A Neural Network Approach to UGV Reconnaissance in MOUT Environments
Brandon S. Perelman
Michigan Technological University
Abstract
One important trend in unmanned systems technology is the push for increased automation.
Many UGVs are currently tele-operated, and attempts have been made to automate individual
components of the work burden to improve the operator-UGV ratio. This paper contains a review
of the literature regarding UGV history, the tasks that automation must accomplish, challenges
for automation, finally a proposition for neural network-based UGV software that would allow a
micro UGV, deployed by a controller during operations in urban environments, to explore
buildings and encode the environment, transmit the information to the human controller, then
return.
Introduction
Unmanned ground vehicles (UGVs), in a dictionary sense, are ground-based mechanized
systems for carrying or transporting items. UGVs specifically do not carry human operators
because they are often selected specifically for jobs with qualities contraindicating human
presence, such as environmental hazards, size or weight constraints, or endurance requirements
(Gage, 1995). Examples of applications outside the military domain include space exploration
(e.g., planetary rovers) and inspecting and assessing damage in radiological disaster sites, such as
the reactors at Chernobyl and Three Mile Island (Fong & Thorpe, 2001).
Early research efforts at the Stanford Research Institute, funded by the Defense
Advanced Research Products Agency (DARPA), produced the first “intelligent” mobile robot,
Shakey, in the late 1960s. This unit was tele-operated via radio frequency, and contained a sensor
package including a video camera, a rangefinder, and touch sensors (Nilsson, 1969).
Much of the initial work in the field conducted by Hans Moravec, initially at Stanford
and then at Carnegie Melon University, focused on producing automation (for a comprehensive
review of the literature, see Gage, 1995). Importantly, into the late 80’s, while efforts to produce
truly automated UGVs for military tasks, such as reconnaissance, surveillance, and target
acquisition (RSTA), encountered funding obstacles, tele-operated UGV projects such as the
Advanced Teleoperator Technology (ATT) dune buggy demonstrated to military shareholders
that it was possible to remotely control ground vehicles for RSTA operations, and even remotely
operate weapons systems attached to those vehicles (Hightower, Smith, & Wiker, 1986).
Currently, the US military employs a number of tele-operated UGVs for a variety of
missions. As of October 2006, US Army UGV systems were involved in over 11,000 incidents
involving improvised explosive devices (IEDs; Nardi, 2009). iRobot’s PackBot system was used
in Afghanistan as early as 2002 to explore cave complexes, and Iraq in 2003 to search for
chemical weapons and conduct vehicle searches (Yamauchi, 2004). Tele-operated Military
UGVs show promise for a number of tasks, including RSTA operations, countermine and EOD
operations, chemical, biological, radiological, nuclear, and explosive (CBRNE) searches (Nardi,
2009), logistics (Gage, 1995), load carriage to support infantry operations (e.g., MULE and its
child-robots, Boston Dynamics’ Legged Squad Support System; DARPA, 2012), and even
offensive operations (e.g., the Marine Corps’ Gladiator UGV; Apostolopoulos, 2014).
The remainder of this manuscript is dedicated to (1) a justification for pursuing autonomy
in UGVs, a discussion of (2) approaches to automation, (3) challenges for automation, and
finally (4) a proposed neural network-based system for UGV operation facilitating RSTA in
military operations in urban terrain (MOUT).
Why Autonomy in UGVs?
Currently in the US military there is a strong push to increase the extent to which UGVs
are capable of operating autonomously (Nardi, 2009). There are a number of logistical reasons
for this drive. First, reducing (or eliminating) the number of human operators required to control
a single system reduces cost, and potentially allows one human to control multiple systems.
Second, the UGV should ideally be able to react immediately to dynamic tactical situations
without taking a human controller out of the fight (Mills, 2007). Third, a human controller
requires an interface with which to control the UGV, which increases costs. Fourth, perceptual
data from the UGV must be transmitted to the controller, and commands must be then
transmitted to the UGV. Signal lag in this loop and perceptual limitations imposed by the UGV’s
sensor package necessarily inhibit the controller’s ability to make control decisions, compared
with an autonomous system that can make decisions without the lag associated with transmitting
and interpreting information. Finally, research in the unmanned aerial vehicle (UAV) domain
(e.g., Mellinger, Michael, & Kumar, 2012) with highly agile UAVs indicates that autonomous
systems are capable of highly aggressive maneuvers that are simply too fast and complex for
even well trained humans to execute reliably using tele-operation.
Tele-operation Interface Constraints
Tele-operation interfaces may be classified into four systems. In direct interfaces, the
operator controls the UGV from the “inside out,” controlling the UGV with hand controllers and
receiving information from the UGV typically in the visual modality. This method of control
requires very high bandwidth low delay communication because video data often contains a
great deal of information. Multimodal / multisensor interfaces provide the operator with a variety
of control modes (e.g., individual actuator control versus coordinated motion) and multiple
displays (i.e., text and visual information). These interfaces combine information from multiple
sensors or data sources to present a single integrated view, and are appropriate for applications
where the remote task environment (e.g., satellite service in space, or volcano exploration) is too
complex to perceive in a way that allows sound decision making. Multimodal / multisensory
interfaces are used to control many High Altitude Long Endurance UAVs, such as Predator.
