Soft computing-based design and control for mobile robot path tracking

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

@InProceedings{Homaifar:1999:CIRA,
  author =       "Abdollah Homaifar and Daryl Battle and 
                 Edward Tunstel",
  title =        "Soft computing-based design and control for mobile
                 robot path tracking",
  booktitle =    "Computational Intelligence in Robotics and Automation,
                 CIRA '99. Proceedings. 1999 IEEE International
                 Symposium on",
  year =         "1999",
  pages =        "35--40",
  month =        "8-9 " # nov,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, soft computing-based design, mobile robot,
                 robot path tracking, evolutionary algorithms, Darwinian
                 concepts, automatic learning, nonlinear mappings,
                 genetic programming, fuzzy control rules, autonomous
                 vehicle, steering control problem, membership
                 functions, rule bases, robustness, sensor measurement
                 noise, nominal forward velocity",
  ISBN =         "0-7803-5806-6",
  URL =          "http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587",
  DOI =          "doi:10.1109/CIRA.1999.809943",
  size =         "6 pages",
  abstract =     "A variety of evolutionary algorithms, operating
                 according to Darwinian concepts, have been proposed to
                 approximately solve problems of common engineering
                 applications. Increasingly common applications involve
                 automatic learning of nonlinear mappings that govern
                 the behavior of control systems. In many cases where
                 robot control is of primary concern, the systems used
                 to demonstrate the effectiveness of evolutionary
                 algorithms often do not represent practical robotic
                 systems. In this paper, genetic programming (GP) is the
                 evolutionary strategy of interest. It is applied to
                 learn fuzzy control rules for a practical autonomous
                 vehicle steering control problem, namely, path
                 tracking. GP handles the simultaneous evolution of
                 membership functions and rule bases for the fuzzy path
                 tracker. As a matter of practicality, robustness of the
                 genetically evolved fuzzy controller is demonstrated by
                 examining the effects of sensor measurement noise and
                 an increase in the robot's nominal forward velocity.",
  notes =        "CIRA'99 http://web.nps.navy.mil/~yun/cira99/",
}

Genetic Programming entries for Abdollah Homaifar Daryl Battle Edward W Tunstel

Citations