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Towards Reproducible Evaluations for Flying Drone Controllers in Virtual Environments

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Abstract and Figures

Research attention on natural user interfaces (NUIs) for drone flights are rising. Nevertheless, NUIs are highly diversified , and primarily evaluated by different physical environments leading to hard-to-compare performance between such solutions. We propose a virtual environment, namely VRFlightSim, enabling comparative evaluations with enriched drone flight details to address this issue. We first replicated a state-of-the-art (SOTA) interface and designed two tasks (crossing and pointing) in our virtual environment. Then, two user studies with 13 participants demonstrate the necessity of VRFlightSim and further highlight the potential of open-data interface designs.
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Towards Reproducible Evaluations for Flying Drone
Controllers in Virtual Environments
Zheng Li
Fudan University
Shanghai, China
Yiming Huang, Yui-Pan Yau, Pan Hui
The Hong Kong University of Science and Technology
HKSAR, China
Lik-Hang Lee*
Daejeon, South Korea
Abstract—Research attention on natural user interfaces (NUIs)
for drone flights are rising. Nevertheless, NUIs are highly diversi-
fied, and primarily evaluated by different physical environments
leading to hard-to-compare performance between such solutions.
We propose a virtual environment, namely VRFlightSim, en-
abling comparative evaluations with enriched drone flight details
to address this issue. We first replicated a state-of-the-art (SOTA)
interface and designed two tasks (crossing and pointing) in our
virtual environment. Then, two user studies with 13 participants
demonstrate the necessity of VRFlightSim and further highlight
the potential of open-data interface designs.
Index Terms—Human-Drone Interaction, Methodologies, Vir-
tual Environments, The Fitts’s Law, Metaverse.
In recent years, quadcopter-style drones have been widely
available due to their highly portable features, and users can
control their drones with dedicated controllers. Drones can
serve as a representative in our physical world, and users with
augmented reality headsets, by leveraging the drone views,
can teleport to such environments [1] for various industrial and
commercial purposes [2] [3]. Although the two-handed remote
control transmitters (RCT) dominate the way of drone flights,
one-handed controllers, e.g., commercial products: FT Aviator
and research prototypes: DroneCTRL [4], allows users to enjoy
higher mobility by reserving a spare hand for other tasks,
e.g., holding a handrail in a train. Furthermore, researchers
have explored alternative solutions to pursue more natural
human-drone interaction [5] that promotes invisible interfaces
inherited in our bodies, e.g., gaze-driven drone flights [6]
and ring-form addendum [7]. However, the proliferation of
flying drone controllers leads to difficult comparisons between
research prototypes. Additionally, the performance of such
controller prototypes is subject to flying tasks in real-world
environments that include a collection of external factors, such
as physical obstacles, and weather conditions (e.g., windy or
rainy days), More notably, replicating research prototypes and
flying tasks are usually time-consuming and expensive.
*Lik-Hang Lee is the corresponding author. Z. Li and Y. Huang were interns
during the study of this work with KAIST. The primary affiliation of Pan Hui
is The Hong Kong University of Science and Technology (Guangzhou), China.
He is also affiliate with the University of Helsinki, Finland. Acknowledgment:
This work has been supported by the Academy of Finland, under Grant
319669, and Grant 325570.
Therefore, we leverage virtual environments (Microsoft Air-
sim) to develop an evaluation tool that offers comprehensive
profiles of controller prototypes. Our virtual environment
enables interaction designers to create new tasks (e.g., flying
routes, checkpoints, and obstacles), and hence records both the
performance of users and flying drones. In our current tool,
the performance refers to users’ task completion time, as well
as drones’ velocity, acceleration, jerk, and flying trajectory. A
newly designed controller can be evaluated by a task in which
a flying drone reaches checkpoints in a high-resolution virtual
world. It is worth noting that constraints of task design appear
with traditional measurements in physical worlds (Figure 1),
including short and simple task [7], manual measurements [8],
room-size setup, short battery life and hence long recharging
time [1]. In contrast, conducting evaluations in virtual environ-
ments can potentially get rid of the aforementioned constraints,
hence facilitating the evaluation progress.
(a) Crossing task (b) Pointing task
Fig. 1: Drone interaction experimental tasks in our physical
lab that are space-occupying and time-consuming.
To illustrate the prominent features of our evaluation tool,
we implemented a prototypical interface of a SOTA con-
troller [7], driven by one-handed and one-thumb operations,
and further compared the SOTA interface with a baseline that
emulates a two-handed tangible controller. Our evaluation tool
reveals new insights into both the two-handed and one-handed
controllers, in which two-handed controller remains superior
to the SOTA method in complicated scenarios. Interaction
designers can utilize this design cue to rationalize the practical
use cases in physical environments. To conclude, this paper
primarily contributes to an evaluation tool for drone controllers
with comprehensive metrics that offers comparable results
between controller prototypes, facilitating result replicability
and open-data research.
