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Marine Robot Autonomy pp 91–175Cite as

Towards Deliberative Control in Marine Robotics

Abstract

We describe a general purpose artificial-intelligence-based control architecture that incorporates in situ decision making for autonomous underwater vehicles (AUVs). The Teleo-reactive executive (T-REX) framework deliberates about future states, plans for actions, and executes generated activities while monitoring plans for anomalous conditions. Plans are no longer scripted a priori but synthesized onboard with high-level directives instead of low-level commands. Further, the architecture uses multiple control loops for a “divide-and-conquer” problem-solving strategy allowing for incremental computational model building, robust and focused failure recovery, ease of software development, and ability to use legacy or nonnative computational paradigms. Vehicle adaptation and sampling occurs in situ with additional modules which can be selectively used depending on the application in focus. Abstraction in problem solving allows different applications to be programmed relatively easily, with little to no changes to the core search engine, thereby making software engineering sustainable. The representational ability to deal with time and resources coupled with Machine Learning techniques for event detection allows balancing shorter term benefits with longer term needs, an important need as AUV hardware becomes more robust allowing persistent ocean sampling and observation. T-REX is in regular operational use at MBARI, providing scientists a new tool to sample and observe the dynamic coastal ocean.

Keywords

  • Planning Horizon
  • Plan Execution
  • Partial Plan
  • Internal State Variable
  • Automate Planning

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    We use the terms “planning” and “deliberation” interchangeably in this chapter.

  2. 2.

    It is important to remember that these principles are broad much as Dwight Eisenhower is reputed to have said In preparing for battle I have always found that plans are useless, but planning is indispensable and Failing to plan is planning to fail.

  3. 3.

    By situated we emphasize that an agent is embedded within a physical robot.

  4. 4.

    This was the first (and to our knowledge only) software ever to be written in LISP to be flown in space.

  5. 5.

    A planning algorithm is sound if invoked on a problem P returns a plan which is a solution for P.

  6. 6.

    A planning algorithm is complete if invoked on a solvable problem P is guaranteed to return a solution.

  7. 7.

    A partial plan is a subsequence of a plan which can be refined into a plan structure.

  8. 8.

    Variability in the water column is along the vertical dimension. Since the Dorado platform can only move forward, the Yo–Yo pattern is the most efficient mechanism for studying water column properties.

  9. 9.

    In such an event even as the planner works towards goal satisfaction, the initial conditions leading to achieve that goal are no longer valid. The planner then tries to achieve a different goal which too has to be discarded similarly. And so on.

  10. 10.

    Note that while it is highly recommended to select a sound value, a failure to produce a plan within a chosen value for this parameter is not considered as a critical failure of the reactor.

  11. 11.

    The world modeled by the plan domain.

  12. 12.

    The current implementation of T-REX is running on a single process and it is the responsibility of the agent itself to emulate reactor multi-threading for deliberation and synchronization.

  13. 13.

    The call stack refers to the sequence of recursive function calls.

  14. 14.

    However, if the flaws related to this goal were resolved, the removal of this goal may likely create new flaws.

  15. 15.

    INLs are fluid sheets of suspended particulate matter that originate from the sea floor [124].

  16. 16.

    Often post-hoc reconstruction of the data set for visualization drives how much adaptation the vehicle can be allowed to undertake.

  17. 17.

    In our September 2010 experiment [137] as the drifter moved over the Davidson Seamount [138] deviation in the California current, resulted in visible directional change that can be seen in Fig. 3.34b near the end of the mission.

  18. 18.

    The MAPGEN system uses the same EUROPA planner used in T-REX. It continues to be used routinely to this day on mission-critical uplink process for the MER mission.

  19. 19.

    Walter Munk of the Scripps Institute of Oceanography has famously stated “Most of the previous century could be called a century of undersampling”—Testimony to the U.S. Commission On Ocean Policy, 18 April 2002.

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Authors and Affiliations

  1. Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA

    Kanna Rajan & Frédéric Py

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Javier Barreiro

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  1. Kanna Rajan
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  2. Frédéric Py
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  3. Javier Barreiro
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Correspondence to Kanna Rajan .

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  1. , Department of Mechanical Engineering, Dalhousie University, 1360 Barrington St., Halifax, Nova Scotia, Canada

    Mae L. Seto

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Rajan, K., Py, F., Barreiro, J. (2013). Towards Deliberative Control in Marine Robotics. In: Seto, M. (eds) Marine Robot Autonomy. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5659-9_3

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