Novel interfaces include all “other” types of interfaces, with examples including haptic control,
gesture control, and hands-free brainwave-based methods. Finally, supervisory control describes
a scheme in which the operator divides a control problem into a sequence of subtasks that the
robot executes autonomously. Supervisory control may be thought of as a bridge between teleoperation and automation (Fong & Thorpe, 2001).
Currently, the majority of UGVs in the US arsenal are tele-operated, and the US
military’s tactics, techniques, and procedures (TTPs) for employing UGVs reflects this state
(Maxwell, Larkin, & Lowrance, 2013). In order to facilitate effect tele-operation, research efforts
have focused on improving sensor packages (i.e., perceptual systems) and controllers (i.e.,
executive systems).
UGV sensors are generally regarded as range-limited to the extent that operators are
seldom aware of events and objects outside the UGV’s immediate surroundings. Since the UGV
cannot perceive remote events, it cannot respond to them and, therefore, cannot secure larger
areas (Kogut, Blackburn, & Everett, 2003). Situation awareness (SA) and workload are serious
concerns for UGV operators and the effects of various manipulations on these factors have been
extensively studied. In their meta-analysis, Coovert, Prewett, Saboe, & Johnson (2010)
illuminated a number of factors impacting aspects of UGV operation. Some of these factors are
particularly pertinent to MOUT operations. For example, complex (e.g., urban) environments are
demonstrably more difficult to navigate than sparse environments (e.g., the ocean floor). The
literature concerning field of view (FOV), important in constricted urban environments, is
particularly inconclusive. For example, wide FOV decreases workload, improves SA, and offers
task-dependent performance benefits. However, wide FOV is associated with increased motion
sickness and difficulty steering. In simulations, participants required maps to carry out coarse
navigation. Maps of the environment are not always available and GPS is not always reliable,
and using a map requires that the controller split his attention between the map and the UGV’s
sensors. Tandem control schemes have been developed (e.g., Chen, 2010; Ha & Lee, 2013) that
allow a UAV to increase the UGV operator’s SA without a need for GPS, but such schemes
require direct line of sight between the unmanned systems, which would be obstructed in many
urban environments, and the expense of fielding an additional unmanned system. Finally,
workload issues are compounded when the UGV operator has duties beyond control, such as
operating weapon systems (Irvin, Leo, & Kim, 2012).
To address workload issues with controlling UGVs, researchers have tested different
styles of joystick (Ögren, Svenmarck, Lif, Norberg, & Söderbäck, 2013) and smartphone control
schemes (Walker, Miller, & Ling, 2013). Both of these types of controllers have associated costs
and rewards, however, and neither addresses the workload burden of simultaneously controlling
the UGV’s movement and performing other tasks (Irvin et al., 2012). In summary, tele-operation
research generally indicates tradeoffs with any conceivable interface, at best, or unrealistic
expectations, at worst. Providing the speed, tactical flexibility, and small “footprint” required for
MOUT operations requires automation.
Current Approaches to Automation
One definition of the minimum requirement for true autonomy is the “capability for both
planning the vehicle course and controlling the position of the vehicle on route.” (Childers, Bodt,
& Camden, 2011) For the purposes of this manuscript, the definition of true autonomy is
expanded to include an unmanned system’s ability to independently achieve stated mission
objectives. For example, a UGV tasked with RSTA should be capable of scouting independently
and recording the necessary information to provide its commander with a comprehensive account
of the situation.
More recently, the Army Research Laboratory (ARL) Robotics Collaborative Technology
Alliance (RCTA) has made significant progress toward this goal, which is attributable to
advances in technology. Specifically, these include perceptual advances such as improved laser
detection and ranging (LADAR) and other sensory devices, perceptual algorithms usable by
automatons to decipher this data, path planning algorithms, and improvements in networking
amongst UGVs (Childers et al., 2011). The remainder of this section comprises a review of the
literature of advances as they pertain to the RSTA mission.
Environment Perception
A UGV’s ability to perceive the environment takes on a different quality when the unit is
automated versus tele-operated. For example, human factors considerations such as motion
sickness prevention due to FOV characteristics (Coovert et al., 2010) are irrelevant in the context
of automated agents. However, additional programming is required to allow the unit to perceive
the information that it senses.
UGVs typically use some combination of vision (i.e., digital camera), LADAR, and sonar
sensors to gather environmental information. Sometimes this disparate information is aggregated
into a single picture (e.g., Peynot & Kassir, 2010). Each of these sensor types has its own
advantages. Vision and LADAR work well over moderate distances (around 200 m) in clear
weather, and can be used for water and mud detection (both serious threats to UGVs; Rankin &
Matthies, 2010; Rankin, Matthies, & Bellutta, 2011), but the quality of the sensory information is
easily degraded by environmental factors such as precipitation, airborne particulate, and foliage.
