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Marc Steinberg

Aerospace Sciences Division (Code 351)

Office of Naval Research

(703) 696-5115, Marc.Steinberg@navy.mil

Naval Intelligent Naval Intelligent Autonomy UpdateAutonomy UpdateNaval Intelligent Naval Intelligent

Autonomy UpdateAutonomy Update

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Intelligent Autonomy VisionIntelligent 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

-Make unmanned systems as smart as animals in some respects-Operate in challenging weather & exploit environmental conditions -Collaborate in close proximity to others

-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

-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

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Biologically Inspired Approaches for Biologically Inspired Approaches for Unmanned System Team & Coalition Unmanned System Team & Coalition

Formation – Behavior-Based ApproachFormation – 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)-MIT (Acemoglu)

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Biologically Inspired Approaches for Biologically Inspired Approaches for Unmanned System Team & Coalition Unmanned System Team & Coalition

Formation – Cognitive-Based ApproachFormation – 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.

– 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

• Mathematical modeling in support of satisficing algorithms for dynamic teaming and coalition forming

– 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

•Dartmouth (Granger, Kralik, Ray, Santos)•MIT (Breazeal)•USC (Sukhatme)

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Air Volume Capacity and Time-Critical Air Volume Capacity and Time-Critical Coordination of Multiple UAVsCoordination 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

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Automated Sensing – Planning & Automated Sensing – Planning & ManagementManagement

• 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 UAV 2UAV 1

CollaborationLayer Cooperative

planning

Task Execution

Layer

task 0

CollaborationLayer

Task Execution

Layer

task 0

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Collaborative & Shared Control of Collaborative & Shared Control of Unmanned Systems 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 co-located 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)

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Affect-Based Control & Human-Directed Affect-Based Control & Human-Directed Learning of Unmanned Systems 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)

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Human-Robotic Interaction 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

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, Subbarao Kambhampati, Mike McBeath, Pat Langley)

Peer-to-Peer Human-Robot Team

RemoteCommander

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Other New Efforts for FY09Other 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