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Autonomous Marine Intelligent Swarming Systems

for Interdisciplinary Observing Networks (A-MISSION)

P.F.J. Lermusiaux,
P.J. Haley, Jr., T. Lolla,
M.P. Ueckermann, W.G. Leslie,
C. Mirabito

Massachusetts Institute of Technology
Center for Ocean Engineering
Mechanical Engineering
Cambridge, Massachusetts

Ongoing MIT research
Additional A-MISSION Links
Presentations
Background information

 

MSEAS A-MISSION supported publications
This research sponsored by the Science of Autonomy Program - Office of Naval Research.

Ongoing MIT research

Our research is driven by the following five objectives:
  1. Research autonomous sensing swarms and formations that exploit the multi-scale, multivariate, four-dimensional environmental-acoustic marine dynamics and predictabilities
  2. Utilize swarming schemes based on control theory, dynamical system theory, artificial intelligence and bio-inspired behaviors, and update them so that in the high-level global optimization, data to be collected affect predictions and feedback to the optimal autonomy
  3. Combine the swarming schemes with our adaptive schemes which forecast the impact of future data to define the optimal autonomy
  4. Develop new schemes and compare them step-by-step, in idealized and realistic simulations
  5. Motivate our fundamental research based efficiency and robustness for optimal for naval operations, undersea surveillance, homeland security and coastal protection

Collected research and reference papers are found here (password protected - email wgleslie at mit dot edu for information).

 

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Additional A-MISSION Links

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Presentations

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Background information

The thrust and scope of our effort is to develop new principled formalisms and methodologies for optimal marine sensing using collaborative swarms of autonomous platforms (AUVs, gliders, ships, moorings and remote sensing platforms) that are smart, i.e. knowledgeable about the predicted environment, acoustic performance and uncertainties, and about the predicted effects of their sensing. Our research focus areas are Autonomous perception and intelligent decision making and Scalable and robust distributed collaboration. Specifically, our work includes research components on: tasking the placement of sensory and computational resources; agile searching; adaptation of algorithms; multi-task learning across multiple sensor types; task allocation, planning and coordination for heterogeneous systems; and structuring autonomy to balance competing tasks. We also involve the evaluation of uncertain a priori information for decision making; supervisory control of autonomous systems; and methods for acquiring and synthesizing information from multiple sources. Basic automated architectures are also employed for efficient integration of sensing, planning, and control of autonomous systems.

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