Levels of Automation for Human Influence of Robot Swarms
Phillip Walker, Steven Nunnally and Michael Lewis
University of Pittsburgh
Nilanjan Chakraborty and Katia Sycara
Carnegie Mellon University
Autonomous swarm algorithms and human-robot interaction (HRI) have both attracted increasing attention
from researchers in recent years. However, HRI has rarely extended beyond single robots or small
multi-robot teams. While one of the benefits of robot swarms is their robust capabilities and the ability of
their distributed algorithms to deal autonomously with the complex interactions amongst swarm members,
there is undoubtedly a need for humans to influence such swarms in some circumstances—especially when
these swarms are operating in unknown or hostile environments. In this paper, we approach the problem of
human-swarm interaction (HSI) using previous research in levels of automation (LOAs) in HRI. We create
a target searching task whereby the swarm can operate at two different levels of autonomy: an autonomous
dispersion algorithm, or user-defined goto points. We investigate what environmental conditions are
conducive to different amounts of human influence, and at what point further human intervention has a
detrimental effect on the swarm’s performance. The results show that for complex environments containing
numerous obstacles and small passageways, there is indeed a need for some human influence; however,
after a certain point, further influence causes performance degradation.
INTRODUCTION
Swarms are made up of numerous small, relatively
inexpensive robots with basic sensing and locomotive
capabilities. By themselves, these individual robots are not
able to perform many tasks of interest; however, as a group
they can be used to perform certain tasks much more robustly
and quickly than a more expensive individual robot or
multi-robot team. While the distinctions between swarms and
multi-robot systems are still not perfectly defined, for the
purposes of this paper, the primary distinction is one of control
complexity. Lewis, Wang, & Scerri (2006) use three levels of
command complexity to describe the possible workloads faced
by a human operator when controlling a robot team. For
instance, when performing a simple waypoint following task,
control of a robot team is typically O(n), as each additional
robot places an increasing demand on the operator because it
must be coordinated with all of the pre-existing robots. When
the operator must deal with the interactions between the
robots, increasing the number causes an exponential increase
in the control requirements for the human, making control
O(>n). Treating the robots as a swarm alleviates this problem
however, because the control algorithm handles the robot
interactions, and thus scales to large numbers of robots with
little extra effort, making control O(1).
Such control algorithms are typically distributed
algorithms that exhibit emergent behaviors based on local
interactions, thus allowing the swarm to act as a unified group
rather than a collection of distinct agents. Approaches to
swarm robotics come from the bio-inspired (Goodrich, Sujit,
Kerman, & Pendleton 2011) and physicomimentic (Spears &
Spears, 2012) views, as well as amorphous and spatial
computing (Khalsa, 2011; Bachrach, McLurkin, & Grue
2008). While these algorithms are powerful for controlling
large groups of robots in a decentralized manner and can
display a wide range of behaviors, they are difficult to predict
and control after the swarm is deployed. A primary goal of
this paper is to address this concern in a variety of
obstacle-filled environments with a swarm performing a target
searching task.
Related Works
Human-swarm interaction (HSI) requires a different
approach than human-robot interaction (HRI) because much of
swarm control is necessarily autonomous and distributed
(specifically the robot interactions). Research in swarm
robotics has focused primarily on improving the autonomous
control algorithms and the hardware of the robots, ignoring the
human control element. A few recent studies have investigated
how humans can effectively influence swarms. Kolling,
Nunnally, & Lewis (2012) investigated the ability of the
human to use various motion control algorithms and
communication graphs to accomplish simple tasks, and in
Walker, et. al. (2012) and Nunnally, et. al. (2012), the authors
looked more closely at a specific target searching task to
investigate communication effects—specifically bandwidth
and latency—on HSI. While communication is an important
aspect when dealing with swarms due to the limited
communication abilities available to simple robots, we must
also investigate how human operators perceive a swarm and
its performance, and how they decide when and how often to
act. This second problem is of particular importance, because
the manner and frequency with which an operator interacts
with the swarm could have a significant impact on the ability
of the human-swarm system to accomplish tasks (Walker, et.
al. 2012).
