UNIT MISSION , OBJECTIVES , TABLE OF ORGANIZATION & EQUIPMENT,TACTICAL COLLECTION , INTELLIGENCE PREPARATION OF THE COIN BATTLESPACE & SUPPORT TO OPERATIONS

Basic Training:

BN INT HQ

PUSH-PULL CLOUD

 This interface supports abstract queries
such as (i) patrol these roads; (ii) search this area; (iii)
provide imagery of a specific location; (iv) track all targets
of a specific class on a specific route or location; or
(v) alert me whenever a threat is identified within a certain
distance of my location.Information that matches
these queries is sent by the system to the handheld device. The handheld interface
provides a map of the surrounding
area that displays real-time
tracks and detections and imagery
metadata. The imagery metadata
describes, at a glance, the
imagery available from the surrounding
area. OPISR-enabled
vehicles are autonomous—if the
information required by the warfighter
is not available at the
time the query is made, OPISR
unmanned vehicles autonomously
relocate so that their sensors can
obtain the required information.
OPISR-enabled unmanned vehicles
support multiple warfighters
simultaneously, with vehicles selforganizing
to define joint courses of action that satisfy
the information requirements of all warfighters.

all OPISR devices are capable
of operating independently as standalone systems or
as ad hoc coalitions of devices.

Each OPISR agent contains a
personal blackboard system that maintains a model of
the agent’s environment. Three types of information are
stored on each agent’s blackboard: beliefs, metadata, and
raw data. Raw data are unprocessed sensor data from a
sensor within the OPISR system. Metadata is information
that provides context to a set of raw data including
sensor position, pose, and time of collection. Beliefs are
abstract “facts” about the
current situation. Beliefs
include geospatial artifacts
such as targets, Blue
force locations, or search
areas.Beliefs can be
developed autonomously
from onboard pattern
recognition software or
data fusion algorithms or
asserted by humans. Mission-
level objectives, the
goals that drive OPISR,
are a special class of belief
that must be produced by
a human. The storing and
retrieval of information
to and from agent blackboards
is performed by
the blackboard manager.
The blackboard manager
accepts, stores, and
retrieves information from
171sensors
on board the agent’s device, from other agents,
or from pattern recognition/data fusion software contained
within the agent. The integrity of the data stored
on the blackboard is maintained by a truth maintenance
system (TMS). The TMS performs two functions. First,
the TMS resolves conflicts between beliefs. The simplest
form of conflict resolution is accomplished by storing
the belief with the more recent time stamp. For example,
one belief might posit that there is a target at grid [x, y]
at time t0, and a second belief might posit that there is
no target at grid [x, y] at time t1. More sophisticated conflict-
resolution algorithms are scheduled to be integrated
into OPISR in 2012. The second TMS function is the
efficient storage of information within the blackboard.
When performing this task, the TMS caches the most
relevant, timely information for rapid access and, when
long-lived systems generate more data than can be managed
within the system, the TMS removes less important
information from the blackboard. For caching and
removal, the importance of information is defined by the
age, proximity, uniqueness, and operational relevance.

Agent Communications Manager
Coordination between agents is asynchronous,
unscheduled, and completely decentralized, as it has
to be, because any centralized arbiter or scheduled
communications would introduce dependencies
that reduce the robustness and fault tolerance that
is paramount in the OPISR design. Because agent
communication is asynchronous and unscheduled,
there is no guarantee that any two agents will have
matching beliefs at an instance of time. Fortunately,demonthe
control algorithms used by OPISR are robust to
belief inconsistencies. Cross-agent truth maintenance
is designed to the same criteria as agent-to-agent
communications: Information exchanges between
agents seek to maximize the consistency of the most
important information but do not require absolute
consistency between agent belief systems. Information
exchange between agents is performed by the agent
communications manager. When communications
are established between agents, the respective agent
communications managers facilitate an exchange of
information between their respective blackboards.
When limited bandwidth and/or brief exchanges limit
the amount of information exchanged between agents,
each agent communications manager uses an interface
control component to prioritize the information to be
transmitted. Information is transmitted in priority
order, with priority being determined by information
class (beliefs being the most important, followed by
metadata), goal association (e.g., if a warfighter
has
requested specific information, then that information is
given priority), timeliness, and uniqueness.

