Ocean Observatories Initiative

OOI CI Kick-Off MeetingDevils Thumb Ranch, Colorado

September 9-11, 2009

Autonomous Marine Sensing and Control

Arjuna Balasuriya, Stephanie Petillo, Mike Benjamin and Henrik Schmidt

Laboratory for Autonomous Marine Sensing Systems,

Massachusetts Institute of Technology

OOI CI Kick-Off Meeting, Sept 9-11, 2009 2

Underwater Distributed Network• Drawbacks

– Uncertain Communication• Low bandwidth

• Intermittency

• Latency

– Uncertain Navigation• No maps

• No navigation infrastructure

– Uncertain acoustic sensing• Smaller apertures

• Spatial and Temporal Variability

• Benefits– Autonomous adaptivity

• Environment• Tactical situation

– Multi-platform Collaboration• Sensing• Processing• Control

– Redundancy• Sensors• Platforms

Nested Autonomy Architecture

Nested Autonomy

OOI CI Kick-Off Meeting, Sept 9-11, 2009 3

Cluster activated by Shore Station Environmental updates – Planner or by an event

AUVs & gliders approach the ROI. Adaptively sense and exploit environment.

Detect features and adaptively manoeuvre to resolve ambiguity for optimal tracking and localization (DLT)

Collaborative Localization and Tracking (LT). Cluster formations

Transmit updated, enhanced measurement messages. Environmental updates in real-time.

E

Feature Tracks

Feature Tracks

OOShore

E

80bpsCCL

Nested Autonomy Implementation

OOI CI Kick-Off Meeting, Sept 9-11, 2009 4

Backseat Driver Paradigm - ASTM F41

Autonomy System asa Whole

Control and Navigation System

Three components of the overall vehicle architecture.

•Control and Navigation (frontseat driver)Actuator control, inertial navigation, GPS,compass, DVL, dead-reckoning systems, vehicle safety.

•Autonomy System as a WholeSensor processing, sensor fusion, autonomy, contact management, data logging, system monitoring, mission control, communication.

•Autonomous Decision-Making (backseat driver)Deciding vehicle heading, speed, and depth.

Autonomous Decision-Making

PayloadComputer

Main VehicleComputer

MOOSIvP Helm

What is MOOS & MOOS-IvP?

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• MOOS-IvP is an Open Source project developing autonomy software for marine vehicles.

• It is a collaboration between NUWC, MIT, and Oxford University - funded by ONR, 311.

• MOOS is autonomy middleware developed and distributed by Oxford University.

• The IvP Helm is a set of autonomy modules developed by NUWC (Code 25) and MIT.

• 100,000+ lines of public code, 100,000+ lines of non-public code.

Platforms with MOOS-IvP: (Several others with just MOOS)

Bluefin 21” UUV Iver2 UUV Auton. kayaks OEX 21” UUV REMUS-100 REMUS-600 NURC USV

• NSF – OOI-CI

• ONR - PLUS INP, UCCI, SWAMSI, GOATS

• SBIR - Robotic Marine Systems, SARA (OSD and Air Force), Rite Solutions

• NURC – NATO 4G4 program, others.

Programs:

MOOS

OOI CI Kick-Off Meeting, Sept 9-11, 2009 6

Autonomy System asa Whole

Control and Navigation System

Three components of the overall vehicle architecture.

• Control and Navigation (frontseat driver)Actuator control, inertial navigation, GPS, compass, DVL, dead-reckoning systems, vehicle safety.

• Autonomy System as a WholeSensor processing, sensor fusion, autonomy, contact management, data logging, system monitoring, mission control, communication.

• Autonomous Decision-Making (backseat driver)Deciding vehicle heading, speed, and depth.

Autonomous Decision-Making

PayloadComputer

Main VehicleComputer

MOOSIvP Helm

module

module

module

module

module

module

module

module

module

module

MOOSCore

MOOS• Modules coordinated through a publish and

subscribe interface. • Overall system is built incrementally.

The “glue” for the autonomy system as a whole.

module MOOSDBPublish

Subscribe

MOOS-IvP

OOI CI Kick-Off Meeting, Sept 9-11, 2009 7

Autonomy System asa Whole

Control and Navigation System

Three components of the overall vehicle architecture.

• Control and Navigation (frontseat driver)Actuator control, inertial navigation, GPS, compass, DVL, dead-reckoning systems, vehicle safety.

• Autonomy System as a WholeSensor processing, sensor fusion, autonomy, contact management, data logging, system monitoring, mission control, communication.

• Autonomous Decision-Making (backseat driver)Deciding vehicle heading, speed, and depth.

Autonomous Decision-Making

PayloadComputer

Main VehicleComputer

MOOSIvP Helm

IvP Helm

• Modules coordinated through logic (behavior algebra), objective functions and multi-objective optimization.

• Overall system is built incrementally.

The “glue” for the autonomous decision-making enginebehavior

behavior

behavior

behavior

behavior

behavior

behavior

behavior

behavior

behavior

IvPCore

behavior IvP Solver

Objective function

Sensor Info

Closer Look at MOOS-IvP

OOI CI Kick-Off Meeting, Sept 9-11, 2009 8

Waypoint

Controlled Vehicle

ObstacleVehicle

1. Messages are read from the MOOSDB.

2. The Info Buffer is updated for access by all behaviors.

3. Behaviors are queried for output if any.

4. The IvP Solver reconciles behavior output.

5. Messages are posted back to the MOOSDB.

Rapid Environmental Assessment

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Example: Thermocline Tracking

• An adaptive environmental sampling and tracking behavior employing Rapid Environmental Assessment

• Calculates and monitors the changing temperature gradient through the water column, based on the average local depth vs. temperature profile

• AUV or glider will autonomously adapt the depth range of its yo-yo to more closely sample and track a thermocline

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Thanks !