# Multi-Robot Strategies for Communication-Constrained Exploration and
Electrostatic Anomaly Characterization

Gjosse Zijlstra1, Karen L. Aplin2, Edmund R. Hunt1 1School of Engineering
Mathematics and Technology, Faculty of Engineering, University of Bristol,
Bristol, UK.
Email<EMAIL_ADDRESS>of Civil, Aerospace, and Design.
Faculty of Engineering, University of Bristol, Bristol, UK.GZ is supported by
the UK EPSRC (EP/L015293/1). ERH is supported by the Royal Academy of
Engineering under the Research Fellowship program.

###### Abstract

Exploration of extreme or remote environments such as Mars is often recognized
as an opportunity for multi-robot systems. However, this poses challenges for
maintaining robust inter-robot communication without preexisting
infrastructure. It may be that robots can only share information when they are
physically in close proximity with each other. At the same time, atmospheric
phenomena such as dust devils are poorly understood and characterization of
their electrostatic properties is of scientific interest. We perform a
comparative analysis of two multi-robot communication strategies: a
distributed approach, with pairwise intermittent rendezvous, and a
centralized, fixed base station approach. We also introduce and evaluate the
effectiveness of an algorithm designed to predict the location and strength of
electrostatic anomalies, assuming robot proximity. Using an agent-based
simulation, we assess the performance of these strategies in a 2D grid cell
representation of a Martian environment. Results indicate that a decentralized
rendezvous system consistently outperforms a fixed base station system in
terms of exploration speed and in reducing the risk of data loss. We also find
that inter-robot data sharing improves performance when trying to predict the
location and strength of an electrostatic anomaly. These findings indicate the
importance of appropriate communication strategies for efficient multi-robot
science missions.

##  I Introduction

Exploring extreme environments with multi-robot systems, such as Mars or the
Moon, presents many challenges. One such challenge is having reliable and
effective communication links between robots to allow data transfers,
especially if the robots travel far away from each other and there is no
supporting communication infrastructure. Meanwhile, a scientific opportunity
in exploring other planets is the lack of research into phenomena in
atmospheric electricity, an example of which are dust devils, which are
suspected to be a source of Martian lightning [1]. Here, we compare two multi-
robot communication strategies: a distributed approach with intermittent
rendezvous between pairs of robots, and a fixed base station approach. In both
strategies, exploration data can only be shared within a certain proximity. We
also consider how we can use multi-robot systems to locate a dust devil and
estimate both its position and strength, assuming communications proximity in
the first instance.

Our goals are to first, determine which communication strategies can explore
the environment the fastest and also evaluate the amount of data at risk if
the system loses a robot. This research can contribute to decisions about
which communication architectures are useful when multi-robot systems are
considered for exploration of Mars, the Moon, or communication-constrained
environments on Earth. We hypothesize that the intermittent pairwise
rendezvous communication system will demonstrate a faster exploration rate
compared to the centralized base approach, and show a lower risk of data loss
because of more frequent meet-ups. This is based on the notion that the
flexibility of intermittent rendezvous might allow for more dynamic data
sharing and shorter navigational paths for agents to travel. Our second goal
is to consider the opportunity and potential approach to using multi-robot
systems to detect and characterize electrostatic anomalies such as dust
devils. We hypothesize that the availability of multiple, distributed gradient
readings in a multi-robot system will allow significantly quicker localization
and characterization of anomalies. We present some background on recent
extraterrestrial exploration missions, electrostatic research and dust devil
phenomena before describing our algorithm and simulation approach.