Sonar, despite its limited range, is capable of penetrating environmental debris (Yamauchi,
2010).
As the UGV senses environment information, on-board algorithms create a perceptual
picture, or model, of the environment using Simultaneous Localization and Mapping (SLAM)
algorithms. It is worth noting that this information is both actionable by the UGV itself, and
transmissible as reconnaissance information. LADAR is primarily used to create this model, and
is usable in both rural (Zhou & Dai, 2010) and urban terrain (Whitty, Cossell, Dang, Guivant, &
Katupitiya, 2010). In addition to mapping static terrain, LADAR data can be used to track
moving objects such as people (Navarro-Serment, Mertz, & Hebert, 2010).
Tactical Behavior
UGVs may act on their environmental perceptions in a number of ways, of which
navigation is perhaps the most common. In most contexts, because efficiency is the goal, shortest
path algorithms are commonly used. Shakey, for example, used the A* algorithm, whereas many
autonomous agents in both the UGV domain and cognitive modeling use Dijkstra’s algorithm or
something similar. However, in military applications, UGVs may be targeted by the enemy.
Therefore, tactical planning systems are required for self-preservation. One such system by
Hussain, Vidaver, and Berliner (2005) enables a UGV to use tactical behaviors to evade mobile
enemies and accomplish its goals. Specifically, the Advocates and Critics for Tactical Behaviors
(ACTB) system generates lists of “advocates,” situational prompts for certain tactical behaviors,
and selects from amongst them based on “critics,” reward plans based upon tactical goals (e.g.,
minimizing risk or maximizing speed). In addition to driving locomotion, the ACTB system
includes logic for aiming cameras and could presumably adapt to control weapons systems.
Challenges to Automation
UGV barriers to automation are typically the result of hardware, rather than software,
shortcomings. Specifically, unlike UAVs, UGVs sometimes operate in environments where
compass and GPS instruments cannot be used for localization. In urban environments,
environmental obstacles such as stairs pose serious threats to UGV mobility. Furthermore,
because they travel on a surface rather than flying, UGVs must be able to physically traverse
terrain, or at least recognize when traversing terrain would be difficult or impossible.
Sensor Challenges
GPS is often used by UGVs for localization, while a compass instrument is used to
establish current facing. Maxwell et al. (2013) describe a scenario in which semi-autonomous
unmanned air, sea, and land units defended a high value target attacked by an enemy which
arrived by water and then proceeding over land. The unmanned systems were tasked with
identifying and tracking the attacker. In this scenario, of the two UGVs, one performed
acceptably while the other experienced difficulty navigating. Specifically, the UGV’s compass
readings were disrupted by electromagnetic interference from that UGV’s range finder and
perhaps also vibration associated with traversing outdoor terrain. UGVs operating in urban
terrain will often need to traverse terrain such as stairs, and rely on LADAR for sensing the
environment, so these concerns are particularly important.
GPS units suffer from dilution of precision errors when UGV-satellite line of sight (LOS)
is occluded. In urban environments, UGVs will often operate inside of buildings, or in areas
where buildings are sufficiently high as to occlude LOS. Finally, accelerometers and gyroscopes
used by UGVs for proprioceptive information are affected by environmental conditions, such as
temperature (Durst & Goodin, 2012). In summary, compass, GPS, and proprioceptive
information are not robust sources of data for localization.
Environmental Challenges
In addition to sensor issues, the environment poses challenges for UGVs. For example,
mud and water threaten UGV mobility in rural as well as urban environments. Vision algorithms
(e.g., Rankin & Matthies, 2010; Rankin, Matthies, & Bellutta, 2011, respectively) can detect
these threats, but rely on cues that may not be available in urban environments, such as sky
reflections off of the liquid’s surface, or information from multiple sensors (e.g., stereo, visible,
and multi-wave IR) working together. Urban environments carry their own specific threats to
UGVs. Research carried out as part of the Safe Operations of Unmanned systems for
Reconnaissance in Complex Environments (SOURCE) Army Technology Objective (Grey,
Karlsen, DiBerardino, Mottern, & Kott, 2012) identified a number of urban environment-specific
threats to UGVs. However, most of these threats pertain to larger UGVs operating on roads.
Comparatively little research has been conducted on UGVs operating inside structures, though
the main challenges appear to be stairways. In its present incarnation, the proposed automated
UGV system would not necessarily be required to traverse stairs.
A Way Forward
As UGVs are a developing technology, authors have propositioned numerous
conceptualizations of how they might be used in future conflicts. Mills (2007) suggests that
UGVs will likely support troops in combat operations. Mills suggests that future UGVs will be:
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Heavily armed and armored
Automated, with manual control the exception rather than the rule
Capable of functioning independently of GPS or way-points for pathfinding
Equipped with excellent IFF capability and less than lethal options for force escalation
Able to recognize obstacles (e.g., fences and jersey barriers) and hazards (e.g., mud and
barbed wire)
Quiet

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Lightweight
Self-destructable or otherwise unusable by the enemy
Reliable
Cheap
Modular – payloads are expected to evolve faster than the UGV itself
Interoperable
Capable of tactical behavior and quickly reacting to dynamic circumstances
Mills (2007) presents a view of UGVs that is far more combat-oriented than traditional
approaches to automation would allow, however.
