Naval Intelligent Autonomy
Update
Marc Steinberg
Aerospace Sciences Division (Code 351)
Office of Naval Research
(703) 696-5115, Marc.Steinberg@navy.mil
1
Intelligent Autonomy Vision
-Reduce manning requirements
(numbers, skills, training) to manage a
family of unmanned systems
-Support the ability to share assets,
collaborate, and get unmanned system
services to the tactical edge
-Allow operators to manage
heterogeneous unmanned systems at a
mission level (individual, team, coalition)
-Airspace/Waterspace management to
allow for operation in close proximity to
manned & other unmanned systems
-Improve robustness & reduce need
for substantial human intervention to
maintain performance in unplanned or
unexpected situations
-Develop tools, methodologies, testing
approaches to better predict
autonomous behavior
-Make unmanned systems as smart as
animals in some respects
-Operate in challenging weather &
exploit environmental conditions
2
-Collaborate in close proximity to others
Biologically Inspired Approaches for
Unmanned System Team & Coalition
Formation – Behavior-Based Approach
• New effort started in July
• Cataloging, modeling, and analysis of
biological behaviors related to
predator-prey relationships among
intelligent social animals
• Biologically-inspired
heterogeneous cooperation
• Cooperative behaviors in
communications degraded
environments
• Distributed versus centralized
optimization for networked control
• Embedded humans
• Experimentation and Validation
•UPenn (Pappas, Kumar, Jadbabaie,
Koditschek)
•Georgia Tech (Arkin,Balch, Egerstedt)
•UC, Berkeley (Tomlin, Hedrick, Sastr
•ASU (S. Pratt)
•Biological “Think Tank”
-UPenn (White)
-Univ of Washington (Parrish)
-Michigan Tech (Vucetich)
-Yale (Skelly)
3
-MIT (Acemoglu)
Biologically Inspired Approaches for
Unmanned System Team & Coalition
Formation – Cognitive-Based Approach
•
•
New effort started in July
Development of dynamic teaming and coalition
forming theories rooted in modular social
intelligence exhibited by high-functioning
mammals.
–
–
–
–
–
–
•
Mathematical modeling in support of satisficing
algorithms for dynamic teaming and coalition
forming
–
–
–
–
•
Identification of relevant social intelligence models
Teaming and coalition forming through shared
cognitive abilities
Cognitive Modeling
Coding of relevant teaming parameters, utility
functions, and costs of coalition membership
Satisficing in intractable domains
Human role
•Dartmouth (Granger, Kralik, Ray,
Santos)
•MIT (Breazeal)
•USC (Sukhatme)
Biologically-derived models of individual ACAs
Mathematical models of systems of ACAs and
individual ACA subsystems
Robustness/fault tolerance
Evaluation of information exchange
Performance Evaluation
–
–
Simulation-based evaluation
Physical demonstration
4
Air Volume Capacity and Time-Critical
Coordination of Multiple UAVs
• New effort with Naval Postgraduate School (Kaminer, Dobrokhodov,
Jones) and Univ. of Illinois (Hovakimyan)
• Real-time generation of the multiple collaborative paths that support
safe multi-UAS operations in crowded airspaces.
– For pre-defined airspace volume and a number of given mission tasks,
determine the appropriate number of UAVs and generate feasible paths
for these UAVs that satisfy airspace constraints, guarantee deconfliction
and meet the mission requirements both spatially and temporally.
• Development of nonlinear path following control laws that can follow
aggressive sensor data collection trajectories despite the use of
existing conventional autopilots for the inner loop control.
– Greater robustness when following predefined trajectories designed to
put the platform in a position to collect the appropriate sensor data in
the presence of environmental disturbances
– Augment an inner-loop autopilot using L1 adaptive control theory.
• Flight Testing at Camp Roberts
5
Automated Sensing – Planning &
Management
• U.C., Berkeley
• Collaborative sensing
language and distributed
control algorithms to support
tasking of unmanned air
systems based on high-level
commands
• Collaborative sensing
language for tasking vehicles
based on a petrii net-like
approach
• Completed multi-UAV Flight
Test
– Cooperative search and
localization
– System tasked remotely through
a web-based interface
Collaboration
Layer
Cooperative
planning
Collaboration
Layer
task 0
task 0
Task Execution
Layer
Task Execution
Layer
UAV 1
UAV 2 6
Collaborative & Shared Control of
Unmanned Systems
• Beginning Phase II SBIR, Phase II
STTR, & Transition program for prior
STTR Phase II
• Autonomy & Human Interface
technology to support small teams of colocated and distributed users in
managing larger number of unmanned
systems & sharing unmanned systems
resources
– Autonomy, decision aids, and situation
awareness tools to support collaborative
decision-making among teams of
operators and unmanned systems
– Distributed control & optimization
algorithms to share services with users in
small units.
– Trend & Configural Displays to simplify
human interaction for small unit users
Performers under different efforts
– Charles River Analytics/MIT
(Dr. Missy Cummings)
– Perceptronics/CRA/USC (Dr.
Milind Tambe)/MIT (Dr. Missy
Cummings)
– Aurora/MIT (Dr. Jon How, Dr.
Missy Cummings)
7
Affect-Based Control & Human-Directed
Learning of Unmanned Systems
• Beginning Phase II SBIR & Phase II
STTR
• Enable human operators to provide
direction to unmanned air systems to
change behaviors and support adapting
or learning new tactical behaviors of
interest
– Control approaches that draw on
biologically-based theories of cognition,
personality, and affect as an initial
inspiration, but then develop them within
a more conventional engineering
framework
– Dynamically modify autonomous
behavior in future unmanned systems in
a manner which is easily understandable
and controllable by human operators and
users
• Learning approaches that support
human-direction
Performers under different efforts
– Chi Systems/USC (Dr. Lynn
Miller & Stephen Reed)
– Aptima/CERI(Dr. Nancy Cooke)
8
Human-Robotic Interaction
• Flexible, robust & scalable
human-robot teams in dynamic
and uncertain environments
– Integration of cognitive models,
action schemas and statistical
estimation.
– Integration of behavior models
and distributed control.
• Assigning tasks with different,
potentially incompatible goals &
enabling UVs to execute them
• Robust NLP under time pressure
• Learning by instruction during task
execution – learn new skills and
solve problems on the fly during
task execution
Peer-to-Peer Human-Robot
Team
Remote
Commander
Performers under 2 efforts
– MIT (Cynthia Breazeal, Deb Roy, Nick Roy,
John How) , Vanderbilt (Julie Adams),
UMASS (Rod Grupen) Univ. Washington
(Dieter Fox), Stanford (Pam Hinds)
– Indiana (Matthias Scheutz), Notre Dame
(Kathleen Eberhard), Stanford (Stanley
Peters), ASU (Chitta Baral, Subbarao9
Kambhampati, Mike McBeath, Pat Langley)
Other New Efforts for FY09
• Multiple ONR autonomy-related FY09
Multidisciplinary University Research Topic
– Highly Decentralized Autonomous Systems for
Force Protection and Damage Control
– Bio-inspired Autonomous Agile Sensing and
Exploitation of Regions of Interest within Wide
Complex Scenes
– Computational Intelligence for Decentralized
Teams of Autonomous Agents
– Machine Intelligence and Adaptive
Classification for Autonomous Systems
10