##  II Background

In robotic Mars missions to date, exploration has only been performed by one
rover, although the Perseverance Rover has carried a small helicopter,
Ingenuity [2]. Ingenuity served multiple purposes: among these was acting as a
technology demonstrator, showcasing the first instance of multi-robot
communication on Mars. The radio systems onboard Ingenuity utilize a power-
saving approach, with a range around 250 m for higher bandwidth settings and
around 1 km when used in lower bandwidth settings. [3]. There is ongoing
research into the potential to use multi-robot systems, which could comprise,
for example, smaller rovers [4] or legged robots [5, 6]. Currently, there is a
Moon mission by JAXA (Japan Aerospace Exploration Agency) where they have
deployed two heterogeneous smaller robots [7], seen in Fig. 1 In addition to
mapping physical terrain, robots could be used for mapping other physical
variables of note; we are particularly interested in atmospheric electricity
as a motivating example [8]. We have considered the use of electric field
mills mounted to rover robots, which might realistically number 4–6 (Fig. 2,
[9]). One motivation to measure the electrostatic field is to detect and
locate dust devils. Dust devils are atmospheric vortices which can vary
considerably both in width (0.5–30 m) and height (30 m–2 km, [10, 11]). The
dust particles that get picked up rub against each other (triboelectrically
charged) and create large electrostatic fields, which can be measured remotely
[12]. To date, there have been no measurements of the electrostatic fields
created by dust devils on Mars but there are simulation studies of dust devils
on Earth and on Mars, which estimate the limits of dust devil electric
discharge [13]. In order characterize dust devils using a multi-robot system
we develop an algorithm to turn the locally read electrostatic field
measurements into a prediction of a dust devil’s location and strength. Dust
devils are poorly understood, and analyzing the direction and velocity of them
would allow for a greater understanding of the underlying physical mechanisms.
An example of past research into multi-robot algorithms for environment
anomaly detection include CIMAX (‘Collective Information Maximization’) [14],
a decentralized approach that assumes a rather high density of swarm robots
that may not be feasible in space missions. The use of virtual potential
fields is quite common as a control approach for robot path planning (e.g.
[15, 16, 17]), and has been considered in the context of planetary exploration
[18], although navigation informed by the physical electrostatic field has
not, to our knowledge, been considered.

Figure 1: JAXA moon robots, LEV-1 and LEV-2. Figure 2: Rover robot with
electric field mill for electrostatic electricity mapping [9].

Key elements of deploying a multi-robot system are effective exploration and
communication strategies. An example of a real-time Coverage Path Planning
(CPP) solution is presented in [19]. With regard to communication, there is
research into maintaining connectivity with the use of micro-robots on lunar
exploration missions [20]. Paet et al. [20] describe a way of maintaining a
connection between robots by using radio propagation and communication graphs.
They simulated a network of three robots whereby the rovers’ mobility affected
the network connectivity. A multi-robot deployment to a remote and/or extreme
environment such as Mars may have robots necessarily far apart and hence
unable to communicate high volumes of data easily until they are in proximity
again. In this case, a decentralized communication architecture could be
advantageous, because long journeys will be necessary back and forth to a
central hub robot or ‘mothership’. A ‘sparse swarm’ configuration [21] could
be the logical choice in that case; in exploration, it could employ some form
of ‘Swarm SLAM’ [22]. This would entail each robot building its own map of the
environment and then periodically sharing it with other robots; yet
appropriate, distributed data sharing strategies are still in development for
swarm SLAM, both among robots and relaying back to human users [22]. We
consider a data-sharing strategy where robots intermittently rendezvous to
share their data, to safeguard and enlarge the map they have built.

##  III Methods

We present the Methods in two parts, A and B: for the communication-
constrained exploration strategies and electrostatic anomaly characterization
approaches, respectively. We use a simulator based on the Python Mesa library
[23], an agent-based modelling (ABM) framework with a grid-based environment.
Our custom-designed agents (robots) and models are integrated into Mesa. This
allows for flexibility in testing different scenarios and algorithms.

###  III-A Communication-Constrained Exploration

In these experiments, we evaluate two approaches on two different maps, one
with inaccessible terrain features and another with flat, featureless terrain.
Simulations will be carried by a number of robots (from 2 to 5, representing a
realistic mission payload) and different values for the
‘$\mathrm{connectionTime}$’ threshold (see Algo. 1 later). These trials aim to
assess the speed of environment exploration and to monitor the amount of
unique data in the system, which indicates potential data loss if a robot is
lost. Each configuration is simulated 20 times.

####  III-A1 Environment Description

A Mars map is generated using random noise to create a 100$\times$100 cell
world with a hazard level, that ranges from 1–5, whereby 5 is the maximum
hazard level. One of the worlds used in the simulations can be seen in Figure
3, where more red cells represent a higher hazard level. The other baseline
map is the same 100$\times$100 however there are no hazard levels defined. The
scale of the environment is considered to be 10 km $\times$ 10 km to have a
realistic limitation of communication range.

