Multi-robot formations for area coverage in space applications

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Nakasuka, Shinichi
dc.contributor.advisor Taipalus, Tapio Leitner, Jürgen 2012-03-06T13:34:12Z 2012-03-06T13:34:12Z 2009
dc.description.abstract This thesis presents two algorithmic implementations of multi-robot formation control for the area coverage problem. It uses a space exploration scenario, with a marsupial robot society, for tasks like mapping, habitat construction, etc. The solutions are though applicable to a wider range of applications, since area coverage is seen as one of the canonical problems in multi-robot application. Starting with an overview of multi-robot systems in space applications, both currently in use and planned for the near future, it then presents the two algorithms and their implementation in C++: (i) a vector force based implementation and (ii) a machine learning approach. The second is based on an organizational-learning oriented classifier system (OCS) introduced by Takadama an evolution of Holland's learning classifier system (LCS). To ease the development, testing and evaluation of the control algorithms a simulator, named SMRTCTRL, was implemented during a 3 months research stay at the University of Tokyo. The vector-based control approach was tested using a multi-robot society of LEGO Mindstorms Robots and a comparison of the two algorithm was done with the help of the simulator. en
dc.format.extent 99
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.title Multi-robot formations for area coverage in space applications en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.department Automaatio- ja systeemitekniikan laitos fi
dc.subject.keyword space robotics en
dc.subject.keyword multi-robot cooperation en
dc.subject.keyword area coverage en
dc.subject.keyword machine learning en
dc.subject.keyword simulation en
dc.subject.keyword formation control en
dc.subject.keyword Learning Classifer Systems en
dc.subject.keyword LCS en
dc.identifier.urn URN:NBN:fi:aalto-201203071284
dc.type.dcmitype text en
dc.programme.major Automaatiotekniikka fi
dc.programme.mcode Aut-84
dc.type.ontasot Diplomityö fi
dc.type.ontasot Master's thesis en
dc.contributor.supervisor Halme, Aarne
dc.contributor.supervisor Hyyppä, Kalevi
dc.location P1 fi

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