Autonomous Marine Sensing and Control

1. Autonomous Marine Sensing and Control Arjuna Balasuriya, Stephanie Petillo, Mike Benjamin and Henrik Schmidt Laboratory for Autonomous Marine Sensing Systems, Massachusetts Institute of Technology

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

3. Feature Tracks E E OO Shore Feature Tracks Nested Autonomy • Cluster activated by Shore StationEnvironmental 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. 80bps CCL OOI CI Kick-Off Meeting, Sept 9-11, 2009

4. Nested Autonomy Implementation Backseat Driver Paradigm - ASTM F41 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 Whole Sensor processing, sensor fusion, autonomy, contact management, data logging, system monitoring, mission control, communication. • Autonomous Decision-Making (backseat driver) Deciding vehicle heading, speed, and depth. Autonomy System as a Whole Autonomous Decision- Making Payload Computer MOOS IvP Helm Main Vehicle Computer Control and Navigation System OOI CI Kick-Off Meeting, Sept 9-11, 2009

5. What is MOOS & MOOS-IvP? • 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 NURC USV Auton. kayaks OEX 21” UUV REMUS-100 REMUS-600 Programs: • 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. OOI CI Kick-Off Meeting, Sept 9-11, 2009

6. module module MOOS 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 Whole Sensor processing, sensor fusion, autonomy, contact management, data logging, system monitoring, mission control, communication. • Autonomous Decision-Making (backseat driver) Deciding vehicle heading, speed, and depth. Autonomy System as a Whole Autonomous Decision- Making Payload Computer MOOS IvP Helm Main Vehicle Computer Control and Navigation System module The “glue” for the autonomy system as a whole. MOOS module module • Modules coordinated through a publish and subscribe interface. • Overall system is built incrementally. module module MOOS Core module Publish MOOSDB module module Subscribe module OOI CI Kick-Off Meeting, Sept 9-11, 2009

7. behavior MOOS-IvP 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 Whole Sensor processing, sensor fusion, autonomy, contact management, data logging, system monitoring, mission control, communication. • Autonomous Decision-Making (backseat driver) Deciding vehicle heading, speed, and depth. Autonomy System as a Whole Autonomous Decision- Making Payload Computer MOOS IvP Helm Main Vehicle Computer Control and Navigation System IvP Helm The “glue” for the autonomous decision-making engine behavior behavior behavior • Modules coordinated through logic (behavior algebra), objective functions and multi-objective optimization. • Overall system is built incrementally. behavior behavior IvP Core Objective function behavior behavior IvP Solver behavior behavior behavior Sensor Info OOI CI Kick-Off Meeting, Sept 9-11, 2009

8. Closer Look at MOOS-IvP Obstacle Vehicle Waypoint • Messages are read from the MOOSDB. • The Info Buffer is updated for access by all behaviors. • Behaviors are queried for output if any. • The IvP Solver reconciles behavior output. • Messages are posted back to the MOOSDB. Controlled Vehicle OOI CI Kick-Off Meeting, Sept 9-11, 2009

9. Rapid Environmental Assessment 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 OOI CI Kick-Off Meeting, Sept 9-11, 2009

10. Thanks ! OOI CI Kick-Off Meeting, Sept 9-11, 2009