Evolving decision-making functions in an autonomous robotic exploration strategy using grammatical evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/iros/IbrahimA13,
  author =       "Mohd Faisal Ibrahim and Bradley James Alexander",
  title =        "Evolving decision-making functions in an autonomous
                 robotic exploration strategy using grammatical
                 evolution",
  booktitle =    "IEEE/RSJ International Conference on Intelligent
                 Robots and Systems (IROS 2013)",
  publisher =    "IEEE",
  year =         "2013",
  month =        nov,
  pages =        "4340--4346",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, control engineering computing, evolutionary
                 computation, path planning, robots, automatic
                 derivation, autonomous robotic exploration, autonomous
                 robotic mapping, control software, decision-making
                 function, ground based exploration platform,
                 navigational control, navigational task, scoring
                 function, collision avoidance, grammar, mobile robots,
                 navigation, power capacitors, power demand",
  bibdate =      "2014-01-10",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/iros/iros2013.html#IbrahimA13",
  URL =          "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6679723",
  DOI =          "doi:10.1109/IROS.2013.6696979",
  ISSN =         "2153-0858",
  abstract =     "Customising navigational control for autonomous
                 robotic mapping platforms is still a challenging task.
                 Control software must simultaneously maximise the area
                 explored whilst maintaining safety and working within
                 the constraints of the platform. Scoring functions to
                 assess navigational options are typically written by
                 hand and manually refined. As navigational tasks become
                 more complex this manual approach is unlikely to yield
                 the best results. In this paper we explore the
                 automatic derivation of a scoring function for a ground
                 based exploration platform. We show that it is possible
                 to derive the entire structure of a scoring function
                 and that allowing structure to evolve yields
                 significant performance advantages over the evolution
                 of embedded constants alone.",
  notes =        "also known as \cite{6696979}",
}

Genetic Programming entries for Mohd Faisal Ibrahim Brad Alexander

Citations