld signal 3)
end-effector position error 4) end-effector attitude error 5) position of the
aerial platform 6) attitude of the aerial platform 7) joint one 8) joint two

</td> </tr>  
<tr>  
<td>

MATLAB script

</td>  
<td>

print_raw_signals.m

</td>  
<td>

it prints the raw data coming from the EC sensor

</td> </tr>  
<tr>  
<td>

MATLAB script

</td>  
<td>

print_traj_3d.m

</td>  
<td>

print a 3d image of: the pipe, the desired and real trajectory of the end-
effector highlighting in blue and red the parts in contact or not with the
pipe, respectively, the parts in which the sensor detect the presence of a
weld, the estimated mapping of the weld on the pipe, the real map of the weld
on the pipe

</td> </tr> </table>

#  Table 12:Content of the dataset named: “A Truly Redundant Aerial
Manipulator System with Application to Pushand-Slide Inspection in Industrial
Plants”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, CNRS will preserve a copy of
the dataset.

**Dataset size** : 14.2 GB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2640361_

**5.2.** Control-based local optimization methods for planning

**Dataset name:** Control-based local optimization methods for planning

**Authors:** E. Cataldi, G. Antonelli, D. Di Vito, P.A. Di Lillo

**Data contact and responsible:** CREATE, Gianluca Antonelli

**Dataset objective:** This dataset contains the code and the experimental
data for the development, testing and validation of a planner, which has been
designed with the purpose to have an agile system able to perform operations
inside a dense industrial installation, taking into consideration several
obstacles inside the workspace and online (re-)planning. In particular, the
proposed approach is based on merging control-based local optimization methods
inside the planning algorithms (Task T6.1).

**Dataset description:** The dataset includes all data collected from the
experiments performed on a mockup represented by a fixed-base 7 DOFs
manipulator in two different scenarios. In the first case, a static
environment has been considered. In the second one, the user places an
obstacle on the manipulator’s path in real-time. Thus, a re-planning results
necessary to manage this change in the environment.

**Data description** : The time history of the variables involved into the
planner are provided in ASCII format, to be used from any kind of software.
The code of the planning algorithm, developed in C++ under the ROS
environment, is provided as well.

The content of the dataset is shown in Table 13:  
  
<table>  
<tr>  
<th>

**Data**

</th>  
<th>

**Format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

dist_dynamic_obstacle.txt

</td>  
<td>

ASCII format

</td>  
<td>

end-effector, wrist and elbow distances from the obstacle introduced in real-
time in the workspace

</td> </tr>  
<tr>  
<td>

distance_from_joint2.txt

</td>  
<td>

ASCII format

</td>  
<td>

end-effector and wrist distances from 3D-Space joint2 position

</td> </tr>  
<tr>  
<td>

distance_from_joint3.txt

</td>  
<td>

ASCII format

</td>  
<td>

end-effector and wrist distances from 3D-Space joint3 position

</td> </tr>  
<tr>  
<td>

distance_from_obstacles.txt

</td>  
<td>

ASCII format

</td>  
<td>

end-effector, wrist and elbow distances from the three obstacles present in
the workspace

</td> </tr>  
<tr>  
<td>

joint_ik.txt

</td>  
<td>

ASCII format

</td>  
<td>

joints positions

</td> </tr>  
<tr>  
<td>

joint_velocity.txt

</td>  
<td>

ASCII format

</td>  
<td>

joint velocities

</td> </tr>  
<tr>  
<td>

jointLimit2.txt

</td>  
<td>

ASCII format

</td>  
<td>

second joint position limit

</td> </tr>  
<tr>  
<td>

jointLimit4.txt

</td>  
<td>

ASCII format

</td>  
<td>

fourth joint position limit

</td> </tr>  
<tr>  
<td>

jointLimit6.txt

</td>  
<td>

ASCII format

</td>  
<td>

sixth joint position limit

</td> </tr>  
<tr>  
<td>

virtualWallZ.txt

</td>  
<td>

ASCII format

</td>  
<td>

distance from the horizontal plane set at the baseframe of the manipulator

</td> </tr>  
<tr>  
<td>

ee_velocity.txt

</td>  
<td>

ASCII format

</td>  
<td>

end-effector linear and angular velocities

</td> </tr> </table>

#  Table 13: Content of the dataset named: “Control-based local optimization
methods for planning”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, UNICAS will preserve a copy of
the dataset.