This section highlights the related work of aircraft control,
simulation, and virtual environments. As one of the most
widely accessible robotic crafts to the public, drones have
attracted many researchers’ attention. Different controllers
are developed to enhance human-drone-interaction scenarios,
including the traditional hand movement controller [9], eye-
gaze-based controller [10], voice-based-controller [11], etc.
However, all of these controllers should be tested in a stan-
dardized environment, which takes great effort to deploy.
Experiments for robotic deployment in physical environ-
ments calls for resources of time and space. Thus, simulation
platforms have been built, allowing easy-to-access, faster
and low-cost evaluations, for instance, behavioral dynamics
between human avatars and robotics [12], simulation plat-
forms enable emulations of 3D-printed robots [13], an inter-
active robotic arm for housework [14], social robot naviga-
tion [15], etc. Lately, evaluation of commercially-available ser-
vice robots moved to online platforms for scalable evaluations.
On the other hand, the emerging AR/VR in recent years can
serve as an alternative to simulation. The virtual environment
can go beyond physical constraints. For instance, AR can
help architects to understand the user perception of movable
walls [16], while building such movable walls in real buildings
are usually impractical. Similarly, mixed reality robots can
induce people to empathize with bad incidents [17]. VR
environments can deliver a testing ground of risky operations
in which injury and casualty are not affordable, for instance,
virtual driving and risky driving events [18], and virtual
jumping between intervals to observe users’ locomotion [19].
We acknowledge that numerous open-source drone flight
simulators exist, e.g., autonomous drones [20] and training
modules for human operators [21]. However, a recent sur-
vey [22] highlights the difficulty of evaluating one flight
controller, i.e., substantially high development time and testing
efforts. Also, our work uniquely leverages virtual environ-
ments to resolve the lack of standardization of flight controller
architectures and hence, drone controllers’ evaluation.
Our system aims to provide a ubiquitous and open-data
environment for human-drone interaction research, with a
focus on the designs of drone controllers. We implement
the system with four significant modules in order to create
a standardized environment for conducting the human-drone
experiment systematically (Figure 2). As mentioned, one of the
limitations of drone experiments is the difficulty of efficiently
comparing results among the research community. To resolve
this problem, we aggregate the data from Input, Pathway, and
Environment modules into experiment logs for a reproducible
and trackable experiment. Another challenge is the external
factors of drone experiments, for instance, the drone models,
drone sizes and speeds, and the physical environment: wind
speed and weather. For manipulating the external contribu-
tors, we implement the Environment module and Drone state
manager indirectly linked to the Airsim platform [23], based
on the UNREAL engine. Therefore, we can standardize all
the experimental factors and perform human-drone interaction
experiments in an identified yet united environment.
A. System Components
Utilizing the Airsim binary (editable UNREAL project), we
implement the experimental evaluation system (Figure 2) with
extra programmed modules for manual drone control, network
communication protocols, and input interfaces. 1) Manually
drone control. The extra functionality for controller interface
support. 2) network communication protocols. Information
exchanging service between input devices and evaluation
platform. 3) Input interfaces. GUIs for adjusting experiment
settings and filling in personal information. We create a
portable released package based on the original Airsim block
binary by removing most redundant assets and adding the
necessary experiment objects. With a combination of code
and binary, we construct an experimental platform without
re-compiling the Airsim source code and re-installing the
UNREAL engine. Besides the code and binary, the system also
includes the 3D asset files for two primary operations: pointing
and crossing [8] [24]. The checker asset is a plate for the
pointing operation, and the crossing asset is a door frame for
the crossing operation. A drone path exists for each pointing
and crossing operation, including start points, endpoints, and
checkpoints. The nine major programmed modules are as
Controller-Keyboard: Directing drone’s movements
with the controllers (e.g., keyboards/touchscreens).
Drone Environment Parameter Controller: Storing the
environment setting.
Drone Environment Setting GUI: User interface for
adjusting the environment setting.
Drone State: Storing the drone state setting.
Experiment Logger: Logging experiment results.
Main Program: The executable file for experiments that
initializes the whole experiment.
Path Generator: Generating the experimental task
drone flying path.
Path Manager: storing the setting of the experiment path.
UDP Server: UDP communication between devices.