In general, human-robot and human-swarm interaction is
accomplished through supervisory control (Sheridan &
Verplank 1978; Sheridan, 1992; Sheridan, 2002). Although
supervisory control represents a general approach to
interaction with automation, this paper only considers
supervisory control of robotic applications. Supervisory
control in robotics describes a human-robot system in which
the robot executes the decision-making and control of tasks in
a semi-autonomous manner—requiring intermittent
monitoring and control on the part of the human operator.
Sheridan & Verplank (1978) proposes a 10-point level of
automation (LOA) scale to characterize the degrees of
autonomy possible for human-machine systems, ranging from
a system where the machine has full autonomy to one where
the human controls everything. This scale has been used and
modified extensively to describe and evaluate levels of
automation for a number of supervisory control systems on
differing robotic platforms (Riley, 1989; Kaber & Endsley,
1997; Ruff, Narayanan, & Draper, 2002; Proud, Hart, &
Mrozinski , 2003).
In recent years, researchers have extended the idea of
LOAs to a second dimension that specifies the task that is
being performed. To give a simple example, one human
operator supervising multiple unmanned aerial vehicles
(UAVs) to explore a given area for targets may want the
dispersion and paths of the UAVs to be automated, yet retain
control over what is marked as a target and what each of the
UAVs will do upon target detection. On the four-stage
information-processing model defined in (Parasuraman,
Sheridan, & Wickens, 2000), this corresponds to an action
implementation stage LOA of 7 or higher (depending on the
details of the implementation), and an information analysis
stage LOA of 4 or lower. This second dimension allows HRI
system designers to vary the LOA depending on the current
operation of the robot (Endsley, 1999; Parasuraman, Sheridan,
& Wickens, 2000; Miller & Parasuraman, 2003; Miller &
Parasuraman, 2007). In Coppin & Legras (2012), the authors
introduce the autonomy spectrum, which extends the
two-dimensional LOA model by allowing different
user-selectable modes at each control task corresponding to
different possible LOAs for the same task. Furthermore, their
model includes predefined pathways between different LOA
combinations at each stage, corresponding to the different
possible methods of operation.
This research has provided promising results towards a
better theory of how human operators might influence
individual robots or small robot teams operating under
different LOAs. However, while research in levels of
automation in HRI has been primarily concerned with
determining which LOAs are appropriate for human operators,
there has been little research investigating how a user
perceives and interacts with a swarm operating under
autonomous or semi-autonomous algorithms. Most algorithms
for swarm behavior, such as rendezvous or dispersion, only
give provably correct outcomes once many assumptions or
simplifications are met (such as a static environment with no
obstacles). In most real world cases these assumptions are not
met, and thus we need human operators to observe the swarm
as it changes and decide when the swarm is operating
according to the operator's desired goals and when the human
should override the autonomous control and provide corrective
input. However, while human supervisory control of swarming
robots may be necessary in these cases, it is unknown how
much control is needed, and after what point further operator
influence will degrade the performance of the swarm. In other
words, what is the best balance between automation and direct
human control? This paper investigates this question in the
context of a swarm performing a simple foraging task in
several environment types.
Hypotheses and Contributions
In this paper, we study the problem of human control of
swarm behavior and investigate the proper balance between
automation and direct human control. Our approach of
influencing the swarm as a whole is distinguished from earlier
work in human-multi-robot control, in which individual robots
are sequentially controlled. We hypothesize that, in more open
environments, a higher amount of automation will suffice in a
simple target searching task. However, in environments with
dense obstacles, and especially those which contain small
passageways and complex obstacle arrangements, a higher
level of human influence may improve swarm performance,
but only up to a point, after which performance will degrade.