**Dataset size** : 31.1 MB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2641158_

**5.3.** Reactivity for safe operation

**Dataset name:** Reactivity for safe operation.

**Authors:** A. Caballero, F. Real, A. Suárez, V. Vega, M. Béjar, A.
Rodríguez-Castaño, A. Ollero.

**Data contact and responsible:** USE, Álvaro Caballero.

**Dataset objective:** This dataset contains the necessary data for the
development, testing and validation of reactive techniques for obstacle
avoidance of aerial manipulators in industrial environments. This dataset can
be used as benchmark for other methods and algorithms as well as for
performing simulations and tests.

**Dataset description:** The provided dataset includes planning results of the
different local replanning algorithms described in Section 5.3 of Deliverable
D6.2. This dataset is the result of applying such algorithms to the
application scenarios presented in Sections 7.3 and 8.2 of Deliverable D6.2
for the Aerial Robotic System for Long-Reach Manipulation in Section 2.2 of
the same deliverable.

**Data description** : The data have been classified in a set of subfolders
organized hierarchically according to the used algorithm, the application
scenario and the origin of the data, i.e., simulation or real experiments.
Each subfolder contains two MATLAB files (MotionPlan.mat and Execution.mat)
with the main information of both the computed motion plan and its execution
by the aerial manipulator.

The content of these files is explained in Table 14. Additionally, a README
file with a more detailed description has been included in the dataset.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Motion Plan

</td>  
<td>

MotionPlan.InitialState

</td>  
<td>

Initial state

</td> </tr>  
<tr>  
<td>

MotionPlan.GoalState

</td>  
<td>

Goal state

</td> </tr>  
<tr>  
<td>

MotionPlan.NumberOfIterations

</td>  
<td>

Number of iterations to compute the plan

</td> </tr>  
<tr>  
<td>

MotionPlan.NumberOfNodes

</td>  
<td>

Number of nodes within the plan

</td> </tr>  
<tr>  
<td>

MotionPlan.Nodes

</td>  
<td>

State associated to each node

</td> </tr>  
<tr>  
<td>

MotionPlan.Time

</td>  
<td>

Timestamp associated to each node

</td> </tr>  
<tr>  
<td>

MotionPlan.Cost

</td>  
<td>

Cost associated to each node

</td> </tr>  
<tr>  
<td>

MotionPlan.Parent

</td>  
<td>

Parent associated to each node

</td> </tr>  
<tr>  
<td>

MotionPlan.OptimalTrajectory

</td>  
<td>

Nodes in the optimal trajectory

</td> </tr>  
<tr>  
<td>

Execution of the motion plan

</td>  
<td>

t

</td>  
<td>

= Execution(1,:)

</td>  
<td>

Timestap

</td> </tr>  
<tr>  
<td>

q1_ref

</td>  
<td>

= Execution(2,:)

</td>  
<td>

Reference for the longitudinal position of the aerial platform

</td> </tr>  
<tr>  
<td>

q3_ref

</td>  
<td>

= Execution(3,:)

</td>  
<td>

Reference for the vertical position of the aerial platform

</td> </tr>  
<tr>  
<td>

q7R_ref

</td>  
<td>

= Execution(4,:)

</td>  
<td>

Reference for the angular position of the right upper link of the dual arm

</td> </tr>  
<tr>  
<td>

q8R_ref

</td>  
<td>

= Execution(5,:)

</td>  
<td>

Reference for the angular position of the right lower link of the dual arm

</td> </tr>  
<tr>  
<td>

q7L_ref

</td>  
<td>

= Execution(6,:)

</td>  
<td>

Reference for the angular position of the left upper link of the dual arm

</td> </tr>  
<tr>  
<td>

q8L_ref

</td>  
<td>

= Execution(7,:)

</td>  
<td>

Reference for the angular position of the left lower link of the dual arm

</td> </tr>  
<tr>  
<td>

q1

</td>  
<td>

= Execution(8,:)

</td>  
<td>

Longitudinal position of the aerial platform

</td> </tr>  
<tr>  
<td>

q3

</td>  
<td>

= Execution(9,:)

</td>  
<td>

Vertical position of the aerial platform

</td> </tr>  
<tr>  
<td>

q7R

</td>  
<td>

= Execution(10,:)

</td>  
<td>

Angular position of the right upper link of the dual arm

</td> </tr>  
<tr>  
<td>

q8R

</td>  
<td>

= Execution(11,:)

</td>  
<td>

Angular position of the right lower link of the dual arm

</td> </tr>  
<tr>  
<td>

q7L

</td>  
<td>

= Execution(12,:)

</td>  
<td>

Angular position of the left upper link of the dual arm

</td> </tr>  
<tr>  
<td>

q8L

</td>  
<td>

= Execution(13,:)

</td>  
<td>

Angular position of the left lower link of the dual arm

</td> </tr> </table>

#  Table 14: Content of the dataset named: “Reactivity for safe operation”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, USE will preserve a copy of the
dataset.