B. Component interaction
These modules of the experiment platform are independent
and only exchange the state and setting information (Figure
2) between each other. The executed Main Program is the
container for the components, including Drone State, Path
Manager, UDP Server, Controller, Environment Parameter
Controller, and Drone Environment Setting GUI. The drone
state module passes the Airsim platform client through the
MAIN to other components. The drone state module also
provides the drone velocity, angle, acceleration, position, and
events that record the drone collision. The experiment data
Fig. 2: System architecture
flows from the Drone State component to the Experiment
Logger, Path Manager, and Drone Environment Parameter
The Environment Setting GUI passes the participant’s mes-
sage and the input configuration to the Environment Param-
eter Controller and simultaneously the “Start” and “End”
commands to the UDP Server components. The drone state
message and the experiment event will occur when the Drone
State takes the corresponding command(s) for poi from the
UDP server. For example, hitting the door frame and plate
checker notifies the Path Manager, then the Path Generator
changes the state of the checker. Meanwhile, the Drone State
component will call the APIs of the Experiment Logger, thus
logging the event(s) and the Drone State message(s).
To understand the efficacy of VRFlightSim for evaluating
controllers with Human-Drone interaction, we implemented
two common drone operations, namely drone pointing and
crossing operations on the Microsoft Airsim platform (version:
1.5.0.) [23] with UNREAL Engine (version: 4.25.4) The
pointing operation refers to a drone flying from one location
to another, and both start and end points require the drone to
take off and land, respectively [24]. The crossing operation
refers to a drone flying through a door frame in mid-air [8],
which can be regarded as a subset of the pointing operation.
A. Participants and Apparatus
We recruited 13 participants (19 28 years old; 11 male
and 2 female) to conduct the drone interaction experiment
with VRFligthSim. All participants, who are experienced
smartphone users, are students from our university campus.
Only two of them have experience with drone flights, while
the others are inexperienced, i.e., first-time drone users. The
participation is based on informed consent and is truly volun-
tary. The experimental procedures comply with the regulations
of GDPR and the IRB of our universities. All user-generated
data were de-identified and password-protected, and will be
deleted after the project completion.
We employed an iPhone 7 that allows us to replicate the
SOTA control interface, driven by the 3-D touch function (i.e.,
force detection), that resulted in a ring-form controller [7], and
a baseline that emulates a tangible two-button controller (see
Figure 3). We chose these touchscreen-based controllers as
Fig. 3: Controller interfaces of Two-button (Baseline, left) and
One-handed (SOTA, right) operations on the blue button(s).
typical methods, as smartphones support many drone com-
modities in recent years. For the SOTA control interface,
the drone speed is determined by the force exertion level of
the user, given by the force sensor on iPhone 7 [7]. Drone
speeds for the baseline interface is derived from the distance
between the touch point and the mid-point of the button. The
smartphone connects to a computer running the VRFlightSim
via WIFI, which sends the RC command for controlling the
simulated drone by the UDP client programmed in Swift
(IOS). Also, the received RC commands are handled by a UDP
server programmed in Python on the computer. The received
RC commands and the corresponding actions of the drone will
first go through the API of VRFlightSIM (written in Python),
and then be processed by the API of Airsim, based on the
UNREAL engine.
B. Experimental Setup in the Virtual Environment
We re-implement the settings of pointing and crossing tasks,
based on prior works [24] [8] in our virtual worlds. The user
perspective in the Airsim environment is set as a 90° field-
of-view and 165cm height that emulates the normal vision of
a person with the average height in our population. As such,
the position of the camera is fixed at the starting point (0, 0,
1.65). The weather of the experimental environment defaults as
“sunshine” initially, which corresponds to a satisfactory level
of illumination, and the wind level of the experiment block
is zero (i.e., turn off). We did our utmost to minimize the
distraction from the background and the checkpoints. Hence,
as shown in Figure 4, we employ a plain colour background,
in addition to two objects (a triangular pyramid and a cube) to
assist the participants in recognizing the directions. Also, the
experimental checkers (i.e., checkpoints) are built using the
basic box asset in the UNREAL engine with the modification
in size and colour. The pointing checker is a red-colour square
plate, and the crossing checker is a red-colour and squared
door frame perpendicular to the ground with blank space in
the door frame facing the participants. The middle part of the
crossing checker is a piece of transparent box-sized material
that serves as the trigger for the mission’s completion. Finally,
the drone model in this experiment is a quadcopter named
“Parrot Mambo Fly”, sized at 0.18 x 0.18 (m), that replicates
the experimental setting for casual drone flights [7].
(a) Crossing task (b) Pointing task
Fig. 4: Experiments Tasks for Drone Flights in VRFlightSim.