Contrary to this view, the role that we propose for UGVs in MOUT operations is less
combat-oriented and more focused on information gathering. Lif, Jander, & Borgvall (2006)
conducted an evaluation of UGVs employed for reconnaissance during MOUT training. Rather
than focusing on the capabilities of the UGV, Lif et al. (2006) focus on the needs of the military
unit and how the UGV meets or impedes those needs. For example, the authors sought to answer
questions such as, “Does using the UGV necessitate changes in tactics, resource allocation, or
unit organization?” The results of this study indicated that UGVs improve soldiers’ ability to
gather reconnaissance, unit stealth, and the utility of information gathered (as leadership can
view the images directly). However, using the UGV impeded forward movement – sending the
UGV into a building and following with infantry is slower than simply advancing with infantry –
and its use therefore requires a calculated tradeoff between safety and speed. Finally, the main
human factors concern with the UGV was whether using the UGV is worth occupying the
operator who could otherwise be fighting. Therefore, the UGV should at least incorporate a
capability to “return to handler.” These needs form the basis for the system proposed in the
remainder of this document.
Neural Networks for UGV Reconnaissance
While others (e.g., Mills, 2007) have envisioned armed UGVs patrolling alongside
soldiers, here I propose an autonomous UGV, deployable in groups, capable of scouting ahead of
infantry in urban terrain. In this section, I will discuss the current state of our modeling efforts,
and potential methods for integrating other approaches to modeling with existing technology
applied to machine cognition. This proposed system is inspired by neural networks of the
hippocampus that are used to approximate navigation tasks and memory in animals and humans.
A number of features make neural networks particularly attractive for this task.
1. Neural networks can be used as mechanisms for both encoding and retrieving information
about the environment, allowing an agent to simultaneously direct itself and provide
reconnaissance information using the same computations.
2. Neural networks in navigation are often used to model the behavior of animals that
depend on proximal, rather than distal, cues in the environment. The models are capable
of operating using sensory input from short range, highly reliable sources that have robust
analogs in robot navigation.
3. Because they encode memories, neural networks do not rely on GPS for localization.
Localization is achieved by examining the environment and comparing those features
with the agent’s memory for the environment.
Representing the Environment
In neural network models of the hippocampus, the goal is to represent the environment
using biologically plausible data structures. The most basic of these data structures are “place
cells” (O’Keefe & Nadel, 1978) in the cornu ammonis (CA) area of the hippocampus, which fire
as ensembles when the organism is in specific locations in the environment. These models are
primarily concerned with CA layers 1 and 3 (CA1 and CA3; Levy, 1989; Levy, Colbert, &
Desmond, 1990), with the CA1 layer containing the contextual cues required for localization,
and the CA3 layer containing a network of interconnected cells forming a cognitive map (e.g.,
Mueller, Perelman, & Simpkins, 2013; Samsonovich & Ascoli, 2005). CA3-CA3
interconnections code for locations between which it is possible to move (see Figure 1). In order
to navigate to a particular point, the location is chosen by activating the CA1 layer, which in turn
activates its corresponding CA3 location. There are a number of mechanisms for pathfinding
based on the CA3 representation, the most simple of which is perhaps a spreading activation
followed by a “goal scene” (Mueller et al., 2013).
Figure 1. The CA1 and CA3 matrices often used in hippocampus based neural networks.
In the code (e.g., Mueller et al., 2013, adapted from code originally published by
Samsonovich & Ascoli, 2005), place cell layers are structured as sets of matrices: an
environment grid, a recency map, a clutter matrix, a CA1 matrix, and a CA3 layer comprised of
four matrices – one for each cardinal direction. The cardinal direction matrices are necessary for
experiential learning (see below). This matrix set can be applied to any environment of arbitrary
size but can be scaled. That is to say that, in the current configuration, all matrices are 25 * 25
cells, but this could be adapted for higher or lower resolution as necessary.
The agent “learns” the environment through exploration. The cardinal direction matrices
represent known adjacency and accessibility amongst cells. As the agent explores the
environment, these matrices (initially all set to zero) receive positive values where the agent can
move between two cells. From the standpoint of biological plausibility, the associations here
represent the strengthening of CA3-CA3 connections through exploration. For example, if the
agent is in cell [3, 22] and moves north, then the north matrix for cell [3, 22] receives a value of
1. These matrices may or may not be treated as symmetrical (i.e., in such a scenario, the south
matrix for cell [3, 23] would also receive a positive value as the agent had just come from that
location). While currently the algorithm requires movement between the cells, it could easily be
modified so that mere perception is sufficient to build these associations. Through this
mechanism, the agent builds up a picture of where it (or other agents) can move in the
environment.