Figure 3: Simulated Martian terrain map with rugged and impassable features

For each map, two different communication systems are tested. These have the
same exploration strategy for robots based on a greedy approach. The algorithm
works as follows: at each step, a robot will look at its known map of cells,
find any unknown cells bordering those, choose a random one to move to, and
generate a path using the A* algorithm. While the robot is moving it has a
radius around it that will “see” cells. Every time a new cell is “seen” the
robot gains data, which is defined by:

⬇

self.dataLevel += 1 + random.randint(0, 3)

This algorithm is described in more detail in Algorithm 1.

####  III-A2 Fixed Base Station Communication System

The first system that will be tested and used as a baseline is a fixed base
station, or centralized system. In this simulation, there is a fixed robot at
the center of the map that will not move. Once a robot decides to transfer
data, the robot will travel with the most efficient route back to the fixed
point and have a bi-directional transfer of its data. To determine when to go
back and transfer a variable called ‘$\mathrm{connectionTime}$’ is used. This
is simply a threshold cap on the amount of new data the robot can receive, and
in simulation, the lower the value the more often the robot will go back and
transfer.

####  III-A3 Intermittent Rendezvous Communication System

The second system that will be tested is a rendezvous system between robots, a
decentralized system. To represent realistic communication constraints, each
robot will have two communication systems: one with low bandwidth but high
range, and another with high bandwidth but limited range. When a robot decides
to transfer data it will send a rendezvous request to another robot using its
high-range system. Other robots will respond and use this communication mode
to discuss a meetup location, and once agreed both robots will travel to this
location. While traveling the robots will not do any exploration and only
choose known and safe paths. When both are at this location they will transfer
all the data they have with each other using this high-bandwidth system.
Similar to the fixed base station system, the robot will decide to rendezvous
when hitting a threshold called ‘$\mathrm{connectionTime}$’.

Algorithm 1 Robot Behaviors in Mars Exploration Simulation

1:procedure Explore

2: Identify unknown cells adjacent to known cells

3: Choose a random unknown cell to move to

4: Generate path to chosen cell using A* algorithm

5: while moving do

6: ”See” and collect data from cells within a radius

7:end procedure

8:procedure CommunicateWithBase

9: if $connectionTime$ threshold hit then

10: Calculate most efficient route back to base

11: Move to base and perform bi-directional data transfer

12: end if

13:end procedure

14:procedure InitiateRendezvous

15: if $connectionTime$ threshold hit then

16: Send rendezvous request to another robot

17: Agree on meetup location and move to it

18: Perform data transfer upon meeting

19: end if

20:end procedure

###  III-B Electrostatic Anomaly Characterization

We will use the same simulator framework to assess the capabilities of a
multi-robot system for locating and estimating the electrostatic potential of
Martian dust devils. However as the distances between robots are a lot closer,
they do not use the proposed communication system described earlier and we
assume they have close range communication for our initial analysis.

####  III-B1 Environment Description

Instead of using a varying terrain map, we assume a flat terrain with no
hazardous zones, to remove any kind of limits on the movement behavior of the
robots, which might affect baseline results. The size of the map is
100$\times$100, where one cell is equal to one meter. This scale was chosen to
make feasible the detection of dust devils of a similar size to Earth-bound
measurements of dust devils. As there have not been any electrostatic
measurements of dust devils on Mars, we use representative numbers from dust
devils on Earth [12]. To generate the electrostatic fields on this grid world,
we use the inverse square law $\frac{1}{r^{2}}$ to determine the field
propagation in the grid world.

To evaluate how well the system works, we will assume that all robots are
within communication range and will not need to rendezvous with each other as
in Section IIIA. The simulation will be run multiple times whereby the dust
devil is placed in a random position in the grid. Robots will also be placed
at fixed, equidistant points across the map, dependent on how many robots are
used: Fig. 4 shows an example of the simulation environment. During the
simulation, each robot uses a predictive algorithm called
‘$\mathrm{extended\\_gradients}$’ to determine the location and strength of
the dust devil. Two different setups are evaluated: one where electrostatic
measurements are shared between robots and one where data is not shared. We
assume that sharing data will allow for greater accuracy of the predictions as
the robots will have different points of view of the dust devil. However, this
could come at a cost of longer processing time and potential error-prone
predictions. To accurately assess the performance of these different
configurations, we run each simulation 20 times with both two robots and five
robots, placing the dust devil in a random location for each run. Our
evaluation will focus on two key metrics: the error between the predicted and
actual locations (distance error) and the precision of the strength estimation
(strength error). This will allow us to identify the relative performance of
the two systems behave and whether it might be feasible to use a multi-robot
system for this application.

