Large-Scale UAS Traffic Management
The
major specific objectives of this work are to:
1.
Facilitate the creation of lane-based UAS Traffic
Management (UTM) systems.
2.
Provide safe flight reservation system based in an effective and efficient strategic
deconfliction capability.
3.
Develop robust contingency mitigation strategies,
both pre-flight and in-flight. Pre-flight includes the creation of
emergency pullout and landing lanes; in-flight means tactical
deconfliction protocols such as our Closest Point of Approach
algorithm.
4.
Allow symbiotic learning of both UTM structure and
parameters as well as UAS optimal behaviors using reinforcement
learning in an Agent Based Modeling and Simulation (ABMS) framework.
These
address the major research issues in realizing large-scale UAS
deployment: (1) safe scheduling,
(2) contingency management, and (3) optimal performance.
(also see Large-Scale UTM Papers and Data
and a Lane-Based
UTM Animation)
AFOSR: DDDAS-based Geospatial Intelligence:
UAV Flight Path Planning in Urban Environments
The
major specific objectives of this work are to:
1.
Quantify uncertainty of decision support assertions
formulated by the system resulting from the innovative merger of
state-of-the-art state estimation methods with a novel Bayesian
argumentation system to be developed here.
2.
Develop a dynamic data acquisition recommendation
methodology triggered as a means to reduce uncertainties that arise in
assertions. Data acquisition results in new data which attacks the
arguments which support specific assertions.
3.
Develop optimal path planning methods for
low-altitude surveillance and reconnaissance missions based full
GEOINT: GIS, real-time HUMINT, SIGINT, and MASINT, as well as physics
models of wind and particulate density. This allows integration of
information about mission goals, enemy threats, fuel consumption,
etc. to produce best overall decisions.
4.
Perform validation experiments in BRECCIA, a prototype Geospatial
Intelligence system.
These
address three major research issues in DDDAS: (1) quantify sensing capability,
(2) develop new qualitative modeling approaches, and (3) develop adequate
experimental methods.
(also see BRECCIA Papers and Data)
Computational
Sensor Networks
AFOSR: Bayesian Computational
Sensor Networks
The
major specific objectives of this work are to:
1.
Develop Bayesian Computational Sensor Networks (BCSN) which
detect and identify structural damage.
We will quantify physical phenomenon and sensor models; e.g., develop
piezoelectric and other computational models to reconstruct physical phenomena
and characterize uncertainties due to environmental factors. Note that we are
modeling the physics of the signal and sensor, and if other mechanics models
are needed, will use existing.
2.
Develop an active feedback methodology using model-based
sampling regime (rates, locations and types of data) realized with embedded
sensors and active sensor placement. This will allow on-line sensor model
validation, and the use of on-demand complimentary sensors.
3.
Develop a rigorous model-based systematic treatment of the
following uncertainty models: (1) stochastic uncertainties of system states,
(2) unknown model parameters, (3) dynamic parameters of sensor nodes, and (4)
material damage assessments (viewed as source input parameters).
4.
Perform validation experiments on metal and composite
structures.
These
address three major research issues in DDDAS: (1) quantify sensing capability,
(2) develop new qualitative modeling approaches, and (3) develop adequate
experimental methods.
(also see SNET Papers and Data)
Symmetry
as a Basis for Cognition
Imagine
a robot that (1) when first powered up spends a few days learning about its own
physical structure, including sensing and actuation capabilities, (2) then
connects to the internet and can find appropriately encoded knowledge useful to
its current environment, (3) next asks humans to teach it tasks of interest to
them, and (4) finally enters its life cycle in its designated role, creating
its own knowledge that can be shared.
Such a scenario depends upon shared semantic grounding of the embodied
agents' concepts, as well as computationally effective and efficient
conceptualization processes. We exploit symmetry as a general framework to
achieve this grounding using a specific set of symmetry operators for the recognition,
representation and exploitation of sensorimotor data streams to achieve robust,
autonomous robot behavior. This is a new
approach to robot architecture which uses a set of innate symmetry theories to
parse sensorimotor data into constructs which coordinate the simultaneous
control of actuator/sensor sequences in order to bootstrap affordances from
exploratory actions.
Specifically,
we are developing (1) robust symmetry representations and associated detectors
for 1D, 2D, and 3D data, (2) symmetry-controlled actuators for physical robots,
(3) combined sensorimotor symmetry operators which define desired robot
behaviors, and (4) a symbolic language for robots to share representations and
behaviors. These behaviors are expressed
in such a way as to allow interpretation on a variety of platforms for which
the semantics is defined. Such
conceptualizations represent and maintain robust invariances of the robot with
respect to the environment (e.g., upright pose, forward motion). Finally, we propose to validate these ideas
on a sequence of Symbots - robots whose design and construction is based on
these principles and to measure their performance on real-world scenarios.
(also
see SE(3); )
RobotShare:
Robot Knowledge Sharing
Knowledge representation is a traditional field in
artificial intelligence. Researchers have developed various ways to represent
and share information among intelligent agents. Agents that share resources,
data, information, and knowledge perform better than agents working alone.
However, previous research also reveals that sharing knowledge among a large
number of entities in an open environment is a problem yet to be solved.
Intelligent robots are designed and produced by different manufacturers. They
have various physical attributes and employ different knowledge
representations. Therefore, any non-standard or non-widely-adopted technology
is unsuitable to provide a satisfactory solution to the knowledge sharing
problem. In this research, we pose robot knowledge sharing as an activity to be
developed in an open environment - the World Wide Web. Just as search engines
like Google provide enormous power for information exchange and sharing for
humans, we believe a searching mechanism designed for intelligent agents can
provide a robust approach for sharing knowledge among robots. We have
developed: (1) a knowledge representation for robots that allows Internet
access, (2) a knowledge organization and search indexing engine, and (3) a
query/reply mechanism between robots and the search engine.
(also see RobotShare)
Technical
Drawing Analysis
Engineering drawing analysis involves the automatic
semantic interpretation of scanned images of engineering drawings; this
involves text extraction and interpretation, dimension set analysis, graphics
extraction, etc. Raster map image analysis involves the automatic semantic
interpretation of map images; in this case, it is necessary to extract roads,
road types, road intersections, waterways, elevation lines, land types, etc.
(also see Viper)