C. Task and Procedures
For each operation type, either crossing or pointing, the
participants ran five trials with TWO types of controllers (two-
button baseline and one-handed, under the virtual tasks having
THREE different sizes of experimental checker (Landing areas
for pointing: .4m x .4m, .7m x .7m, and 1.1m x 1.1m; Mid-
air door frames for the crossing: .3m x .3m, .4m x .4m, and
.5m x .5m) and TWO flying distances (Pointing: 2m and 4m;
Crossing: 2.5m and 3,5m). As a result, the experiment setting
is regarded as a 2 controllers * 3 difficulties * 2 distances,
for two operations with five repetitions. The five-trial runs are
sufficient, as the extension of the trial number will lead to
negative performance on the participants, while the tiredness
and boredom of the participant lead to users’ burnt out. The
randomized orders of tasks and participants follow [25] to
reduce the asymmetric skill transfers, and hence the threat
of internal invalidity, i.e., carry-over effects.
At the beginning of the test, we asked every participant
to watch a video demonstrating the flight task performed
by an experienced pilot for both the pointing and crossing
tasks. Afterward, they made some trials, no longer than five
minutes to get basic ideas about the interfaces and the virtual
environments. The five-minute session is sufficient for the
participants to get familiar with the basic control of the drone
and understand the task requirement of the experiment. Next,
the participants ran the formal experimental trials. They had
been told to finish the task as fast and as accurately as possible.
We require the participants to press the start button on
the iPhone interface, and subsequently drive the drone to
the target with the minimum time they can achieve. After
reaching the target, the participants will get a completion
message from the PC display, and the participants require to
press the stop button to end the current trial. After ending a
trial, a file saving message for the RC commands recording
on the iPhone is shown, and the researchers saved this file
to a directory with their name and the testing conditions. If
a trial flight fails due to the mistaken operations caused by
the participants or the program crash, we simply neglect the
experiment result(s) and reran another trial. We collected all
IMU data, including the timestamp with the coordinate(s),
velocity and acceleration values of all directions, and collision
events with the coordinate of the contact point between the
drone and collided objects. The Airsim API supports all the
aforementioned data collection pipelines at a granularity of
every 10ms. The collected data were analyzed for generating
the trajectories of drone flights, the completion times of all
experimental conditions, as well as the illustration of the
predictive models of linear regression based on the Fitts’s
Law [8] [24].
This section first highlights the results of crossing operations
and subsequently pointing operations with all the metrics.
All the participants’ performance with two kinds of con-
trollers under different operations are parsed by three high-
level perspectives: completion time, drone states (Velocity,
Acceleration, and Jerk), as well as trajectory areas. We depict
all the statistical analyses (two-way ANOVA), in Tables I
and II, for the crossing and pointing operations, respectively.
All the graph plots in this section follow the difficulty
index (i.e., ID on the x-axis), inspired by Fitts’s Law [8].
The original definition of Fitts’s law states that the amount
of completion time it takes for the device moving from the
current position to the target is highly related to the width
of the target(W)and the distance between two points(D).
To be more precise, the completion time is proportional to
log(2D /W )(i.e., Fitts’s index of difficulty), which has been
commonly used to determine whether a controller is easy to
handle in the scenario of human-computer interaction.
We collect all the experiment results of crossing & pointing
tasks, and draw the linear relationship of dependent variables
(e.g., time) in each trial and review the performance in differ-
ent difficulties. Note that the larger the proportional coefficient
of the ID is, the more challenging users could handle the drone
flight as the tasks’ difficulty grows.
A. Crossing
Different from the pointing task, the crossing task is a sub-
scenario where the landing process is shaved off. Since it only
focuses on the flying procedure of targeting in the mid-air, we
could analyze this stage more precisely than the pointing task
consisting of take-off and landing. In this section, we analyze
the results of crossing tasks in order to better understand the
properties of two types of controllers.
1) Completion Time: We draw the linear regression be-
tween the difficulty index and completion time of all the
crossing tasks, according to Fitt’s Law. Figure 5a shows that
the SOTA one-handed controller consumes less completion
time in all the crossing tasks than the two-button controller.
This result shares the same trend as [7]. Note that the slope
of the two-button controller is larger than the one-handed
controller, which indicates that the two-button controller is
more sensitive to task difficulty. In contrast, even if the index
of difficulty grows from 2.6 to more than 3.6, the one-handed
controller’s completion time slightly increases by less than one
second. With VRFlightSIM, we could compare the completion
time of different controllers, and observe the controllers’
performance patterns on different tasks.