Purposive Navigation
Movement throughout the space can be directed using a search algorithm. One such
algorithm, novelty-seeking, relies upon the aforementioned recency map to push the agent to
new locations. However, if goal-directed movement is desired, the agent uses interplay between
the matrices comprising the CA3-CA3 structure and the data structure representing the CA1
layer. In the model used by Mueller et al. (2013), the CA1 layer was simply a matrix containing
goal locations. In other models (e.g., Gorchetchnikov & Hasselmo, 2002; Koene,
Gorchetchnikov, Cannon, & Hasselmo, 2003), the CA1 structure contains all features associated
with that particular context or location. In these models, the agent selects or prioritizes goal
locations based upon desired features, activating the CA1 locations that contain those features.
One mechanism for directing navigation to goals is spreading activation, or “goal scent.”
Once the agent has selected goals, and their corresponding CA1 cells are activated, the CA1
locations in turn activate corresponding CA3 locations. From those CA3 locations, activation
spreads throughout the CA3 network through CA3-CA3 interconnections, coded for by the
cardinal direction matrices. Spreading activation through grids is a biologically plausible
mechanism that is also used by existing navigation algorithms (cf. Whitty et al., 2010), but it is
important to note that the current neural network approach differs from these algorithms in a
number of ways. First, existing algorithms use computationally expensive optimization whereas
spreading activation is efficient. Second, the data structures in the aforementioned network,
specifically those comprising the CA3 layer, allow for asymmetries that a robot might encounter
in an urban environment; for example, an environment could contain a configuration where a
UGV could safely traverse through a hole in a wall in one direction, but could not return through
that hole due to height differences or debris. Third, the activation map created using the
spreading activation algorithm, combined with the recency map, allows the agent to use
heuristics for navigation such as hierarchical strategies for route planning. Finally, the present
mechanism slots most or all of the information required for purposive navigation, such as the
environment matrices, the goal locations, and the features in every location, into one list data
structure. Since most of these matrices overlap, they are conceptually very easy to use.
Regarding this final point, I have created a system (Perelman, 2013) whereby
environmental features, contained within an array, can be indexed either alone or in combination,
to feed the CA1 goal layer. In short, the array acts as a filter whereby goal sites are selected on
the basis of the features they contain. This information can be used either for localization or
purposive navigation. One example of an environment that necessitates this is navigation through
a shopping mall. If a UGV wants to determine its current location, and has learned the entire
layout of a mall including all of the stores, it can localize by comparing the combination of stores
(i.e., features) in its current location with all store combinations in the mall. The system is highly
versatile, as anything salient and perceptible can be coded as a feature.
Encoding the Environment
Many biologically inspired neural network models are used to understand animals, most
typically the rat. There are distinct differences between the types of environmental information
that rats encode versus, for example, primates. While primates can rely upon distal visual cues
(Rolls, Robertson, & Georges-François, 1997), rats rely mainly upon proximal cues. The purpose
of this section is to discuss how rodent-specific models handle environment encoding, and to
draw parallels between these mechanisms and currently technology. Rodent models are ideal for
our proposed UGV because they are parsimonious – the same mechanism that allows
environment encoding also provides tactical movement advantages. Note that while all of the
assumptions made herein are based upon established theory, some interpretations of the
published literature are not necessarily supported by consensus.
Rats cannot localize using GPS, and they presumably do not rely on an internal compass.
Rather, theories of rat navigation indicate place cell activation when a boundary is at a particular
distance and allocentric direction from the rat, a process afforded by boundary vector cells
(BVCs; Hartley, Burgess, Lever, Cacucci, & O’Keefe, 2000). Each BVC monitors its own
receptive field, a Gaussian field with specific shape and direction, and the receptive field can
change based on the shape of the environment. The rat localizes by comparing BVC activation in
its current location with BVC activations it has experienced in the past (a process that is
theoretically congruent with the feature-based approach to localization described by Perelman,
2013). Interestingly, longer receptive fields are in fact broader, whereas receptive fields for
detecting more proximal stimuli are tighter, potentially indicating (1) overlapping levels of
precision amongst all BVCs responsible for a single area relative to the rat’s position, and (2) a
potentially hierarchical structure where boundaries are scanned for at various resolution and
distances.
These BVCs perform a function essentially identical to proximal sensors described by
(Yamauchi, 2010). Typically, the sensors involved with modeling environments, using point
clouds for example (Whitty et al., 2010), are very expensive because they are intended to model
large environments. Using the BVC metaphor, the UGV can establish point clouds over much
shorter distances as it explores the environment. Therefore, it requires lower resolution sensors.
Sensors such as sonar provide sufficient sensory capability to model an environment provided
that the agent remains close to boundaries that it can use to “anchor” itself in the environment,
which is presumably one reason that rats tend to stick to walls when moving (Hoffman,
Timberlake, Leffel, & Gont, 1999). Moving along walls also affords tactical advantages such as
reduced visibility and probability of detection. There is no suggestion that an autonomous UGV
should travel along walls exclusively, only that such a preference may be advantageous.
