Incremental Evolutionary Methods for Automatic Programming of Robot Controllers

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Petrovic:thesis,
  author =       "Pavel Petrovic",
  title =        "Incremental Evolutionary Methods for Automatic
                 Programming of Robot Controllers",
  school =       "Norwegian University of Science and Technology,
                 Faculty of Information Technology, Mathematics and
                 Electrical Engineering",
  year =         "2007",
  type =         "PhD in Information and Communications Technology",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, behavior
                 arbitration, finite state automata, evolutionary
                 robotics, incremental evolution",
  URL =          "http://ntnu.diva-portal.org/smash/get/diva2:122983/FULLTEXT01.pdf",
  URL =          "http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1748",
  isbn13 =       "978-82-471-5031-3",
  size =         "274 pages",
  abstract =     "The aim of the main work in the thesis is to
                 investigate Incremental Evolution methods for designing
                 a suitable behavior arbitration mechanism for
                 behavior-based (BB) robot controllers for autonomous
                 mobile robots performing tasks of higher complexity.
                 The challenge of designing effective controllers for
                 autonomous mobile robots has been intensely studied for
                 few decades. Control Theory studies the fundamental
                 control principles of robotic systems. However, the
                 technological progress allows, and the needs of
                 advanced manufacturing, service, entertainment,
                 educational, and mission tasks require features beyond
                 the scope of the standard functionality and basic
                 control. Artificial Intelligence has traditionally
                 looked upon the problem of designing robotics systems
                 from the high-level and top-down perspective: given a
                 working robotic device, how can it be equipped with an
                 intelligent controller. Later approaches advocated for
                 better robustness, modifiability, and control due to a
                 bottom-up layered incremental controller and robot
                 building (Behavior-Based Robotics, BBR). Still, the
                 complexity of programming such system often requires
                 manual work of engineers. Automatic methods might lead
                 to systems that perform task on demand without the need
                 of expert robot programmer. In addition, a robot
                 programmer cannot predict all the possible situations
                 in the robotic applications. Automatic programming
                 methods may provide flexibility and adaptability of the
                 robotic products with respect to the task performed.
                 One possible approach to automatic design of robot
                 controllers is Evolutionary Robotics (ER). Most of the
                 experiments performed in the field of ER have achieved
                 successful learning of target task, while the tasks
                 were of limited complexity. This work is a marriage of
                 incremental idea from the BBR and automatic programming
                 of controllers using ER. Incremental Evolution allows
                 automatic programming of robots for more complex tasks
                 by providing a gentle and easy-to understand support by
                 expert knowledge division of the target task into
                 sub-tasks. We analyze different types of
                 incrementality, devise new controller architecture,
                 implement an original simulator compatible with
                 hardware, and test it with various incremental
                 evolution tasks for real robots. We build up our
                 experimental field through studies of experimental and
                 educational robotics systems, evolutionary design,
                 distributed computation that provides the required
                 processing power, and robotics applications. University
                 research is tightly coupled with education. Combining
                 the robotics research with educational applications is
                 both a useful consequence as well as a way of
                 satisfying the necessary condition of the need of
                 underlying application domain where the research work
                 can both reflect and base itself.",
  notes =        "Supervisor: Keith Downing, Agnar Aamodt",
}

Genetic Programming entries for Pavel Petrovic

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