2) Drone States: VRFlightSIM can capture the real-time
drone states in the experimental procedures, including drone
positions, velocity, acceleration, etc. By analyzing the drone
Metric Completion Time Velocity Acceleration Jerk Trajectory Area
Factor p-value F-statistics p-value F-statistics p-value F-statistics p-value F-statistics p-value F-statistics
Mode 5.80E-08 30.0726 1.39E-38 205.0140 5.28E-50 285.8583 4.82E-04 12.3645 6.08E-11 44.7612
ID 1.41E-08 9.2844 3.07E-03 4.0534 0.0931 2.0031 4.65E-08 10.3923 2.59E-07 8.0527
Mode * ID 0.2955 1.2251 0.1808 1.5715 0.5801 0.7177 0.8200 0.3841 0.4284 0.9816
TABLE I: Two-way ANOVA analysis of Crossing Tasks: DoF = (1,5). All the Controller Interfaces (Mode) and Index of
Difficulties (ID) passed the threshold of p-value<0.05, except ID for acceleration (0.093).
(a) Completion Time of Crossing
(b) Violinplot of Velocity (c) Violinplot of Acceleration (d) Boxplot of Jerk
Fig. 5: Drone States of Crossing Task with Different IDs.
states of each task, we can learn more details about the
participant’s performance and user behaviors with different
controllers. Specifically, Figure 5 depicts the drone states from
three perspectives: velocity, acceleration, and jerk. First, in
terms of velocity, Figure 5b shows the kernel estimation of
the velocity distribution of two controllers on different tasks.
The one-handed controller has a peak at higher flying speeds
than the counterpart of the two-button controller, which means
the participants tend to fly the drone faster with the one-handed
controller. This corresponds to a lower task completion time.
Second, in terms of acceleration, Figure 5c offers the kernel
estimation of real-time acceleration, where the one-handed
controller has a higher peak value as well. Together with the
velocity distribution, we can conclude that users are more
confident at handling a one-handed controller for crossing
operations, and meanwhile chasing a high speed to complete
the task faster. Third, in terms of jerk values (the first time
derivative of acceleration, which has been used to evaluate the
comfort level of vehicles), Figure 5d is the boxplot of the real-
time jerk of the flying drone. We find the potential risk in the
one-handed controller, because of its higher jerk value than the
two-button controller among all the tasks. A high jerk value
means that the drone flew unstably with severe speed changes.
Since people may use drones for photography purposes, the
one-handed controller may add more disturbance, while the
two-button controller might serve as a better choice because
of the jerk value at a modest level. By analyzing the real-time
drone states captured by VRFlightSIM, we can explore the
characteristics of different controllers with in-depth analytics.
3) Trajectory Patterns: Besides the task completion time
and drone states, researchers might also be interested in
drone’s flying trajectory. Since VRFlightSIM can collect the
real-time position of the drone, we can conveniently draw
the flying trajectory. Figure 6 illustrates all the trajectories
Fig. 6: Drone’s Trajectories on Crossing Tasks. The number
near the rectangle denotes its area.
of crossing tasks by different participants. We can compare
the trajectory patterns among the two controllers. For almost
all the tasks, the one-handed controller’s trajectory is more
dispersed with many longer twisted lines. To be more precise,
we compute the standard deviation on each axis. The trajectory
area corresponds to three times the standard deviation from the
center (the blue/orange-colored squares).The smaller the tra-
jectory area is, the more stably participants could complete the
tasks with the corresponding controller. We note that the two-
button controller has a smaller trajectory area than one-handed
controller. The finding indicates that the participants could
control the drone in a more normative scope without excessive
manipulation, for example, overflying the drone away from the
target. This insight is not captured by recent work [7] because
the previous work only deploy their experiments in a physical
lab, where the drone positions are hardly being collected. With
the convenient data capture by VRFlightSIM, we notice some
weaknesses of the SOTA one-handed controller.
B. Pointing
1) Completion Time: As shown in Figure 7a, it can be
clearly seen that the SOTA one-handed controller, in general,
takes longer times of completing the same pointing task than
the two-button controller. Since the red line holds a higher
slope (one-handed) than the blue line (two-button), it could
be seen that the one-handed controller is more sensitive to
tasks’ difficulties. Note that this result is opposite to that
of the crossing task. We attribute this phenomenon to the
extra landing procedure of the pointing task, where the two-
button controller surpasses the one-handed controller. With
VRFlightSim, we can easily conclude that the traditional
method of two-button controllers offers easier access for users
on pointing tasks, especially for the drone landing subprocess.