Design Guidelines – A small UGV for reconnaissance in MOUT environments
The proposed autonomous UGV must meet existing design recommendations and exceed
tele-operated systems in terms of mission requirement fulfillment. In this section, I will discuss
the proposed hardware and software, and how such a system is superior to existing systems.
The present system was conceived mainly using the mission requirements described by
Lif et al. (2006). Specifically, the system is designed to address problems with tele-operation. A
reconnaissance UGV should, according to those mission requirements,
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Complete its mission without burdening a human operator
Provide information parsimoniously to commanders in real time
Return to its handler, and be capable of navigating to a specific location for extraction
Complete its mission quickly to minimize the extent to which the system slows the rate of
advance
In addition, though Mills’ (2007) recommendations pertained specifically to armed UGVs, the
proposed system was inspired by some of that design criteria. Therefore, the UGV should be
capable of,
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Automation, with affordances for manual control
Functioning independently of GPS or way-points for pathfinding
Able to recognize and model obstacles and hazards
Quiet movement and operation
Reliable
Cheap
The proposed system addresses these design considerations in a number of ways. The
proposed system is nearly entirely autonomous, though the UGV can use waypoints or even
search directed by certain features (e.g., preferentially search a space for humans or weapons).
Therefore, the system does not require constant monitoring or control, allowing all human
operators to remain “in the fight.” Partial or complete maps can be inspected by the operator in
real time, or at will. The system relies on input from highly reliable and cheap low resolution
sensors. Because it depends upon context interpretation for localization, and accomplishes its
mission through experiential learning, the system does not rely on GPS. Even given a partial
mapping of the environment, the UGV can be directed, via waypoints, to specific locations at
will.
The system is also very inexpensive. Sonar sensors are cheap (i.e., < $100 USD)
compared to LIDAR sensors. A single gimbaled sonar sensor is sufficient to sweep the
environment. A second advantage of sonar sensors is that they are very small. Ideally, the unit
would be roughly the size of a football and ruggedized (see Figure 2), so that it can be thrown
through windows. The unit should be capable of transmitting without line of sight in urban
environments to its operator. Since it is intended to be thrown, the unit should ideally be capable
of operating while inverted, or at least have a mechanism to right itself. Since the unit is entirely
autonomous, after it is thrown into the structure, it immediately begins collecting data. Beginning
with a novelty-seeking random walk, the unit begins mapping the environment by logging
boundaries and the locations of objects in the room. As the unit explores, it creates both a picture
of the environment that is interpretable by the operator, and a means by which it can navigate to
a waypoint for extraction.
Figure 2. The UGV. Not drawn to scale, the UGV is expected to be small, roughly the
size of a football.
Interface
In the proposed system, the operator would receive information from the unit by means
of a handheld display that updates in real time as the unit explores the environment. The display
can be monitored as the unit explores, or monitored when the operator has time to spare. Since it
is constantly transmitting, if the unit is destroyed or otherwise disrupted, no information will be
lost. And, since the unit does not require GPS or tele-operation to accomplish its mission, it can
continue logging information even if it temporarily moves outside of the range necessary to
transmit to the operator.
The operator should interface with the unit through a rugged handheld device (see Figure
3). The UGV unit should have only one button: a power button used to prime the unit. This same
button should not be used to power down the unit, as it could be activated inadvertently as the
unit explores. Regarding the interface device, touchscreen is contraindicated, as the operator may
need to use the unit while wearing gloves. A small directional pad and cursor should provide
sufficient precision to set waypoints and move the field of view around the space as it is
constructed, two buttons should provide zoom-in and zoom-out functionality (for the display of
the plotted space), and four separate numbered buttons should allow a single display device to
receive and display data from up to four separate units. Those same buttons, when pressed in
combination with the power button on each individual unit, should allow the operator to sync a
unit with a specific number on the display device.
Figure 3. Handset for interfacing with the UGV.
The plotted space itself should display as a map of the information collected by the unit.
A point could indicate where the unit was activated, so that the user has a bearing on the map’s
orientation. As per Figure 3, the unit’s current location within its grid space should be visible
along with the environment as it is constructed via sonar. If, for some reason, the agent’s feed
(via radio) is terminated, its current position should change to indicate this state (becoming a
hollow circle, for example) and the map should remain as it was last constructed. This requires
that a representation of the map be stored on both the unit and the display, and necessitates
memory on both devices.