The next section will describe the task that was given to
the participants, including a description of the simulation used,
the algorithms under which the robots operated, and the
overall design of the experiment. The subsequent section will
present and discuss the results of the study, followed by a
section concluding the paper and presenting possible avenues
of future research.
TASK DESCRIPTION
Our study is designed to investigate how humans use
different levels of intervention to control a swarm in a target
searching task, and whether or not increased human
interaction causes a decrease in swarm performance after a
certain point.
The Environment
We use four different environments of size 100x100
meters, each containing 100 randomly placed targets initially
unknown to both the user and the swarm. Each environment
contains a different number and arrangement of obstacles: two
with random obstacle placement at high and low densities, one
with a more structured obstacle placement (an office building
floor plan), and one open control environment (Figure 1).
We use Stage v. 3.2.2 (Gerkey, Vaughan, & Howard,
2003) to simulate the environment, the targets, and a swarm of
100 differential drive P2AT robots. The GUI and robot
controllers are implemented using the Robot Operating
System (ROS) (Quigley, et. al., 2009). The interface receives
the positions and target information from each robot and
displays them for the user in a large viewing window, and
allows the user to input commands using the mouse and
buttons on a side panel. The robots are displayed as small
circles, with a heading line indicating the direction each robot
is pointing. When the swarm sees a target, it will light up the
color of that target, indicating to the operator that a target is
within range of that robot. Each robot is equipped with a
proximity sensor with a range of four meters (we use a color
blob finder in the ROS implementation as the proximity
sensor), which is used to identify the obstacles, targets, and
other robots nearby.
Swarm Behavior
The human operator is in charge of moving the swarm
around the environment to locate targets that are dispersed
randomly throughout the open space. Two different
commands are provided to maneuver some or all of the
swarm: a disperse and a goto command. These two modes of
operation correspond to high and low levels of autonomy,
respectively, with the disperse mode being equivalent to a 7
on Sheridan's LOA scale (the system executes autonomously
and informs the human), and goto mode being equivalent to a
2 (the human chooses from a set of possible actions). The goto
mode is not fully in the operator's control (a LOA value of 1),
because the user cannot control the interactions between the
selected robots—only their global goal direction. We will now
discuss the disperse and goto commands in detail.
Figure 1. Each of the four environments used in the study: the dense random
environment (top left), sparse random (top right), structured (bottom left), and
control (bottom right).
Disperse. Each robot performs the disperse command by
using the blob finder to observe all obstacles, targets, and
other robots within 4 meters of the robot. Obstacles and other
robots contribute a repulsive force vector scaled linearly by
their distance to the robot (Equation 1). Targets have an
attractive force vector toward them, which always has a
magnitude of 1 (Equation 2).
〈 f
x , repel
, f
y ,repel
〉=(
∑
i=0
N
(r −d )∗〈 x
i
, y
i
〉)/ N
(1)
〈 f
x , attract
, f
y ,attract
〉=(
∑
i=0
N
〈 x
i
, y
i
〉)/ N
(2)
In the above equations, r represents the maximum range of the
blob finder, d the Euclidean distance to the object (as reported
by the blob finder), N, the number of obstacles returned by the
blob finder, and x
i
and y
i
the x and y position of the obstacle i
in the robot's coordinate frame. The attractive forces from the
target will always take precedence over the repulsive forces
from obstacles and other robots, so long as the robot is not
within 1 meter of a repulsive object, in which case the
repulsive force takes precedence. Only unidentified targets
exhibit an attractive force—identified targets are treated as
obstacles. A target is marked as identified once at least 2
robots view it simultaneously. We used this requirement to
simulate the need for robustness in target identification, as
swarm robots will likely have less reliable sensors on-board
with which to identify targets. Requiring multiple
simultaneous viewings would help overcome the false
reportings of any one sensor. If the robot is currently attracted
towards a target, its assigned linear velocity is the scalar
component of the force vector if the robot is more than 1
meter from the target, otherwise it is 0 (meaning the robot will
wait next to the target until it is identified, or until the user
directs the robot elsewhere). The magnitude of the angular
velocity is given in Equation 3.