However, if we examine the performance of individual
participants, VRFlightSim gives a varied design implication of
controller interfaces. We draw the same correlation graphs for
participants with different preferences, in which their preferred
controllers and hence behavior patterns could be mainly di-
vided into three categories: (T1) Two-button-preferred (Figure
7b); (T2) one-handed-preferred (Figure 7c); (T3) Neutral (Fig-
ure 7d). Although provided with the same kinds of controllers
dealt with the same tasks, user’s learning curve is different
from each other. For users of T1, their behavior pattern is the
same as Figure 7a, where the two-button controller always
performs better than the one-handed controller. For users of
T2, the opposite observation happens the completion time of
the two-button controller grows faster with the task difficulty,
which demonstrates the sensitivity of these users to the task
difficulty and the preference for the one-handed controller.
Regarding users of T3, there is no apparent difference
between the two types of controllers, so they acquire the
skills of these controllers to the same extent. It is important
to note that the intersection between the red and blue lines
could imply design cues (More details are available in the next
section. With VRFlightSim, we can easily notice the individual
preference, as well as their learning pattern to decide the
suitable controller design for scenarios of various difficulties.
2) Drone States: Same as the previous analysis on drone
states for the crossing task, we try to study the drone’s velocity,
acceleration and jerk for the pointing task. Figure 8a describes
the violin plot of the drone’s velocity distribution under tasks
with different IDs of task difficulties. Overall, participants tend
to fly the drone faster using the two-button controller, revealing
that they are more confident with the two-button controller for
the sake of high speeds.
About the drone’s acceleration, in Figure 8b, we can see
that, the distribution of acceleration is poly-modal, where the
peak at the zero value means a drone spends much time
flying smoothly or staying still. We notice that the blue plots
have lesser non-zero values among all the tasks, revealing
that a drone receives less movement correction, commanded
by users with the one-handed controller. In particular, we
compute the real-time jerk value and Figure 8c states that,
when handling easy tasks (i.e., when ID value is 1.9), a
drone has less discontinuous acceleration with the one-handed
controller, which means a more stable flying posture. However,
as the task becomes difficult, the two-button controller is more
likely to keep the drone’s stability at a modest level. We could
see that the one-handed controller has its advantage when
handling easier tasks. This trend adds more details on top
of Figure 7a that solely considers the completion time, but
does not capture the drone’s movement consistency that could
impact various drone missions, e.g., photo and video taking.
3) Trajectory Pattern: Similar to the analysis on crossing
tasks, we draw the trajectory of drone movement controlled by
two kinds of controllers, to see whether the drone behaves sta-
bly in each experimental task. Figure 9 illustrates the drone’s
moving trajectories under different pointing tasks, controlled
by one-handed and two-button controllers. For all the six tasks,
the two-button controller could help the participants achieve
a significantly smaller drone trajectory area than the one-
handed controller. This demonstrates that participants might
act more stably when using the two-button controller, requiring
fewer efforts in route correction and fewer unnecessary drone
movements. This result is the same as that of crossing tasks.
However, from a dynamic perspective, we notice some
surprising results. Figures 10a and 10b illustrate the average
trajectory area (take the relative value of the maximum) under
different tasks by trials. As more trials are processed with the
one-handed controller, participants are expected to be more
skilled at handling the drone stably. Also, we anticipated that
the trajectory area would become smaller. The trend was dif-
ferent when the participants ran the trials with the two-button
controller. It can be seen that the trajectory area becomes
more prominent with more and more trials. This phenomenon
might reveal potential evidence of user behaviors. Once the
participants become proficient and confident at handling the
two-button controller, they tend to act more unrestrained and
aggressive in the remaining trials, albeit they have been told to
complete the tasks as stable as possible. With VRFlightSim,
we could quickly notice such vulnerability in the early design
stage, thus providing some advice for the users.
In this paper, we designed and implemented an evalua-
tion methodology with an open-source virtual environment
(Microsoft Airsim), named VRFlightSim, to examine alter-
native input methods (i.e., new controllers) for drone flights.
VRFlightSim aims to improve the ease of collecting drone
performance data, such as completion time, flying trajectory,
Metric Completion Time Velocity Acceleration Jerk Trajectory Area
Factor p-value F-statistics p-value F-statistics p-value F-statistics p-value F-statistics p-value F-statistics
Mode 7.81E-18 77.7359 4.8431E-14 60.3394 0.0015 10.1788 0.0446 4.0557 1.1953E-10 43.0379
ID 5.55E-26 27.8621 1.5873E-47 58.4736 0.0004 4.5486 0.0046 3.4353 3.1615E-34 38.6268
Mode * ID 6.08E-05 5.4555 0.6774 0.6294 0.3330 1.1500 0.0112 2.9981 0.2842 1.2502
TABLE II: Two-way ANOVA analysis of Pointing Tasks: DoF = (1,5). All the Controller Interfaces (Mode) and Index of
Difficulties (ID) passed the threshold of p-value<0.05.