Figure 4: An example of initial conditions for an electrostatic anomaly
experiment. The colour of the grid represents the electrostatic environment,
and red crosses the robots.

####  III-B2 Dust Devil Locating Algorithm

The algorithm that will be used to predict both the location and the strength
of a dust devil is called ‘$\mathrm{extended\\_gradients}$’. As the robot
travels through the grid world, it records the electrostatic field at each
cell it visits. This data can then be used to create a gradient field, which
is represented as a vector at each cell, with a direction and magnitude where
a smaller gradient signifies a small change in the electric field. As the
robot visits more cells it gains more electrostatic gradient information. It
uses the gradient vector direction component to decide its next movement. To
ensure there is a mix of exploration of the grid world, each robot has a
strategy where there is a 20% chance of moving randomly or moving towards the
gradient direction with a 45-degree deviation, which better allows for the
identification of novel gradient information.

At each time step the robot uses this gradient field to predict the location
and the strength of the dust devil. The full algorithm can be seen explained
in the flowchart shown in Fig. 5. There are four main parts (Algorithms 2–5):

  * •

`Aggregate and Sort Gradients`: This part of the algorithm is only run when
the robots are sharing data. It collects the gradients from all other robots
in the simulation and filters them with the top 5 strongest magnitudes, this
then gets added to the focal robot’s gradients.

  * •

`Create Extended Gradient Lines`: This part of the algorithm takes all the
gradients collected and firstly, normalises them and then, extends them by a
scaling factor and the reverse of the inverse square law. This creates lines
from the cell location to the predicted endpoint.

  * •

`Cluster line endpoints with DBSCAN`: This uses a clustering algorithm called
DBSCAN [24] to cluster all line endpoints together .

  * •

`Estimate Source Strength`: Here we take the top 20 most recent electrostatic
measurements (either from one robot or the shared measurements) and calculate
the distance between the location of the point readings and the predicted
location. This is then used with the inverse square law to give an estimate of
the dust devil’s strength. Some filtering is done by using electrostatic
measurements that have low magnitude as this means the measurement is far away
from the source allowing for some error in the predictions.

Pseudo-code of each of these parts can be found in Section 5

Figure 5: Flowchart of the ‘$\mathrm{extended\\_gradients}$’ algorithm.
Algorithm 2 Aggregate and Sort Gradients

