Multi-dimensional Path Planning Evolutionary Computation using Evolutionary Computation   [PP] [EC] [EC]

by

Hocaoglu, C. and Sanderson, A., C.

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Info: Proceedings of the 1998 IEEE World Congress on Computational Intelligence (Conference proceedings), 1998, p. 165-170
Keywords:genetic algorithms, genetic programming
Abstract:
This paper describes a flexible and efficient multi-dimensional path planning algorithm based [PP] on evolutionary computation concepts. [EC] A novel iterative multi-resolution path representation is used as a basis for the GA coding. The use of a multi-resolution path representation can reduce the expected search length for the path planning problem. [PP] If a successful path is found early in the search hierarchy (at a low level of resolution), then further expansion of that portion of the path search is not necessary. This advantage is mapped into the encoded search space [SS] and adjusts the string length accordingly. The algorithm is flexible; it handles multi-dimensional path planning problems, [PP] accommodates different optimization criteria and changes in these criteria, and it utilizes domain specific knowledge for making decisions. In the evolutionary path planner, the individual candidates are evaluated with respect to the workspace so that computation of the configuration space is not required. The algorithm can be applied for planning paths for mobile robots, [MR] assembly, pianomovers problems and articulated manipulators. The effectiveness of the algorithm is demonstrated on a number of multi-dimensional path planning problems. [PP]
Notes:
ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
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BibTex:
@InProceedings{hocaoglu:1998:,
  author =       "Cem Hocaoglu and Arthur C. Sanderson",
  title =        "Multi-dimensional Path Planning Evolutionary
                 Computation using Evolutionary Computation",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "165--170",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c029.pdf",
  size =         "6 pages",
  abstract =     "This paper describes a flexible and efficient
                 multi-dimensional path planning algorithm based on
                 evolutionary computation concepts. A novel iterative
                 multi-resolution path representation is used as a basis
                 for the GA coding. The use of a multi-resolution path
                 representation can reduce the expected search length
                 for the path planning problem. If a successful path is
                 found early in the search hierarchy (at a low level of
                 resolution), then further expansion of that portion of
                 the path search is not necessary. This advantage is
                 mapped into the encoded search space and adjusts the
                 string length accordingly. The algorithm is flexible;
                 it handles multi-dimensional path planning problems,
                 accommodates different optimization criteria and
                 changes in these criteria, and it utilizes domain
                 specific knowledge for making decisions. In the
                 evolutionary path planner, the individual candidates
                 are evaluated with respect to the workspace so that
                 computation of the configuration space is not required.
                 The algorithm can be applied for planning paths for
                 mobile robots, assembly, pianomovers problems and
                 articulated manipulators. The effectiveness of the
                 algorithm is demonstrated on a number of
                 multi-dimensional path planning problems.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}