Adaptive Control of Autonomous Underwater Vehicles
Principal Researcher for Autonomy
Monterey Bay Aquarium Research Institute (MBARI)
Ocean Science the world over is at a cusp, with a move from the Expeditionary to the Observatory mode of doing science. With the advent of ocean observatories, a number of key technologies have proven to be promising for sustained ocean presence. Mobile robots routinely map the benthic environment and sample the water-column up to depths of 6000 meters while tele-operated vehicles navigate remote depths, performing scientific experiments in-situ relating to biogeochemical processes. Such platforms, however, have inherent limitations with how they are commanded and operated; pre-defined sequences of commands are currently used to determine what actions the robot will perform and when, irrespective of the contextual environment in which it operates. As a consequence, not only can robots not recover from unforeseen failure conditions, they are unable to significantly leverage their substantial onboard assets to do opportunistic science.
To mitigate such shortcomings, we are developing new techniques to dynamically command Autonomous Underwater Vehicles (AUV). Our efforts use a blend of generative and deliberative Artificial Intelligence Planning and Execution techniques to shed goals, introspectively analyze onboard resources and recover from failures. In addition, we are working on clustering techniques to adaptively trigger science instruments that will contextually sample the seas as driven by scientific intent. The end goal is to enable unstructured exploration of subsea environments that are a rich trove of problems for autonomous systems. Our work builds on research efforts at NASA to command deep space probes and Mars rovers, and transfers lessons into the oceanic domain. In this talk I will articulate the challenges of working in the hostile underwater domain, lay out the differences relative to space applications, and motivate our architecture for goal-driven autonomy on AUV's.
Kanna is the Principal Researcher in Autonomy at the Monterey Bay Aquarium Research Institute (http://www.mbari.org) a privately funded non-profit Oceanographic institute which he joined in October 2005. Prior to that he was a Senior Research Scientist and a member of the management team of the 95 member Autonomous Systems and Robotics Area at NASA Ames Research Center Moffett Field, California.
At Ames, Kanna was the Principal Investigator of the MAPGEN Mixed-Initiative Planning effort to command and control the Spirit and Opportunity rovers on the surface of the Red Planet. He was one of the six principals of the Remote Agent Experiment (RAX) team, which designed, built, tested and flew the first closed-loop AI based control system on a spacecraft. The RA was the co-winner of NASA's 1999 Software of the Year, the agency's highest technical award (http://ic.arc.nasa.gov/projects/remote-agent/). Kanna's interests are in automated Planning/Scheduling, modeling and representation for real world planners and agent architectures for Distributed Control applications.
Date: Tuesday, Octoboer 28, 2008
Place: Nora Suppes Hall, Conference Room
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