(a) All (b) Two-button-preferred (c) One-handed-preferred (d) Hybrid (Neutral)
Fig. 7: Three User Types for Pointing Task.
(a) Violinplot of Velocity (b) Violinplot of Acceleration (c) Boxplot of Jerk
Fig. 8: Drone States of Pointing Task with Different IDs.
velocity, acceleration, and jerk, with flying diversified tasks,
albeit in virtual environments.
Identifying a large number of potential candidates of input
methods with real-life experiments of drone flights are of-
ten tedious and expensive, while our methodology lowered
the hurdle by offering (1) a preliminary understanding of
flight controllers, and (2) virtual drones and environments as
a comparable yet open testing ground that can potentially
accumulate data from various testers/experiments. We note
that VRFlightSim can screen out the inappropriate candidates,
and work complementary with the real-life experiments, as
a preliminary step in the entire cycle of interaction design.
More importantly, the interaction designers have to conduct
real-life experiments to test the effect of physical form factors
caused by the actual devices, e.g., moving from a smartphone’s
touchscreen to a ring-form device [7].
We further demonstrated the necessity of VRFlightSim with
two flying tasks, namely pointing and crossing. We followed
the proposed control method in [7], and re-implemented two
control methods for a drone. VRFlightSim reveals a new
profile of the one-handed control method driven force-assisted
input. As reported in [7], their tasks show fixed difficulty ID,
e.g., moving towards a direction (e.g., upward) for a 2-meter
distance. In contrast, virtual environments can quantitatively
capture the difficulty of flying tasks. Accordingly, VRFlight-
Sim facilitates the construction of predictive models [25].
Furthermore, as indicated by the Fitts’s Law, the task diffi-
culty allows interaction designers to investigate the robustness
of their proposed input methods in various scenarios, and
further offers insights of mapping the controller designs with
tasks. For instance, the intersection between two lines could
indicate that a controller works better with certain tasks. As
highlighted by the performance time during the pointing task,
the user population with a higher preference to the one-handed
controller (Figure 7c) reflects that the one-handed controller
can outweigh the two-button controller until it encounters
tasks over 3.50 difficulty. Interaction designers with these
cues can define that the highly mobile controller, driven by
one-handed input with force levels, is suitable for easier
tasks. Furthermore, the users with a neutral preference for
both methods (Figure 7d) show the intersection at the task
difficulty index value of 3.00. As such, interaction designers
can narrow down the interaction scenarios, e.g., short-distance
drone flights to landing targets of middle to large sizes.
Fig. 9: Drone’s Trajectories on Pointing Tasks. The number
near the rectangle denotes its area.
(a) Crossing Tasks (b) Pointing Tasks
Fig. 10: Trajectory Area by Trials.
Human-drone interaction requires a common ground for
evaluation. That is, the existing prototypes of drone controllers
were experimented with under different conditions and exter-
nal factors that led to difficult comparisons between studies.
Re-building the testing environments or re-implementing the
input methods are challenging. It is worthwhile to mention
that a recent work [7] limits to simple flying tasks and lacks
data collection channels for drone movements (e.g., trajectory,
jerk, acceleration). Otherwise, setting up numerous markers in
room-scale environments to track the trajectories for such data
collection is costly and time-consuming [1], not to mention the
efforts of building physical obstacles [8]. Thus, our paper calls
for research efforts leveraging a common virtual platform of
drone flight controls. Researchers can collaboratively employ
our evaluation methodology1to compare diversified controllers
under a shared testing ground.
[1] L. Chen, K. Takashima et al., “Pinpointfly: An egocentric position-
control drone interface using mobile ar, in Proc. of the 2021 CHI Conf.
on Human Factors in Comp. Sys., ser. CHI ’21. USA: ACM, 2021.
[2] C. Liu and S. Shen, “An augmented reality interaction interface for
autonomous drone,” in 2020 IEEE/RSJ Inter. Conf. on IROS, 2020, pp.
11 419–11 424.
[3] P. Abtahi, B. Landry et al., “Beyond the force: Using quadcopters to
appropriate objects and the environment for haptics in virtual reality,”
in Proc. of the 2019 CHI Conf. on Human Factors in Comp. Sys., ser.
CHI ’19. USA: ACM, 2019, pp. 359:1–359:13.
[4] T. Kosch, M. Funk et al., “Dronectrl: A tangible remote input control
for quadcopters,” in The 31st Annual ACM Symp. on UIST Adjunct
Proceedings, ser. UIST ’18 Adjunct. USA: ACM, 2018, p. 120–122.
[5] M. Obaid et al., “How would you gesture navigate a drone? a user-
centered approach to control a drone,” in Proc. of the 20th Inter.
