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dc.contributor.advisorMcLaren, Robert W.eng
dc.contributor.authorHan, Kyung Min, 1976-eng
dc.date.issued2007eng
dc.date.submitted2007 Summereng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file (viewed on September 29, 2008)eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionThesis (M.S.) University of Missouri-Columbia 2007.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.eng
dc.description.abstractPath planning problem, including maze navigation is a challenging topic in robotics. Indeed, a significant amount of research has been devoted to this problem in recent years. Genetic algorithm is a popular approach that searches for an optimal solution in given set of solutions. Considering via points as genes in a chromosome will provide a number of possible solutions on a grid map of paths. In this case, path distances that each chromosome creates can be regarded as a fitness measure for the corresponding chromosome. In some cases, a solution path passes through an obstacle. Assuming that the shape of an obstacle is a circle, such random solutions can easily be eliminated by setting-up simple equation between a line created by two via points and the obstacle. The ant colony optimization algorithm is another approach to solve this problem. Each ant drops a quantity of artificial pheromone on every point that the ant passes through. This pheromone simply changes the probability that the next ant becomes attracted to a particular grid point. Since each ant will make a decision at every grid point that it encounters, it is possible that an ant may wander around the grid map or may become stuck among local grid points. In order to prevent this phenomena the proposed solution adapted a global attraction term which guides ants to head toward the destination point. This thesis addresses methods of the path finding problem using these two different approaches. Both algorithms are tested and compared in the result section. The experiment results demonstrate that these two methods have a great potential to solve the proposed problem.eng
dc.identifier.merlinb64878387eng
dc.identifier.oclc259154661eng
dc.identifier.urihttps://hdl.handle.net/10355/5021
dc.identifier.urihttps://doi.org/10.32469/10355/5021eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2007 Freely available theses (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2007 Theseseng
dc.subject.lcshRoboticseng
dc.subject.lcshRobots, Industrialeng
dc.subject.lcshManipulators (Mechanism)eng
dc.subject.lcshGenetic algorithmseng
dc.subject.lcshStructural optimizationeng
dc.titleCollision free path planning algorithms for robot navigation problemeng
dc.typeThesiseng
thesis.degree.disciplineElectrical engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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