1:Initialize $aggregatedInfo$ with own gradients

2:Initialize $temp$ as an empty list

3:for each robot in the model excluding self do

4: Add robot’s gradients to $temp$

5:end for

6:Sort $temp$ by the last element in descending order of magnitude

7:$N\leftarrow 5$ $\triangleright$ Top items to select

8:Select top $N$ items from $temp$ into $topNAggregatedInfo$

9:Extend $aggregatedInfo$ with $topNAggregatedInfo$

Algorithm 3 Create Extended Gradient Lines

1:Initialize $lines$ as an empty list

2:$scalingFactor\leftarrow 5$

3:for each $(x,y,dx,dy,magnitude)$ in $aggregatedInfo$ do

4: $dx\leftarrow-dx$

5: $dy\leftarrow-dy$

6: $gradNorm\leftarrow\sqrt{dx^{2}+dy^{2}}$

7: if $gradNorm\neq 0$ then

8: Normalize $(dx,dy)$ by $gradNorm$

9: $lineLength\leftarrow scalingFactor\cdot\sqrt{\frac{1}{magnitude+1e-6}}$

10: $endX\leftarrow x+dx\cdot lineLength$

11: $endY\leftarrow y+dy\cdot lineLength$

12: Append $((x,y),(endX,endY))$ to $lines$

13: end if

14:end for

Algorithm 4 Cluster line endpoints with DBSCAN

1:$endPoints\leftarrow$ Extract end points from $lines$

2:if $length(endPoints)>0$ then

3: Perform DBSCAN clustering on $endPoints$

4: Calculate $clusterCenters$ as the mean of points in each cluster

5:end if

Algorithm 5 Estimate Source Strength

1:Convert $predictedSource$ to tuple

2:Extract and sort top 20 recent measurements by step

3:Initialize $sourceStrengthEstimates$ as empty

4:for each measurement in sorted recent measurements do

5: Calculate distance $r$ and strength $S$ from $predictedSource$

6: if $r>0$ then

7: Add $S$ to $sourceStrengthEstimates$

8: end if

9:end for

10:$trimFraction\leftarrow 0.2$

11:if not empty $sourceStrengthEstimates$ then

12: $finalEstimate\leftarrow$ Trimmed mean of $sourceStrengthEstimates$

13:else

14: $finalEstimate\leftarrow$ None

15:end if

Figure 6: A comparison of the communication-constrained exploration
strategies: average unique data over time on flat terrain map with minimum and
maximum connectionTime settings. The exploration time is longer and the unique
(most at-risk) data is higher for the centralized (base station) communication
strategy. Figure 7: Average unique data on rugged terrain map with minimum and
maximum connectionTime settings. Results are similar overall as for the flat
terrain map, with a slightly slower traversal, less evident in the rendezvous
strategy. There is slightly less data collected because parts of the map are
inaccessible (83% is accessible).

##  IV Results

###  IV-A Communication-Constrained Exploration

In Figs. 6 and 7, the final results of the simulation trials can be seen. Each
plot shows the different maps used, one with rugged terrain and one with flat
terrain, where the top row shows the centralized and decentralized systems
with a low $\mathrm{connectionTime}$, and the bottom row shows it with a high
$\mathrm{connectionTime}$. The y-axis shows the average amount of unique data
each robot has, whereby the higher the value the more risk there is and more
data would be removed if a robot is lost. The $x$-axis shows the amount of
steps or time it took to fully explore the area. Note that in the rugged
terrain map, approximately 85% of the map can be explored as some cells are
too hazardous for the robots to visit.

From the plots presented, we can get some interesting insights. When looking
at the results from the flat terrain map, Fig. 6, shows that the fixed base
station system requires a significantly longer time to complete the map
exploration when only two robots are present. When we add more robots there is
a decrease in the time to explore. If we look at the unique data it is quite
high, but also decreases with more robots. If we compare this with the
rendezvous system, it demonstrates a very short exploration time. It also has
a considerably lower amount of unique data, suggesting a lower risk of data
loss from prior exploration.

When we consider the $\mathrm{connectionTime}$ variable, with longer times the
fixed base station shows a shorter exploration time, however still not as much
as using the rendezvous system. When we switch and look at the rugged terrain
map in Fig. 7, the same results can be seen, indicating that the terrain does
not influence the exploration time and unique data when comparing the two
systems.

The plots for several results show a plateau, or in some cases, even an
increase in unique data. This happens around the midpoint of the exploration.
This may result from a stage during exploration where the robots encounter
many more new cells. As the exploration goes on and fewer unvisited cells are
left, this plateau decreases again.

One of the reasons for the long exploration time in the fixed base station
system with low connection time is that the robots are spending a long time
going back and forth to the base station and less time exploring the area.
This is not the case in the rendezvous system as the travel time spent going
to meet another robot is a lot less as the two robots meeting up will find a
midpoint between them. The efficiency of the robots during exploration was
assessed over multiple trials: this was done by comparing the ratio of
movements made to the number of environment cells discovered. The results of
these trials can be seen in Table I. It shows that the pairwise communication
strategy is more efficient than the base station.

Pairwise communication | 1 Agent | 2 Agents | 3 Agents | 4 Agents | 5 Agents
---|---|---|---|---|---
Movement/ Cell seen average | 0.25 | 0.22 | 0.19 | 0.20 | 0.18
Movement/ Cell seen std_dev | 0.012 | 0.008 | 0.018 | 0.020 | 0.014
Base station communication | 1 Agent | 2 Agents | 3 Agents | 4 Agents | 5 Agents
Movement/ Cell seen average | 0.67 | 0.40 | 0.36 | 0.29 | 0.27
Movement/ Cell seen std_dev | 0.015 | 0.029 | 0.018 | 0.027 | 0.030

TABLE I: Exploration efficiency results of pairwise and base station
communication strategies.

###  IV-B Electrostatic Anomaly Characterization

(a) Configuration at time step 0

(b) Time step 50

(c) Time step 99

Figure 8: Single simulation run of ‘$\mathrm{extended\\_gradients}$’ algorithm
over time (2 robots).