OPISR seeks out relevant
information, pushing key tactical information directly 169to
impacted soldiers in real time.
warfighters
connect into
the OPISR “cloud,” task OPISR with mission-level ISR
needs, and are subsequently provided with the intelligence
they need. This capability provides intelligence
directly to the warfighter
without requiring the warfighter
to personally direct, or even know about, the
OPISR assets gathering the information. OPISR is
autonomous.


Autonomous Control: cSwarm
OPISR’s autonomous unmanned vehicles use an
approach called dynamic co-fields (DCF), also known
as stigmergic potential fields, to generate movement and
control actions. DCF is a form of potential field control.
Potential field control techniques generate movement
or trigger actions by associating an artificial field function
with geospatial objects. In OPISR, the objects that
are used to derive fields are beliefs. Fields represent some
combination of attraction and/or repulsion. By evaluating
the fields for all known beliefs at a vehicle’s current location,
a gradient vector is produced. This gradient vector
is then used to dictate a movement decision.

DCF extends an earlier potential field
approach called co-fields8 by making the potential fields
dynamic with respect to time and also making vehicle
fields self-referential. Self-referential fields are fields that
induce vehicle decisions that are generated by the vehicle’s
own presence. Adding these dynamic qualities is
key to managing two well-known problems with potential
field approaches: namely, the tendency of vehicles
to become stuck in local minima and the propensity to
exhibit undesired oscillatory behavior. As implemented
in OPISR, DCF is used to cause specific behaviors such
as search, transit, or track, as well as behavioral selection.

All unmanned vehicles in OPISR execute
cSwarm. DCF behaviors specific to unique classes
of vehicle are produced by tailoring the field formula,
which is stored in a database within cSwarm. OPISR
autonomous unmanned vehicles are capable of a variety
of behaviors including:
• Searching contiguous areas defined by warfighters
• Searching linear networks such as roads
• Transiting to a waypoint
• Blue force over-watch
• Target tracking
• Perimeter patrol
• Information exchange infrastructure, in which
unmanned vehicles maneuver to form a network
connection between an information source, such as
an unattended sensor, and warfighters
that require
information on the source. Note that the warfighter
is not required to specify this behavior; the warfighter
needs only to specify the information need,
and the vehicle(s) utilize(s) this behavior as a means
to satisfy the need.
• Active diagnosis, in which vehicles reduce uncertain
or incomplete observations through their organic
sensing capabilities. For example, a UAV with a
sensing capability that is able to classify targets will
automatically move to and classify unclassified targets
being tracked by a cooperating radar.
In addition to the mission-level behaviors described
above, OPISR vehicles exhibit certain attributes within
all behaviors. These universal attributes are:
• Avoiding obstacles or user-defined out-of-bounds
areas
• Responding to direct human commands. OPISR
unmanned vehicles are designed to function autonomously
in response to mission-level objectives;
however, when operators provide explicit flight
instructions, OPISR vehicles always respond to the
human commands i


FUTURE WORK
In FY2012, APL is planning additional improvements
to the OPISR system. Specific OPISR improvements
include (i) the integration of more sophisticated data
fusion techniques into the distributed blackboard, specifically
upstream data fusion and closed-loop ISR, (ii)
flight testing of autonomous behaviors that allow UAVs
to form network bridges between remote sensors and
users, (iii) introduction of advanced simulation-based
test and evaluation techniques, and (iv) the integration
of Exec-Spec into the OPISR framework and Exec-Spec
flight testing. Exec-Spec is an autonomy system developed
by APL for spacecraft control. In the OPISR–Exec-
Spec integration, Exec-Spec will manage vehicle fault
management and safety override functions.

The OPISR system is a framework that provides
a capability through which numerous unmanned platforms simultaneously provide real-time actionable
intelligence to tactical units; abstract, manageable
situational awareness to theater commanders; and highquality
forensic data to analysts. APL has demonstrated
an OPISR system that includes a distributed, selflocalizing
camera payload that provides imagery and
positional metadata necessary to stitch information
from multiple sources; a distributed collaboration system
that is based on robust ad hoc wireless communications
and agent-based data management; and a user interface
that allows users to receive real-time stitched imagery
from unmanned vehicles and that does not require users
to directly control (or even expressly be aware of) the
unmanned vehicles producing the imagery. OPISR is a
bold vision that presents an innovative approach to ISR,
an important enabler emphasized in the Quadrennial
Defense Review13 and other key policy documents, and
gives the Laboratory an enhanced ability to help sponsors
address future capability gaps in this critical area.