Generalization performance of vision based controllers for mobile robots evolved with genetic programming

Renaud Barate, Antoine Manzanera

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a genetic programming system to design automatically vision based obstacle avoidance algorithms adapted to the current context. We use a simulation environment to evaluate the controllers. By restricting the structure of the algorithms to facilitate the compromise between obstacle avoidance and target reaching, we improve the generalization performance of the algorithms.

Original languageEnglish
Title of host publicationGECCO'08
Subtitle of host publicationProceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
PublisherAssociation for Computing Machinery (ACM)
Pages1331-1332
Number of pages2
ISBN (Print)9781605581309
DOIs
Publication statusPublished - 1 Jan 2008
Externally publishedYes
Event10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 - Atlanta, GA, United States
Duration: 12 Jul 200816 Jul 2008

Publication series

NameGECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Conference

Conference10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008
Country/TerritoryUnited States
CityAtlanta, GA
Period12/07/0816/07/08

Keywords

  • Generalization
  • Obstacle avoidance
  • Robotic simulation
  • Vision

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