Robotic System
To explore the impact of dynamic motion on gas leakage
source localization, two robotic systems equipped with micro-
phones were used: a fixed-based robotic arm and a quadruped
robot (robotic dog). The robotic arm, a collaborative robotic
arm developed by Universal Robots and known for its pre-
cision and repeatability (±0.03 mm), was used to study the
effects of translational and rotational microphone motions
while the sound source remained fixed. Its high accuracy
supports the development of localization algorithms by min-
imizing estimation errors. However, its fixed-base design and
850 mm reach limit its applicability in industrial settings.
In contrast, the robotic dog, a quadrupedal robotic
platform, is more suitable for deployment in industrial settings.
This robotic dog is equipped with a 16-channel lidar and
a depth camera. It can map the environment, detect pipe-
lines with potential gas leakage, and generate depth maps
for precise localization. Its ability to navigate varying terrains
enables broader coverage for gas leak detection. It can also be
enhanced with biomimetic pinnae-like structures, mimick-
ing feline ear movements (yaw, pitch, roll, and independent
motion) to improve sound localization by capturing multiple
acoustic samples in complex settings. This enhancement
presents an intriguing direction for future work, as the findings
of Ruhland et al. (2015) suggest that the coordinated movements
of a cat’s head and pinna enable the collection of multiple
acoustic samples to improve sound localization accuracy. This
also introduces the possibility of fully emulating animal head
and ear movements for enhanced sound source localization.
Although we attempted to incorporate feline pinnae and head
movements into our system, the inconsistency and unreliability
of the data, compared to findings from Young et al. (1996), pre-
vented us from achieving the desired results.
For the initial study, the microphones were mounted
on a custom plate attached to the robotic arm, as shown in
Figure 2a. Positioned 90 mm apart on a circular disc, they
mimic the locations of feline pinnae. A similar mounting
bracket was created to hold and position the two microphones
on the back of the robotic dog, as depicted in Figure 2b.
Experimental Setup
Biological creatures like cats and dogs rotate their heads or
move their bodies to localize sound sources and enhance the
TDOA of sound waves at their ears. These movements improve
auditory cues that the brain uses to determine the direction
and distance of a sound. When a sound originates from one
side, it reaches the nearer ear slightly earlier than the farther
ear. This small difference in time, known as the interaural time
difference (ITD), helps the brain determine the direction of the
sound source. By moving their heads or bodies, these creatures
change the relative position of their ears to the sound source,
creating different TDOA patterns that provide additional spatial
cues for more accurate localization.
To replicate these complex motions in a simplified manner,
they are divided into linear and rotational components. For
linear movements, microphones were mounted on the robotic
arm’s end-effector. The arm followed a linear path using a
linear path planner (MoveL), traversing an 8 ​ ×​ 8 grid with
50 mm increments along the X and Y axes. This systematic
movement covered the designated area for sound localization
experiments, with the sound source positioned 1.79 m from the
robotic arm’s base along the Y-axis, as shown in Figure 3a.
Rotational movements were also replicated in a separate
experiment to mimic the head-turning behavior of biological
creatures during sound localization. Microphones mounted
on the robotic arm’s end-effector were rotated along the Z-axis
from 10° to 170° in 5° increments, as shown in Figure 3b. This
systematic variation in microphone orientation was designed
to evaluate how angular positioning affects the TDOA and
enhances the accuracy of determining the direction of arrival
(DOA) of sound.
ME
|
LEAKLOCALIZATION
Leakage
Distance =1790 mm
θ =10°
θ =170°
Δθ =5°
Δx =50 mm
Δy =50 mm Point 64
Point 1
y
x
y
x
Figure 3. Movement of robotic arm: (a) linear along the X and Y axes
(b) rotational around the Z-axis.
3D printed handle
Lidar
Depth camera
z
x
y
90 mm
3D printed handle
90 mm
z x
y
Figure 2. Microphone attachments to (a) the robotic arm and (b) the robotic dog.
54
M AT E R I A L S E V A L U AT I O N • A P R I L 2 0 2 5

Results and Discussion
The results indicate that using only two TDOAs, obtained
by positioning the robotic arm at two points on the grid, the
leakage direction and distance can be estimated with an
error of less than 1° and 40 mm, respectively. Figure 4 illus-
trates the minimum achievable error when each grid point is
paired with other points. One can clearly notice that not all
combinations result in 40 mm distance accuracy, highlight-
ing the importance of systematically selecting the second
point for the second TDOA for distance estimation. Figure 4
explores this concept further by examining the combinations
when points 1 and 29 are used as the initial measurement
points. While most combinations yield good accuracy, there
are certain points that the robot should avoid as the second
measurement location to maintain high accuracy. Figure 5
provides color maps of the grid, where the white square indi-
cates the first measurement point, representing the robot’s
initial position. By accessing the error distribution for each
grid point, the robot can strategically choose the second mea-
surement point to minimize estimation error.
Analysis of Figure 5 reveals that, in most cases, moving ver-
tically is not a wise choice, and horizontal movement is gen-
erally more effective. This is because vertical movement often
fails to provide additional information for solving the hyper-
bolic equations due to insufficient change of TDOA, as indi-
cated in Figures 5c and 5d. This leads to substantial errors in
source localization, as the two resulting hyperbolas tend to be
nearly parallel, reducing the accuracy of the estimation.
Access to maps like those in Figure 5 can significantly aid
the agent in decision-making to obtain the second TDOA.
However, in real-world scenarios or with larger grids, con-
structing these maps and providing the agent with access
becomes challenging. Therefore, we explored the range of
errors the agent might encounter if it selects points randomly.
As discussed earlier, relying on only two points on the grid
and choosing the second location randomly is not an effec-
tive solution. Generally, increasing the number of points
improves source localization accuracy. The key question
is how many points the agent should randomly select to
achieve reliable accuracy. To investigate this, for each fixed
first location, we randomly selected two additional points 100
0
0
0.2
0.4
0.6
10 20 30
Second data collection point
40 50 60 70
0
0
0.2
0.1
10 20 30
Second data collection point
40 50 60 70
0
0
0.2
0.4
0.6
10 20 30
Initial measurement point
40 50 60
0
0.2
0.4
0.6
0.8
1.0
10 20 30
Initial measurement point
40 50 60
Figure 4. Minimum achievable error for each point in combination with subsequent points: (a) error in distance estimation (b) error in direction
estimation. Error distribution in leak distance estimation: Analysis based on fixing the first measurement point and its combinations with
subsequent points: (c) Point 1 (d) Point 29.
A P R I L 2 0 2 5 • M AT E R I A L S E V A L U AT I O N 55
Error
(m)
Error
(m)
Error
(m)
Error
(m)