**Dataset size** : 7.5 MB.

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2641949_

  6. **Validation in the industrial scenario**

  

  

This section is devoted to data that have been collected during the activities
performed in WP8. These datasets have been collected in preliminary tests in
the laboratory and in real experiments. These datasets are grouped depending
on the tasks in WP8 they are involved in.

**6.1.** Installing an EC Sensor on a remote location

**Dataset name:** Installing an EC Sensor on a remote location

**Authors:** E. Gasparin, B. Revaz

**Data contact and responsible:** SENS, Bernard Revaz

**Dataset objective:** Validate the consistency and quality of the data
received by the EC (Eddy Current) sensor when manipulated remotely by a drone
or deployed for permanent monitoring.

**Dataset description:** The dataset provides the EC (Eddy Current) data
obtained during a drone-operated inspection and after the release of the
sensor to a specific location. The collected measurements allow to assess the
capability of detecting relevant features for an EC inspection. This involves
the possibility of recognizing the relevant signatures in the signal, such as,
for example, when the probe crosses a weld or a crack on the inspected
structure. Thus, aiming to demonstrate the feasibility of the inspection
process in the project scenario.

**Data description** : The dataset is organized with the following sections:

  * EXP001: EC data collected during the validation experiments. The EC was installed on the CATEC manipulated and deployed. The measurements refer to the validation experiments performed at the Cement kiln in Seville, Spain. 

  

  * Software: the UPecView software necessary to open the *.sidata files. 

  

  * plots: a preview plot of the experimental data. 

  

The collected EC data are stored in two formats:

  1. *.sidata files: These files are proprietary format that can be opened with the software “UPecView” supplied by Sensima Inspection 

  

  

(http://www.sensimainsp.com). This software provides an interface familiar to
what expected by eddy-current inspectors. Each file includes all the relevant
information that may be used for analysis: the measurements and the instrument
configuration (ex. Excitation frequency of the probe) is contained in this
file.

  2. *.csv files: The csv files contain an export of the measurements only (without instrument settings); a comma separator is used. 

  

  

The content of the dataset is shown in Table 15.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

*.csv and *.sidata 
</td>  
<td>

EXP001/0001A

</td>  
<td>

Manual calibration block scan with cracks, Seville, Spain.

</td> </tr>  
<tr>  
<td>

*.csv and *.sidata 
</td>  
<td>

EXP001/0001B

</td>  
<td>

Manual reference weld pipe scan, Seville, Spain.

</td> </tr>  
<tr>  
<td>

*.csv and *.sidata 
</td>  
<td>

EXP001/0002A

</td>  
<td>

CATEC Drone overall scan inspection and sensor deployment, Cement kiln,
Seville, Spain.

</td> </tr>  
<tr>  
<td>

*.csv and *.sidata 
</td>  
<td>

EXP001/0002B

</td>  
<td>

Deployed sensor, Cement kiln, Seville, Spain.

</td> </tr>  
<tr>  
<td>

*.csv and *.sidata 
</td>  
<td>

EXP001/0002C

</td>  
<td>

Deployed sensor, Cement kiln , Seville, Spain.

</td> </tr>  
<tr>  
<td>

*.csv and *.sidata 
</td>  
<td>

EXP001/0002D

</td>  
<td>

Permanent sensor removal, Cement kiln, Seville, Spain.

</td> </tr>  
<tr>  
<td>

*.exe, program 

installer

</td>  
<td>

Software/

UPECView_1.7.1.3.rc_win-

64_cxf_Setup.exe

</td>  
<td>

UPecView software installer.

</td> </tr>  
<tr>  
<td>

images

</td>  
<td>

plots/~multiple files~

</td>  
<td>

Preview plot of the datafiles contained in the section “data”. There is a
direct correspondence of the filename.

</td> </tr> </table>

#  Table 15: Content of the dataset named: “Installing an EC Sensor on a
remote location”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, SENS will preserve a copy of
the dataset.

