Vision-Based Obstacle Avoidance: A Coevolutionary Approach

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

@Misc{browne:1996:bsc,
  author =       "David Browne",
  title =        "Vision-Based Obstacle Avoidance: A Coevolutionary
                 Approach",
  school =       "Department of Software Development, Monash
                 University",
  year =         "1996",
  type =         "Bachelor of Computing with Honours",
  address =      "Australia",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.csse.monash.edu.au/hons/projects/1996/David.Browne/",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/browne/browne_thesis.ps.gz",
  size =         "147 pages",
  abstract =     "This thesis investigates the design of robust obstacle
                 avoidance strategies. Specifically, simulated
                 coevolution is used to breed steering agents and
                 obstacle courses in a `computational arms race'. Both
                 steering agent strategies and obstacle courses are
                 represented by computer programs, and are coevolved
                 according to the genetic programming paradigm.

                 Previous research has found it difficult to evolve
                 robust vision based obstacle avoidance agents. By
                 independently evolving obstacle avoidance agents
                 against a competing evolving species (ie the obstacle
                 courses), it is hypothesised that the robustness of the
                 agents will be increased.

                 The simon system, an existing genetic programming tool,
                 is modified and used to evolve both the obstacle
                 avoidance agents and the obstacle courses. A comparison
                 is made between the robustness of coevolved obstacle
                 avoidance agents and traditionally evolved
                 (non-coevolved) agents. Robustness is measured by
                 average performance in a series of randomly generated
                 obstacle courses.

                 Experimental results show that the average robustness
                 of the coevolved oa agents is greater than that of the
                 traditionally evolved, and statistically it is shown
                 that this data is representative of all cases.

                 It is therefore concluded that coevolution is
                 applicable to oa type problems, and can be used to
                 evolve more robust, general purpose Vision-Based
                 Obstacle Avoidance agents.",
}

Genetic Programming entries for David G Browne

Citations