Genetic Programming Approach for Autonomous Vehicles

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

@InProceedings{Mechatronics2004_Abstract_026,
  author =       "Miha Kovacic and Miran Brezocnik and Joze Balic",
  title =        "Genetic Programming Approach for Autonomous Vehicles",
  booktitle =    "Mechatronics 2004 9th Mechatronics Forum International
                 Conference",
  year =         "2004",
  address =      "METU, Ankara, Turkey",
  month =        "30 " # aug # "-1 " # sep,
  organisation = "Atilim University",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://mechatronics.atilim.edu.tr/mechatronics2004/papers/Mechatronics2004_Abstract_026.pdf",
  size =         "1 page",
  abstract =     "GP was used for intelligent path planning of an
                 autonomous vehicle in 2D production environment. Robot
                 had to find loads, to avoid all the obstacles and to
                 reach the target point. The production environment
                 (robot, loads and obstacles) are represented as free 2D
                 shapes. The robot discretely rotates for 30 degrees
                 left and right and moves forward by two different
                 steps. Step decreases if the sensor detects the load or
                 obstacle. The GP system tries to find gradually optimal
                 program for robot navigation through production
                 environment as a consequence of interactions between
                 the robot and detected environment. Program for
                 navigation can be randomly constructed of logical
                 operators (IFLOAD, IF-OBSTACLE), basic commands (MOVE,
                 RIGHT, LEFT), and connection functions (CF2, CF3).

                 Each program is run several times until 100 time units
                 for the robot's task are used or the target point is
                 reached. The system for genetic programming was run
                 50-times. Robot travelled safely with all collected
                 loads to the target point 2-times, which means that the
                 probability of the finding successful navigation
                 program is 4 percent. In future the researches will be
                 oriented particularly towards conceiving an improved GP
                 system with the possibility of use 3D models of the
                 production environment. Preliminary results of the
                 concept are encouraging.",
  notes =        "University of Maribor, Faculty of Mechanical
                 Engineering, Maribor, Slovenia",
}

Genetic Programming entries for Miha Kovacic Miran Brezocnik Joze Balic

Citations