v
a
=atan2( f
y , attract
, f
x , attract
)
(3)
If there are obstacles within 1 meter of the robot, or no
unidentified targets within range of the blob finder, then the
repulsive force takes precedence. In this case, the linear
velocity is set to the max speed of 0.4m/s, and the magnitude
of the angular velocity is given by Equation 4.
v
a
=atan2 ( f
y , repel
, f
x , repel
)
(4)
Goto. At any point during the mission, the human may
choose to select some subset of the robots to perform a goto
command. When the operator issues a goto command, each
robot receives the point specified by the user and sets its
angular velocity using Equation 5 and its linear velocity to a
constant 0.4m/s.
v
a
=atan2 ( f
y , goto
, f
x , goto
)−θ
r
(5)
In Equation 5, x
goto
and y
goto
are coordinates of the goto point in
the world coordinate frame, and θ
r
is the robot's heading. Once
the robot is within 1 meter of the goto point, it automatically
switches to performing a disperse command. In this manner,
the operator can let the swarm disperse to explore and cover
new areas autonomously, or override the automation and tell
subgroups to move to a location along a specific path. The
goto command allows the user more control over the behavior
of the robots; however, it requires more operator attention,
especially if the operator wishes to issue several sequential
goto commands, say to move part of the swarm around a large
obstacle.
Experimental Design
Twenty participants (8 men and 12 women) were
recruited from the University of Pittsburgh and surrounding
areas to participate in the study. Initially, each participant
received instructions on the task—both how to control the
swarm and mark targets—and an explanation of how the
disperse and goto algorithms work. After instruction, they
were allowed 10 minutes to train themselves with operating
the swarm and using the interface. Following this, each
participant had 10 minutes to search each of the four
environments in Figure 1, presented in a random order. In all
conditions, the participants began with the swarm of 100
robots positioned randomly in a 20x20 meter box at the center.
Participants were told that their goal was to find as many
targets as possible within the time given.
RESULTS AND DISCUSSION
During the experiments, each robot's position and current
mode (goto or disperse) was logged each second. We used
number of targets marked as the dependent variable in this
study and the measure of success for each participant in each
environment. A one-way ANOVA indicates that, overall, there
was a significant difference between environments in terms of
the number of targets marked (F = 19.15, p < .001). Table 1
gives the differences between environments.
Each participant was assigned to one of three equal-sized
groups for analysis based on how much of the time their
robots spent in goto mode (as opposed to disperse mode). The
members of the swarm in goto mode are being directed by the
operator to specific locations in the environment, and thus are
operating at the lower level of autonomy than those members
in disperse mode. Post hoc analysis showed that these three
groups gave the clearest picture of how much human control
was useful, and where the performance dropoff occurred. In
the structured environment, the three groups were low use of
goto commands (robots spent on average 37.6 to 91.6% of the
time in goto mode), medium (91.6 to 98.1%), and high (98.1 to
100.0%). Results from a one-way ANOVA indicate that in the
structured environments, there was a significant difference
between these groups (F = 10.15, p = .001), with the medium
goto group marking significantly more targets (mean M =
48.67) than the low (M = 23.43, p < .001) or high (M = 37.43,
p = .062) groups (Figure 2). There were no significant
differences between the three groups in the dense random (M
l
= 43.14, M
m
= 62.5, M
h
= 57.43, F = 0.41, p = .671), sparse
random (M
l
= 65.14, M
m
= 76.83, M
h
= 60.71, F = 0.58, p = .
568) or control (M
l
= 71.57, M
m
= 80.33, M
h
= 73.71, F = 0.33,
p = .727) environments. In the results above, M
l
, M
m
, and M
h
represent the mean number of targets identified in each of the
low, medium, and high goto groups, respectively. We chose to
group the participants afterwards as opposed to placing them
in predefined groups beforehand because we were unsure at
what point further human intervention would lower
performance. These results indicate that a significant amount
of human intervention was necessary in the structured
environments (around 91 to 98%), however, full human
control was less successful.