Example of Use Scenario
A squad of soldiers is tasked with investigating a house where a “be on the lookout”
(BOLO) suspect is believed to be hiding. The suspect is wanted for his role in manufacturing
IEDs used against coalition soldiers. Because of the possibility of armed enemy inside, and the
possibility of traps inside the house, they are authorized to first investigate the structure using a
number of the automated UGVs. Arriving outside the house, a two story structure, the UGV
operator activates two units and throws one unit through a downstairs window and another
through the window of the upper floor. Immediately, the units begin a novelty-seeking random
walk to map out the environment, preferring to stick to walls as they model the rooms. They
transmit the information in real time to the UGV operator’s handset. While they are exploring,
the UGV operator is free to provide perimeter security as the units transmit to his handset. After
a couple of minutes, the operator examines the handset and, finding that the UGVs have mapped
out the space, shows the display to the patrol leader.
Finding no humans within the structure, the patrol leader calls explosive ordinance
disposal (EOD) professionals to the site. The structure floor plan generated using the UGV sonar
data provides the EOD personnel with information that will guide their search using either a teleoperated robot or by hand.
References
Apostolopoulos, D. (2014). Gladiator. Carnegie Mellon – The Robotics Institute. Retrieved from
http://www.ri.cmu.edu/research_project_detail.html?project_id=566&menu_id=261,
February, 2014.
Chen, J. Y. C. (2010). UAV-guided navigation for ground robot tele-operation in a military
reconnaissance environment. Ergonomics, 53, 940-950.
Childers, M. A., Bodt, B. A., & Camden, R. (2011). Assessing unmanned ground vehicle tactical
behaviors performance. International Journal of Intelligent Control and Systems, 16, 5266.
Coovert, M. D., Prewett, M. S., Saboe, K. N., & Johnson, R. C. (2010). Development of
Principles for Multimodal Displays in Army Human-Robot Operations (No. ARL-CR651). University of Florida: Tampa
DARPA. (2012). DARPA’s four-legged robots walk out for capabilities demonstration. DARPA
News. Retrieved from http://www.darpa.mil/NewsEvents/Releases/2012/09/10.aspx,
February, 2014.
Durst, P. J. & Goodin, C. (2012). High fidelity modelling and simulation of inertial sensors
commonly used by autonomous mobile robots. World Journal of Modelling and
Simulation, 8, 172-184.
Fong, T. & Thorpe, C. (2001). Vehicle Teleoperation Interfaces. Autonomous Robots, 11, 9-18.
Gage, D. W. (1995). UGV History 101: A brief history of unmanned ground vehicle (UGV)
development efforts. Unmanned Systems Magazine, 13.
Gorchetchnikov, A; Hasselmo, ME (2002). A model of hippocampal circuitry mediating goaldriven navigation in a familiar environment. Neurocomputing, 44, 423-427. doi:
10.1016/S0925-2312(02)00395-8
Gray, J. P., Karlsen, R. E., DiBerardino, C., Mottern, E., & Kott, N. J. (2012). Challenges to
autonomous navigation in complex urban terrain. In SPIE Defense, Security, and
Sensing (pp. 83870B-83870B). International Society for Optics and Photonics.
Ha, C. & Lee, D. (2013). Vision-based teleoperation of unmanned aerial and ground vehicles. In
Proceedings of the 2013 IEEE International Conference on Robotics and Automation
(ICRA), Karlsruhe, Germany, May 6-10.
Hartley, T., Burgess, N., Lever, C., Cacucci, F., & O’Keefe, J. (2000). Modeling place fields in
terms of the cortical inputs to the hippocampus. Hippocampus, 10, 369-379.
Hightower, J. D., Smith, D. C., & Wiker, S. F. (1986). Development of remote presence
technology for teleoperator systems. In Proceedings of the 14th Meeting of the
UJNR/MFP, September.
Hoffman, C. M., Timberlake, W., Leffel, J., & Gont, R. (1999). How is radial arm maze behavior
in rats related to locomotor search tactics? Animal Learning & Behavior, 27, 426-444.
Hussain, T. S., Vidaver, G., & Berliner, J. (2005, May). Advocates and critics for tactical
behaviors in UGV navigation. In Defense and Security (pp. 255-266). International
Society for Optics and Photonics.
Irvin, C., Leo, S., & Kim, J. (2012). GunBot: Design concept of a semi-autonomous UGV with
omni-directional mobility and auto-target tracking. In Proceedings of the Florida
Conference on Recent Advances in Robotics, Boca Raton, Florida, May 2012.
Koene, R.A., Gorchetchnikov, A., Cannon, R.C. and Hasselmo M.E. (2003) Modeling goaldirected spatial navigation in the rat based on physiological data from the hippocampal
formation. Neural Networks, 16, 577-84.
Kogut, G., Blackburn, M., & Everett, H. R. (2003). Using video sensor networks to command
and control unmanned ground vehicles. SPACE AND NAVAL WARFARE SYSTEMS
CENTER SAN DIEGO CA.
Levy, W.B. (1989). A computational approach to hippocampal function. In R. D. Hawkins & G.
H. Bower (Eds.), Computational models of learning in simple neural systems (pp. 243305). New York, NY: Academic Press.
Levy, W. B., Colbert, C. M., & Desmond, N. L. (1990). Elemental adaptive processes of neurons
and synapses: a statistical/computational perspective. In M. A. Gluck & D. E. Rumelhart
(Eds.), Neuroscience and connectionist models (pp. 187-235). Nillsdale, NJ: Lawrence
Erlbaum Assoc., Inc.
