IEEE Robotics & Automation Magazine - September 2018 - 15

Myo bracelets were connected via Bluetooth to their dedicated
PCs, where the processing described previously was executed
to retrieve the operator's arm pose and orientation. Although
in the present work it is not specifically addressed, the communication channel plays a paramount role for the achievement of our objectives. In fact, it was shown that high
communication delays in visuo-haptic applications (>150 ms)
significantly degrade performance [17]. For these reasons, for
future development we will build a robust and effective communication channel, e.g., refining existing perceptually motivated compression approaches of the transmitted data (dead
band and prediction approaches) to enable a proper information exchange.
Software Architecture
Given the target of the mission and the new robot setup, a flexible and easily reconfigurable software platform was needed.
We chose the Cross-Bot-Core (XBotCore) [18] robot control
framework, which satisfies hard real-time (RT) requirements,
ensuring 1-kHz control loop in EtherCAT-based robots. The
robot software architecture played a key role in the mission
success: it guaranteed control module code reusability and
interoperability with the Yet Another Robot Platform (YARP)
[19] non-RT framework. XBotCore is a novel approach to
configure low-level control systems by using modern description formats, such as the Universal Robot Description Format
(URDF) [32] and the Semantic Robot Description Format
(SRDF) [33], which are traditionally used for high-level software components. Thanks to the introduced abstractions, it is
possible to control different robots or different parts of the
same robot without code changes: the application programming interface (API) provided to control the robot is dynamically built starting from the robot URDF/SRDF. Modifying the
SRDF, e.g., removing a kinematic chain, such as the torso,
results in a different API for the user that is compatible with
the available/desired parts of the robot to control. We exploited
this feature by removing the leg chains from the SRDF, and we
controlled the humanoid upper body using a YARP module
without any code modification.
Control and Perception
Teleoperation Module
To remotely control the upper body of the WALK-MAN
robot, we developed a dedicated control module, which
receives the information needed from the pilot station to
reproduce the teleoperator movements on the robot. In particular, three kinds of data are sent to the control module and
then translated to a robot joint motion: the head orientation,
the pose of the wrists, and the amount of hand closure.
The quaternion representing the operator's head orientation with respect to the plane perpendicular to the gravity vector is translated, by means of a linear map, in the yaw and pitch
joint of the head and in the yaw joint of the torso. The rotation
corresponding to the roll angle has not been considered. For
each arm of the teleoperator, using the two Myo armband

bracelets' relative orientation, the cartesian pose of the wrist
with respect to the shoulder is computed. This pose is then
scaled to map the human arm to the robot arm, and it is sent
through the network. When the pose is received by the control
module, a Jacobian-based inverse kinematics is performed,
obtaining the desired arm joint's position. Note that at the system start-up, the teleoperator assumes a predefined homing
position to define the relative position of the two Myos.
Thanks to the EMG sensors of the Myo armband bracelets,
a value proportional to the signal representing the muscular
activity on each forearm is obtained using a linear map. This
value represents the desired position for the hand motor. This
is very convenient for the human operator: because the Myo
bracelets are positioned on the forearm, a muscular activity
can be generated by opening and closing the hand;
consequently, the robot
To remotely control the
will move the hand as the
teleoperator does. The
upper body of the WALKobtained desired joint
positions for the hands,
MAN robot, we developed a
arms, torso, and head
joints are then sent to the
dedicated control module.
low-level controller of the
motor boards, resulting in
a robot motion. In each part of this control scheme, safety
bounds are checked before moving the robot to avoid self-collisions. A tuning phase for each teleoperator takes place before
the experiments, because each person is characterized by different electromyographic signals. During this phase, the teleoperator is required to raise the arms and keep them fixed in a
straight pose for 3 s.
Vision Module
To visually examine the inspected building, we used the exteroceptive sensors, i.e., lidar and RGB-D cameras, to acquire crucial information about the structure of the indoor environment.
For this purpose we developed two different vision-processing
modules dedicated to different measurements acquisition.
Plane Detection Module
The first module has been developed to analyze the structure
of the scene by searching for planar regions in it. If the extracted planes are bigger than a certain threshold, they are categorized in four different types: ceiling, floor, and frontal and
lateral wall. This categorization is necessary for inspection in
disaster scenarios, e.g., to recognize cracks or anomalous inclination of walls (see Figure 6). For the classification, the relative
orientation between the planes and the robot head is used.
Moreover, the pilot can compare the relative distance and orientation of two planes by selecting them through the PI.
The plane estimation algorithm uses as input the lidar data
provided by the rotating laser scanner of the MultiSense-SL
head. The point cloud that has been used for plane classification is obtained by acquiring and accumulating 10 s of laser
data to allow a whole environment scanning [Figure 7(a)].
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IEEE Robotics & Automation Magazine - September 2018

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