(a) 20 runs showing distance error of prediction with 2 robots.

(b) 20 runs showing strength error of prediction with 2 robots.

(c) 20 runs showing distance error of prediction with 5 robots.

(d) 20 runs showing strength error of prediction with 5 robots.

Figure 9: Plots of multiple simulation runs with both 2 and 5 robots. Error
bands show interquartile range.

In Figs. 8(a), 8(b) and 8(c) a single run can be seen for both two robots over
time. We have put a cap on the step count to 100 as anything after this would
mean the robot will have more than enough time to traverse the grid world and
move on top of the dust devil. The plots show the movements of the robots over
time with the average predictions that they make.

In Figure 9(a),9(b), 9(c), and 9(d) a full set of simulation runs can be seen.
These are done over 20 runs with both 2 robots and 5 robots. Figures 9(a) and
9(c) show the distance error, while Figures 9(b) and 9(d) show the strength
error. In all cases, the blue lines show simulations where data is shared
between robots and the red where robots do not use any shared information. All
of these graphs show the interquartile range as the error bands.

##  V Discussion and Conclusion

###  V-A Communication-Constrained Exploration

We considered two communication strategies for a multi-robot system in a
remote environment without preexisting communications infrastructure. We
particularly consider the multi-robot SLAM case where large amounts of terrain
data might need to be shared to build a shared map of an unfamiliar
environment as it is explored. If robots have to be in physical proximity to
share data, our two strategies involving a fixed base station (centralized)
and a pairwise rendezvous system (decentralized) would both be relevant. We
found that the time for exploration for the rendezvous system consistently
outperforms the fixed base station system, regardless of the number of robots
or the structure of the terrain. Looking at the amount of unique data (located
in only a single robot) in each system it is also lower in the rendezvous
system suggesting it has lower risk of costly data loss on robot failure.

Clearly, the choice of communication strategies plays a guiding role in the
efficiency of multi-robot exploration. Nevertheless, a decentralized swarm
SLAM approach has its challenges [22], and in some cases, there may be good
reasons to maintain a central base station with particular communication
capabilities: to a relay satellite, for example. In that case, an approach
combining elements of both centralization and decentralization could be an
attractive choice. Future research could test the rendezvous strategy with
real robots, for example coordinating meetings via LoRa and then sharing data
via WiFi. We will also employ realistic multi-robot simulations to develop our
communication strategies to better consider the impact of rugged terrain on
communication performance.

###  V-B Electrostatic Anomaly Characterization

Our investigation into dust devil location and strength prediction has
provided insights into the capabilities and limitations of using a multi-robot
system for this task. Using the simulation framework, we explored various
scenarios involving different dust devil locations with different numbers of
robots and whether they were sharing information or not. The core of our
experiments was the ‘$\mathrm{extended\\_gradients}$’ algorithm, which is
designed to predict the location and strength of dust devils based on
electrostatic measurements. A notable finding from our study is the impact of
information sharing between robots: this improved the speed and accuracy of
finding the location and strength of the electrostatic anomaly. However, in
the early parts of the simulation, shared information led to a larger error in
predicting the location of the source, which in turn also created a large
error in the strength prediction. Having done simulations for both a 2-robot
setup and a 5-robot setup, we found larger group sizes extended the benefit of
a faster prediction, but also a larger inaccuracy at the start of the
simulation. This means there needs to be a balance between the speed of
locating the source while also managing the errors at the start. This initial
error highlights the challenges of using multi-robot systems for this kind of
task, and more research needs to be done to refine the algorithm.

###  V-C Conclusion and future works

This research shows the importance of selecting an appropriate communication
strategy in multi-robot systems for efficient exploration, whereby the
decentralized, pairwise rendezvous system, shows particular promise due to its
superior performance and lower risk profile. However, the use of the
centralized system could offer a balanced solution, in unique scenarios
requiring a specific communication capability. For electrostatic anomaly
characterization, the ‘$\mathrm{extended\\_gradients}$’ algorithm offers a
promising approach. We will investigate cases where an area contains more than
one anomaly, which is conceivable given an estimate of one dust devil per
square km per sol [25]. While the approach works well with a static (or
possibly, slow-moving) dust devil, real-world data shows that dust devils on
Earth often move at significant speed (5 to 10 ms-1, [12]). The algorithm will
also need development for such cases.

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