Random
Sparse
Structured Control
Random Dense, M = 53.95 .020* .002* < .001*
Random Sparse, M = 67.10 - < .001* .159
Structured, M = 35.90 - - < .001*
Control, M = 74.95 - - -
Table 1. The p-values for each of the environment pairs compared by mean
number of targets found. Significant differences are indicated by an asterisk.
Further analysis shows that the most successful operators
use the goto mode to break their swarms up into several
subgroups, each larger than or equal to the threshold needed to
mark a target, and directed those groups to explore separate
areas of the map. Subgroups were represented by connected
components of the sensing graph of the robots, whereby two
robots were connected if they were within sensing range of
each other. A linear regression on the data shows that
operators with a higher number of connected components of
sufficient size (i.e. larger than the target threshold) found more
targets on average (r
2
= .096, p = .005, see Figure 3). Here, r
2
is the correlation coefficient of determination for the linear
model. The data also show that, when grouped into
equal-sized low, medium, and high groups by number of
connected components, the high group maintained their higher
number of connected components throughout the experiment
more consistently, staying on average at 70.0% of the
maximum number of connected components attained
throughout the experiment (standard deviation σ = 0.059).
This was significantly higher than the low (M = 55.5%, σ =
10.5%, p = .023) and medium (M = 60.8%, σ = 7.5%, p = .
019) groups.
Figure 2. The number of targets marked by the each of the low, medium and
high groups of participants in the structured environment. The groups were
determined by the average time the robots spent in goto (non-autonomous)
mode.
These results indicate that operators who either rely on
the automation too much by leaving the swarm in disperse
mode, or who take manual control nearly all the time, do
worse than those who strike a balance between high and low
automation. This result was only evident in the structured
environment, as this environment required some intervention
to move subgroups of the swarm into different areas and
through the doorways and hallways, but too much intervention
caused robots to get stuck in corners, likely causing operators
to spend more time switching between subgroups than
searching for targets.
In the more open and less structured environments (i.e.
the dense and sparse random and control maps), differences in
the usage of the autonomous dispersion algorithm and manual
control via goto commands had no effect on the number of
targets identified. This is likely because there was little
downside to manually controlling the swarm, as there are no
corners or confined spaces in which the swarm could get
stuck. The most successful operators overcame the
disadvantages of reducing inter-robot distances through use of
the goto command by breaking the swarm up into subgroups
that searched different areas of the map.
Figure 3. The number of targets marked based on the average number of
connected components that were larger than the target identification threshold.
Each point represents one participant in one condition.
CONCLUSIONS AND FUTURE WORK
This study provides support for the idea that a balance
between levels of automation is necessary in HSI. In
structured environments, some intervention was needed to
properly disperse a swarm and discover targets throughout the
map; however, when the operator took too much control and
never allowed the automation to operate, performance
declined. We also demonstrated that in less structured
environments, participants were able to adopt one of two
strategies to find targets successfully: either leave most of the
control of swarm movement to the autonomous dispersion
algorithm, or manually break the swarm up into subgroups to
explore different areas of the map.
Future work should investigate separating operators into
predefined LOA groups for other swarm tasks, including
mapping and target identification tasks with realistic sensor
models, to determine if the optimal amount of human
influence is similar to this study. Other work could also
improve the dispersion algorithm to allow for adequate
coverage even when there are significant obstacles in the
environment. Finally, future research should also investigate
how humans perceive the ability of autonomous algorithms to
perform certain tasks, and whether they believe their own
manual influence improved or could improve swarm
performance.
AKNOWLEDGEMENTS
This research has been sponsored in part by AFOSR
FA955008-10356 and ONR Grant N0001409-10680.
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