Laboratory for Autonomous Marine Sensing Systems

Director: Prof. Henrik Schmidt

Affiliated Faculty & Staff: Prof. Arthur Baggeroer, Prof. John Leonard, Prof. Pierre Lermusiaux, Dr. Michael Benjamin, Dr. Arjuna Balasuriya, Dr. Donald Eickstedt, Dr. Wenyu Luo, Joseph Curcio

Graduate Students: Kevin Cockrell, Raymond Lum, Costas Pelekanakis, Stephanie Petillo, Toby Schneider

Lab Manager: Wayne Leslie

MIT; 5-223; Center for Ocean Engineering; Dept. of Mechanical Engineering; 77 Massachusetts Avenue; Cambridge, MA 02139; (617) 324-3076

Upcoming and Ongoing:

Ocean Observatories Initiative (OOI) (formerly ORION)

OOI CyberInfrastructure Observing System Simulation Experiment (OSSE)

GLINT09 June/July 2009

PLUSINP09 July/August 2009

Recent Events:

SWAMSI09

GLINT08

CCLNet'08

Henrik Schmidt presentation at the Festival della Scienza in La Spezia, Italy, 25 Oct. 2007 [PPT] [PDF] (in Italian)

Interview of Henrik Schmidt by Italian television [WMV] (for IE only) (in Italian - Henrik's story begins approx. 5:50 into the video and lasts for roughly three minutes)

PLUSNet07: Keyport, Washington; Sept.-Oct. 2007 (Password protected)

The MIT Laboratory for Autonomous Marine Sensing in the Department of Mechanical Engineering is specializing in the development of new distributed ocean sensing concepts for oceanographic science, national defense and coastal management and protection. The laboratory was established in 2005 following the merger of the Mechanical and Ocean Engineering departments. It continues two decades of multi-disciplinary research and development into such systems by Department of Ocean Engineering, and the MIT Sea Grant AUV Laboratory. In addition to the Laboratory Director, Prof. Henrik Schmidt, the research team includes four faculty, Prof. John Leonard, Prof. Daniela Rus, Prof. Arthur Baggeroer, and Prof. Pierre Lermusiaux; four Research Engineers, Dr. Arjuna Balasuriya, Mr. Joseph Curcio, Dr. Donald Eickstedt, and Dr. Michael Benjamin; and one post-doc, Dr. Wenyu Luo. The faculty and staff, together with a significant number of students, provides a strongly multidisciplinary team with expertise in oceanographic sensing and modeling, sonar system technology, computational underwater acoustics, and marine robotics and communication networking.

Bluefin'21 AUVs and autonomous surface craft operated by the Laboratory for
Autonomous Marine Sensing from the NATO R/V Leonardo in July 2005
at Elba Island, Italy.

Five MIT Autonomous Surface Craft in use in the Charles River Test Bed

Major Equipment

The Laboratory owns and operates two state-of-the-art Bluefin'21 autonomous underwater vehicles (AUV), and a fleet of ten autonomous surface craft (ASC), as well as support equipment for their operation, including an acoustic communication bouy, and a Sonadyne long baseline navigation system. The laboratory is operating the AUV and ASC from research vessels, and have been deployed and operated on average once a year during major field experiments. Through joint research agreements with the NATO Undersea Research Centre (NURC) in La Spezia, Italy, the Laboratory has regular access to their two research vessels, and their engineering research staff. The Laboratory operates several payloads for the AUV, including three acoustic sensing payloads for oceanographic mapping, bottom and sub-bottom object detection. Currently, a new payload is being built under ONR DURIP funding, including a highly capable acoustic towed array, intended for littoral undersea surveillance, and other underwater acoustic research.

Charles River ASC Test-bed

Through an agreement with the MIT Sailing Pavilion the Laboratory has established a test-bed for the autonomous surface craft on the Charles River, adjacent to MIT. This Charles River ASC Test-bed is used for smaller scale testing of new adaptive and collaborative sensing concepts. As such it provides a useful intermediate link between the simulation environment and ocean field experiments. The test-bed is used routinely for development and testing of collaborative and adaptive, autonomous control of up to ten ASC using the MOOS-IvP control architecture.

Computing

The laboratory operates a cluster of ten workstations, in addition to a large number of laptop computers used for the field efforts. Most of the computers are operated using the Linux operating system, with the laptops in general having a dual-boot capability in Windows. The computer network includes a central CVS server for the open-source MOOS-IvP autonomous platform control software applied exclusively for the robotic platforms in the Laboratory. It also supports a state-of-the-art underwater acoustic modeling capability available to faculty, staff and students. A specific capability is a comprehensive MOOS-IvP simulation capability, which is used for simulation experiments, which the Laboratory uses extensively in the planning and preparation of field experiments, and for development and testing of new autonomous behaviors and processes. A unique capability of the MOOS-IvP simulator is the full integration of a high-fidelity, real-time underwater acoustic modeling capability, which supports simulation of fully autonomous acoustic sensing missions with AUVs towing hydrophone arrays. This capability is a crucial component of the development of the fully integrated, autonomous sensing, modeling and control concepts being required for distributed underwater surveillance systems with no or extremely limited possibility for operator involvement and intervention.

MOOS-IvP Nested Autonomy Documentation

MOOS-IvP Nested Autonomy Prototype for Distributed Undersea Sensing User’s Guide
MOOS-IvP Undersea Autonomous Network Simulator User’s Guide
MOOS-IvP Autonomous Command and Control for Undersea Network Nodes User's Guide
MOOS-IvP Undersea Network Command and Control Topside User’s Guide

Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS)

With Prof. Pierre Lermusiaux joining MIT in Jan. 2007, and becoming the most recent faculty member to be associated with LAMSS, the Laboratory will add the Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) (formerly known as the Harvard Ocean Prediction System (HOPS)) to its in-house capabilities. The Laboratory faculty has a decade long history of collaborating with the MSEAS group, and most recently, as part of the ONR PLUSNet effort, MSEAS is being closely integrated with the MOOS-IvP simulation environment, further enhancing the Laboratory's capabilities for simulating adaptive and collaborative sensing missions with distributed, fixed and mobile platforms.