Academic Mindtrek Conf. USA: ACM, 2016, p. 113–121.
[6] J. P. Hansen, A. Alapetite et al., “The use of gaze to control drones,”
in Proc. of the Symp. on Eye Tracking Research and Applications, ser.
ETRA ’14. USA: ACM, 2014, pp. 27–34.
[7] Y.-P. Yau, L. H. Lee et al., “How subtle can it get? a trimodal study of
ring-sized interfaces for one-handed drone control,” Proc. ACM Interact.
Mob. Wearable Ubiquitous Technol., vol. 4, no. 2, Jun. 2020.
[8] K. Yamada et al., “Modeling drone crossing movement with fitts’ law,”
in HCI, 2019.
[9] M. Obaid, F. Kistler, G. Kasparaviˇ
e, A. E. Yantac¸, and M. Fjeld,
“How would you gesture navigate a drone? a user-centered approach
to control a drone,” in Proceedings of the 20th International Academic
Mindtrek Conference, 2016, pp. 113–121.
[10] J. P. Hansen, A. Alapetite, I. S. MacKenzie, and E. Møllenbach, “The
use of gaze to control drones,” in Proceedings of the symposium on eye
tracking research and applications, 2014, pp. 27–34.
[11] M. Landau and S. Van Delden, “A system architecture for hands-free
uav drone control using intuitive voice commands, in Proceedings of
the companion of the 2017 acm/ieee international conference on human-
robot interaction, 2017, pp. 181–182.
[12] F. Lier et al., “Towards automated execution and evaluation of simulated
prototype hri experiments,” in Proc. of the 2014 ACM/IEEE Inter. Conf.
on HRI, ser. HRI ’14. USA: ACM, 2014, p. 230–231.
[13] U. Berdica et al., “Mobile 3d printing robot simulation with viscoelastic
fluids,” in 2021 IEEE/RSJ Inter. Conf. on IROS, 2021, pp. 7557–7563.
[14] B. Shen, F. Xia, C. Li et al., “igibson 1.0: A simulation environment
for interactive tasks in large realistic scenes, in 2021 IEEE/RSJ Inter.
Conf. on IROS, 2021, pp. 7520–7527.
[15] N. Tsoi et al., “An approach to deploy interactive robotic simulators on
the web for hri experiments: Results in social robot navigation, in 2021
IEEE/RSJ Inter. Conf. on IROS, 2021, pp. 7528–7535.
[16] B. V. D. Nguyen et al., “Exploring an architectural framework for
human-building interaction via a semi-immersive cross-reality method-
ology, in Proc. of the 2021 ACM/IEEE Inter. Conf. on HRI, ser. HRI
’21. USA: ACM, 2021, p. 252–261.
[17] S. H. Seo, D. Geiskkovitch, and et al., “Poor thing! would you feel sorry
for a simulated robot? a comparison of empathy toward a physical and
a simulated robot,” in Proc. of the Tenth Annual ACM/IEEE Inter. Conf.
on HRI, ser. HRI ’15. USA: ACM, 2015, p. 125–132.
[18] D. Goedicke, J. Li et al.,VR-OOM: Virtual Reality On-ROad Driving
SiMulation. USA: ACM, 2018, p. 1–11.
[19] D. Wolf, K. Rogers et al.,JumpVR: Jump-Based Locomotion Augmen-
tation for Virtual Reality. USA: ACM, 2020, p. 1–12.
[20] E. Soria, F. Schiano, and D. Floreano, “Swarmlab: a matlab drone swarm
simulator, 2020 IEEE/RSJ Inter. Conf. on IROS, pp. 8005–8011, 2020.
[21] V. T. Nguyen et al., “Dronevr: A web virtual reality simulator for drone
operator, 2019 IEEE Inter. Conf. on AIVR, pp. 257–2575, 2019.
[22] E. Ebeid et al., “A survey of open-source uav flight controllers and flight
simulators,” Microprocess. Microsystems, vol. 61, pp. 11–20, 2018.
[23] S. Shah, D. Dey et al., “Airsim: High-fidelity visual and physical
simulation for autonomous vehicles,” 2017.
[24] K. Yamada et al., “Modeling drone pointing movement with fitts’ law,”
in Extended Abstracts of the 2019 CHI Conf. on Human Factors in
Comp. Sys., ser. CHI EA ’19. USA: ACM, 2019, p. 1–6.
[25] I. S. MacKenzie, Human-Computer Interaction: An Empirical Research
Perspective, 1st ed. San Francisco, CA, USA: Morgan Kaufmann
Publishers Inc., 2013.
ResearchGate has not been able to resolve any citations for this publication.
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