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dc.contributor.advisorJohn J. Leonard and Michael R. Benjamin.en_US
dc.contributor.authorWoerner, Kyleen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.date.accessioned2016-09-13T18:06:50Z
dc.date.available2016-09-13T18:06:50Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/104118
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages [294]-[301]).en_US
dc.description.abstractThe field of autonomous collision avoidance has continued to advance in many areas including sensory and perception, navigation, payload integration, and collision avoidance. The advances in collision avoidance, however, have largely focused on iterative changes to the velocity obstacle - an algorithm that inherently loses important collision avoidance information key to replicating a human-like decision space. This thesis examines algorithms that generalize the traditional velocity obstacle into a multi-threshold based approach that more realistically represent and evaluate human ship driving practices. Novel protocol-constrained collision avoidance evaluation algorithms are proposed including the ability to perform both on-line and post-mission analysis of both robots and humans. These algorithms become especially important when considering complex missions of competing objectives in a contact-dense, protocol-constrained collision avoidance environment. Introduction of competing performance metrics consistent with human ship driving practices allows autonomous collision avoidance algorithm designers to consider previously unexplored tradespaces. On-water results of up to five simultaneously interacting autonomous vessels validate the collision avoidance algorithms using four key areas of evaluation: spatial efficiency, temporal efficiency, protocol compliance, and safety. Testing of 10 complex scenarios totaled over 6,150 vehicle-pair on-water encounters. Human-robot field experimentation demonstrated autonomous collision avoidance performance under conflicting protocol requirements of COLREGS while interacting with human-driven vessels. An autonomous collision avoidance "road test" framework is proposed to incorporate testing of arbitrary collision avoidance algorithms both in the field and in simulation.en_US
dc.description.statementofresponsibilityby Kyle Woerner.en_US
dc.format.extent293, [8] pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleMulti-contact protocol-constrained collision avoidance for autonomous marine vehiclesen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.identifier.oclc958136955en_US


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