Lif, P., Jander, H., & Borgvall, J. (2006). Tactical evaluation of unmanned ground vehicle during
a MOUT exercise. In Proceedings of the Human Factors and Ergonomics Society 50th
Annual Meeting, 2557-2561.
Maxwell, P., Larkin, D., & Lowrance, C. (2013). Turning Remote-Controlled Military Systems
into Autonomous Force Multipliers. Potentials, IEEE, 32(6), 39-43.
Mellinger, D., Michael, N., & Kumar, V. (2012). Trajectory generation and control for precise
aggressive maneuvers with quadrotors. The International Journal of Robotics
Research, 31(5), 664-674.
Mills, M. E. (2007). Challenges to the acceptance and proliferation of tactical UGVs. RUSI
Defence Systems, 10(2), 28-30.
Mueller, S. T., Perelman, B. S., & Simpkins, B. (2013). Pathfinding in the cognitive map:
Network models of mechanisms for search and planning. Biologically Inspired Cognitive
Architectures, 5, 94-111.
Nardi, G. J. (2009). Autonomy, Unmanned Ground Vehicles, and the US Army: Preparing for the
Future by Examining the Past. ARMY COMMAND AND GENERAL STAFF COLL
FORT LEAVENWORTH KS SCHOOL OF ADVANCED MILITARY STUDIES.
Navarro-Serment, L. E., Mertz, C., & Hebert, M. (2010). Pedestrian detection and tracking using
three-dimensional LADAR data. The International Journal of Robotics, 29, 1516-1528.
Nilsson, N. J. (1969). A mobile automaton: An application of artificial intelligence techniques. In
Proceedings of the First International Joint Conference on Artificial Intelligence,
Washington, D.C., May, pp. 509-520.
Ögren, P., Svenmarck, P., Lif, P., Norberg, M., & Söderbäck, N. E. (2013). Design and
implementation of a new teleoperation control mode for differential drive UGVs.
Autonomous Robots, 1-9.
O'Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map (Vol. 3, pp. 483-484).
Oxford: Clarendon Press.
Perelman, B. S. (2013). A simple feature based approach to context in neurocomputational
models of navigation. Unpublished manuscript.
Perelman, B. S. & Mueller, S. T. (2013). A Neurocomputational Approach to Modeling Human
Behavior in Simulated Unmanned Aerial Search Tasks. In Proceedings of the 2013
International Conference on Cognitive Modeling.
Peynot, T. & Kassir, A. (2010). Laser-camera data discrepancies and reliable perception in
outdoor robotics. In Proceedings of the 2010 IEEE/RSJ International Conference on
Intelligent Robots and Systems, Taipei, Taiwan, October 18-22.
Rankin, A. L. & Matthies, L. H. (2010). Passive sensor evaluation for unmanned ground vehicle
mud detection. Journal of Field Robotics, 27, 473-490.
Rankin, A. L., Matthies, L. H., & Bellutta, P. (2011). Daytime water detection based on sky
reflections. In Proceedings of the 2011 IEEE International Conference on Robotics and
Automation, Shanghai, China, May 9-13.
Rolls, E.T., Robertson, R.G., & Georges-François, P. (1997). Spatial view cells in the primate
hippocampus. European Journal of Neuroscience, 9, 1789–94
Sam, R. & Hattab, A. (2014). Improving the control behavior of unmanned ground vehicle
(UGV) using virtual windows. Unpublished manuscript. Retrieved from
http://www.ammarhattab.com/, January 31, 2014.
Samsonovich, A. V., & Ascoli, G. A. (2005). A simple neural network model of the
hippocampus suggesting its pathfinding role in episodic memory retrieval. Learning &
Memory, 12, 193-208. doi:10.1101/lm.85205
Walker, A. M., Miller, D. P., & Ling, C. (2013). Spatial orientation aware smartphones for teleoperated robot control in military environments: A usability experiment. In Proceedings
of the Human Factors and Ergonomics Society 57th Annual Meeting.
Whitty, M., Cossell, S., Dang, K. S., Guivant, J., & Katupitiya, J. (2010). Autonomous
navigation using a real-time 3D point cloud. In Proceedings of the Australasian
Conference on Robotics and Automation.
Yamauchi, B. (2004). PackBot: A versatile platform for military robotics. In Proceedings of the
SPIE, 5422, 229.
Yamauchi, B. (2010). All-weather perception for man-portable robots using ultra-wideband
radar. In 2010 IEEE International Conference on Robotics and Automation, Anchorage,
AK, May 3-8.
Zhou, B. & Dai, X. Z. (2010). Set-membership based real-time terrain modeling of mobile robots
with a laser scanner. In Proceedings of the 2010 IEEE International Conference on
Mechatronics and Automation, Xi’an, China, August 4-7.