**Dataset size** : 272.9 MB

**Zenodo link** :  _https://doi.org/10.5281/zenodo.2652208_

**6.2.** Deploying, operation and maintenance of a mobile robot

**Dataset name:** TRIC Crawler Internal Localization on Pipe Segment
Verification by

External Ground Truth System

**Authors:** M. Oetiker

**Data contact and responsible:** AIR, Moritz Oetiker

**Dataset objective:** Validation of TRIC Crawler internal localization in an
industrial scenario (on a carbon steel pipe segment).

**Dataset description:** The TRIC magnetic inspection crawler, once it is
deployed by the UAV, needs to move on the surface of an elevated pipe-segment,
while localizing itself. The internal localization system is the main
reference for inspection and only receives position updates from the UAV
during periodic “maintenance” flights of the UAV. The dataset is providing a
comparison of the internal localization (odometry and IMU, matching the TRIC
to the pipe surface) and an external ground truth measurement recorded by the
Optitrack camera system. The longitudinal coordinate (x-axis) is uncertain
because it cannot be determined by the TRIC crawler in an absolute way. In
postprocessing of the experiment, a position update by the UAV was simulated
to correct the x-axis drift.

**Data description** : The dataset contains photographs and a video of the
experiment, plots of the results (including the simulated position updates),
raw-data and python code to access the raw data.

The content of the dataset is shown in Table 16.  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Data name and format**

</th>  
<th>

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Sqlite database containing a pickle file on each entry

</td>  
<td>

11.4.2018 the path

09_08.bag.sqlite

</td>  
<td>

of

</td>  
<td>

moe

</td>  
<td>

crawlerPoseMessage(DataMessage) defined

in BasicMessages.py (=Crawler pos) MocapRigidBodyMessage(PoseMessage) defined
in mocap_message.py (=Optitrack)

</td> </tr>  
<tr>  
<td>

Sqlite database containing a pickle file on each entry

</td>  
<td>

11.4.2018 the path

10_35.bag.sqlite

</td>  
<td>

of

</td>  
<td>

moe

</td>  
<td>

crawlerPoseMessage(DataMessage) defined

in BasicMessages.py (=Crawler pos) MocapRigidBodyMessage(PoseMessage) defined
in mocap_message.py (=Optitrack)

</td> </tr>  
<tr>  
<td>

Sqlite database containing a pickle file on each entry

</td>  
<td>

11.4.2018 the path

10_54.bag.sqlite

</td>  
<td>

of

</td>  
<td>

moe

</td>  
<td>

crawlerPoseMessage(DataMessage) defined

in BasicMessages.py (=Crawler pos) MocapRigidBodyMessage(PoseMessage) defined
in mocap_message.py (=Optitrack)

</td> </tr>  
<tr>  
<td>

Sqlite database containing a pickle file on each entry

</td>  
<td>

11.4.2018 the path

13_01.bag.sqlite

</td>  
<td>

of

</td>  
<td>

moe

</td>  
<td>

crawlerPoseMessage(DataMessage) defined

in BasicMessages.py (=Crawler pos) MocapRigidBodyMessage(PoseMessage) defined
in mocap_message.py (=Optitrack)

</td> </tr>  
<tr>  
<td>

Sqlite database containing a pickle file on each entry

</td>  
<td>

9.4.2018 17_41.bag.sqlite

</td>  
<td>

</td>  
<td>

crawlerPoseMessage(DataMessage) defined

in BasicMessages.py (=Crawler pos) MocapRigidBodyMessage(PoseMessage) defined
in mocap_message.py (=Optitrack)

</td> </tr> </table>

#  Table 16: Content of the dataset named: “TRIC Crawler Internal Localization
on Pipe Segment Verification by External Ground Truth System”.

**Dataset sharing:** Open access (always in accordance with the GA and the CA
clauses).

**Archiving and preservation:** This dataset is archived in ZENODO and linked
with OpenAIRE. The ZENODO link has been made available at a Dataset section in
the AEROARMS website. Besides, for redundancy, USE will preserve a copy of the
dataset.

**Dataset size** : 822.7 MB

**Zenodo link** :  _http://doi.org/10.5281/zenodo.2643087_

**7\. Conclusions**

This document presented the final Data Management Plan (DMP) of the AEROARMS
project. The first version was submitted in M6. The objective is to detail
what data the project has generated, whether and how it has been exploited and
made accessible for verification and re-use, and how it will be curated and
preserved.

These datasets are classified depending on the tasks they are involved in the
project. The document is structured in 4 Sections, each devoted to the
datasets of WPs 3, 4, 5, 6 and 8. All datasets have been had available to the
community in Zenodo and have been linked to OpenAire.