1. Ocean Observatories Initiative OOI CI Kick-Off Meeting Devils 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

2. OOI CI Kick-Off Meeting, Sept 9-11, 20092 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

3. Nested Autonomy OOI CI Kick-Off Meeting, Sept 9-11, 20093  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 OO Shore E 80bps CCL

4. Nested Autonomy Implementation OOI CI Kick-Off Meeting, Sept 9-11, 20094 Backseat Driver Paradigm - ASTM F41 Autonomy System as a 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 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. Autonomous Decision- Making Payload Computer Main Vehicle Computer MOOS IvP Helm

5. What is MOOS & MOOS-IvP? OOI CI Kick-Off Meeting, Sept 9-11, 20095 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:

6. MOOS OOI CI Kick-Off Meeting, Sept 9-11, 20096 Autonomy System as a 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 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. Autonomous Decision- Making Payload Computer Main Vehicle Computer MOOS IvP Helm module MOOS Core MOOS Modules coordinated through a publish and subscribe interface. Overall system is built incrementally. The “glue” for the autonomy system as a whole. module MOOSDB Publish Subscribe

7. MOOS-IvP OOI CI Kick-Off Meeting, Sept 9-11, 20097 Autonomy System as a 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 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. Autonomous Decision- Making Payload Computer Main Vehicle Computer MOOS 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 engine behavior IvP Core behavior IvP Solver Objective function Sensor Info

8. Closer Look at MOOS-IvP OOI CI Kick-Off Meeting, Sept 9-11, 20098 Waypoint Controlled Vehicle Obstacle Vehicle 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.

9. Rapid Environmental Assessment OOI CI Kick-Off Meeting, Sept 9-11, 20099 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

10. OOI CI Kick-Off Meeting, Sept 9-11, 200910 Thanks !