A Simulation Based Framework for Discovering Planning Logic for Autonomous Unmanned Surface Vehicles

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Petr Svec and Max Schwartz and Atul Thakur and 
                 Davinder K. Anand and Satyandra K. Gupta",
  title =        "A Simulation Based Framework for Discovering Planning
                 Logic for Autonomous Unmanned Surface Vehicles",
  booktitle =    "ASME 2010 10th Biennial Conference on Engineering
                 Systems Design and Analysis,",
  year =         "2010",
  editor =       "Nilufer Egrican and Memis Acar",
  volume =       "3 Mechanisms and Robotics",
  pages =        "711--720",
  address =      "Istanbul",
  month =        jul # " 12-14",
  publisher =    "ASME",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-7918-4917-0",
  DOI =          "doi:10.1115/ESDA2010-24894",
  abstract =     "This paper describes a computational framework for
                 automatically synthesising planning logic for unmanned
                 surface vehicles (USVs). The basic idea behind our
                 approach is as follows. The USV explores the virtual
                 environment by randomly trying different moves. USV
                 moves are simulated in the virtual environment and
                 evaluated based on their ability to make progress
                 towards the mission goal. If a successful action is
                 identified as a part of the random exploration, then
                 this action is integrated into the logic driving the
                 USV. This approach has been used for automatically
                 generating planning logic for USVs. The planning logic
                 is represented as a decision tree which consists of
                 high-level controllers as building blocks, conditionals
                 and other program constructs. We used strongly-typed
                 GP-based evolutionary framework for automatic
                 generation of planning logic for blocking the
                 advancement of a computer-driven intruder boat toward a
                 valuable target. Our results show that a genetic
                 programming based synthesis framework is capable of
                 generating decision trees expressing useful logic for
                 blocking the advancements of an enemy boat.",
  notes =        "http://www.asmeconferences.org/esda2010/ University of
                 Maryland, College Park, MD


Genetic Programming entries for Petr Svec Max Schwartz Atul Thakur Davinder K Anand Satyandra K Gupta