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High-Level Control Of Modular Robots

dc.contributor.authorCastro Fernandez, Sebastianen_US
dc.contributor.chairKress Gazit, Hadasen_US
dc.contributor.coChairCampbell, Marken_US
dc.date.accessioned2012-06-28T20:54:11Z
dc.date.available2012-06-28T20:54:11Z
dc.date.issued2012-01-31en_US
dc.description.abstractReconfigurable modular robots can exhibit different specializations by rearranging the same set of parts comprising them. Actuating modular robots can be complicated because of the many degrees of freedom that scale exponentially with the size of the robot. Effectively controlling these robots directly relates to how well they can be used to complete meaningful tasks. This paper discusses an approach for creating provably correct controllers for modular robots from high-level tasks defined with structured English sentences. While this has been demonstrated with simple mobile robots, the problem was enriched by considering the uniqueness of reconfigurable modular robots. These requirements are expressed through traits in the high-level task specification that store information about the geometry and motion types of a robot. Given a high-level problem definition for a modular robot, the approach in this paper deals with generating all lower levels of control needed to solve it. Information about different robot characteristics is stored in a library, and two tools for populating this library have been developed. The first approach is a physics-based simulator and gait creator for manual generation of motion gaits. The second is a genetic algorithm framework that uses traits to evaluate performance under various metrics. Demonstration is done through simulation and with the CKBot hardware platform.en_US
dc.identifier.otherbibid: 7745390
dc.identifier.urihttps://hdl.handle.net/1813/29174
dc.language.isoen_USen_US
dc.subjectModular Robotsen_US
dc.subjectHigh-Level Controlen_US
dc.subjectGenetic Algorithmsen_US
dc.titleHigh-Level Control Of Modular Robotsen_US
dc.typedissertation or thesisen_US
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorCornell Universityen_US
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Mechanical Engineering

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